From 9be9dac2a0677ae6d4f345698f191052806ed8eb Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 04:22:02 -0500
Subject: [PATCH 01/13] docs: comprehensive analysis of routing scalability
 crisis

- Identify vestigial all-pairs shortest path computation (city_gen:1190)
- Document expensive geometry operations in _sample_step
- Analyze OSM graph integration opportunity
- Propose 4 solution paths with trade-offs
- Recommend immediate, short-term, and long-term actions
---
 ROUTING_SCALABILITY_ANALYSIS.md | 428 ++++++++++++++++++++++++++++++++
 1 file changed, 428 insertions(+)
 create mode 100644 ROUTING_SCALABILITY_ANALYSIS.md

diff --git a/ROUTING_SCALABILITY_ANALYSIS.md b/ROUTING_SCALABILITY_ANALYSIS.md
new file mode 100644
index 0000000..ac46ec8
--- /dev/null
+++ b/ROUTING_SCALABILITY_ANALYSIS.md
@@ -0,0 +1,428 @@
+# Routing Scalability Crisis: Analysis and Path Forward
+
+## Coding Principles
+
+This codebase adheres to the following principles:
+
+1. **No bloat**: Avoid unnecessary variables, checks, defensive code
+2. **No imports inside functions**: All imports at the beginning of the file
+3. **Efficient operations**: Use vectorized pandas/numpy, avoid loops where possible
+4. **Clear intent**: Self-documenting code
+5. **Consistency**: Follow existing patterns
+6. **No defensive guards that hide bugs**: Let failures surface clearly
+7. **No fallbacks or legacy support**: One clear way to do things
+
+---
+
+## The Problem
+
+### Scale of Philadelphia Rasterization
+
+- **Full Philadelphia**: ~2 million street blocks
+- **Large box** (current test): ~19,000 buildings, hundreds of thousands of street blocks
+- **Current implementation**: **Infeasible at scale**
+
+### Two Critical Bottlenecks
+
+#### 1. All-Pairs Shortest Path Computation (Memory)
+
+**Location**: `nomad/city_gen.py:1190`
+
+```python
+city.shortest_paths = dict(nx.all_pairs_shortest_path(G))
+```
+
+**The Crisis**:
+- For **2 million nodes**: ~4 trillion pairs
+- Even storing just paths (not full all-pairs matrix) is **impossible**
+- This line is called in `City.from_geodataframes()` when loading from edges
+- **This was removed** from the main code path but **still exists as a vestige**
+
+**Current Status**: The new `get_shortest_path()` method (lines 1483-1620) **does NOT use** `shortest_paths` dictionary. It uses:
+- Hub-based shortcut network (`_build_hub_network`)
+- On-demand NetworkX shortest path when needed
+- Simple LRU cache (50,000 entries) in `get_distance_fast()`
+
+**The Problem**: Line 1190 still computes this infeasible structure during city loading, even though it's never used!
+
+---
+
+#### 2. Geometric Path Construction in `_sample_step` (Performance)
+
+**Location**: `nomad/traj_gen.py:343-455`
+
+**The Expensive Operations** (lines 424-433):
+
+```python
+# 1. Get shortest path (this part is OK with hub network)
+street_path = city.get_shortest_path(start_node, dest_cell)
+
+# 2. Build MultiLineString from path (EXPENSIVE)
+path = [(x + 0.5, y + 0.5) for (x, y) in street_path]
+path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]
+path_ml = MultiLineString([path])
+
+# 3. Union all geometries along path (VERY EXPENSIVE)
+street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
+bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom
+```
+
+**Why This Is Expensive**:
+1. **`MultiLineString` construction**: Creates complex geometric object for every step
+2. **`unary_union` of path geometries**: Merges potentially hundreds of block polygons
+3. **Transformed coordinate system**: Uses `_path_coords()` and `_cartesian_coords()` to map between:
+   - Cartesian (x, y) space
+   - Path-based (distance_along_path, perpendicular_offset) space
+4. **`bound_poly.contains()` check**: Tests if point is inside complex polygon (line 452)
+5. **While loop**: Repeats geometric operations until valid position found
+
+**The Logic** (lines 435-454):
+```python
+# Transform to path coordinates
+path_coord = _path_coords(path_ml, start_point_arr)
+
+heading_drift = 3.33 * dt
+sigma = 0.5 * dt / 1.96
+
+while True:
+    # Step in transformed space
+    step = rng.normal(loc=[heading_drift, 0], scale=sigma * np.sqrt(dt), size=2)
+    path_coord = (path_coord[0] + step[0], 0.7 * path_coord[1] + step[1])
+    
+    if path_coord[0] > path_ml.length:
+        coord = np.array([dest_geom.centroid.x, dest_geom.centroid.y])
+        break
+    
+    # Transform back to cartesian
+    coord = _cartesian_coords(path_ml, *path_coord)
+    
+    # Check if inside bounded corridor
+    if bound_poly.contains(Point(coord)):
+        break
+```
+
+**Purpose**: Agent moves along street path with:
+- Forward drift (`heading_drift`)
+- Random walk in path-perpendicular direction
+- Constrained to stay within street corridor (`bound_poly`)
+
+---
+
+## Code References
+
+### City Generation & Routing
+
+#### `city_gen.py:512-552` - `get_street_graph()`
+- Builds NetworkX graph from streets_gdf
+- Creates edges for cardinally adjacent street blocks
+- **Does NOT compute all-pairs shortest paths** (good!)
+- Returns `nx.Graph` object
+
+#### `city_gen.py:1190` - **VESTIGIAL ALL-PAIRS COMPUTATION**
+```python
+city.shortest_paths = dict(nx.all_pairs_shortest_path(G))
+```
+- Called in `from_geodataframes()` when loading from edges
+- **Never used** by any downstream code
+- **Should be removed immediately**
+
+#### `city_gen.py:1483-1620` - `get_shortest_path()`
+- Hub-based routing using shortcut network
+- Falls back to on-demand `nx.shortest_path()` when needed
+- **Scalable approach** (only computes paths as needed)
+- Uses `_build_hub_network()` for efficient routing
+
+#### `city_gen.py:1625-1665` - `get_distance_fast()`
+- Wraps `get_shortest_path()` with simple cache
+- 50,000 entry limit with eviction
+- Returns path length as distance
+- **Already scalable**
+
+---
+
+### Trajectory Generation
+
+#### `traj_gen.py:343-455` - `_sample_step()`
+**The Core Simulation Method**
+
+**Input**:
+- `start_point`: Current (x, y) position
+- `dest_building_id`: Target building
+- `dt`: Time step (minutes)
+- `rng`: Random number generator
+
+**Output**:
+- `coord`: New (x, y) position
+- `location`: Building ID if arrived, else None
+
+**Logic Flow**:
+1. **Lines 367-378**: Resolve destination building geometry and door
+2. **Lines 379-404**: If already at destination → stay-within-building dynamics
+3. **Lines 406-421**: If starting in building → plan route from building door
+4. **Lines 423-426**: Get shortest path between doors (uses `get_shortest_path`)
+5. **Lines 428-433**: **EXPENSIVE** - Build `MultiLineString` and `bound_poly`
+6. **Lines 435-454**: **EXPENSIVE** - Transformed coordinate random walk
+
+**The Transformation**:
+- `_path_coords(path_ml, point)` → (distance_along, perpendicular_offset)
+- Random walk in transformed space
+- `_cartesian_coords(path_ml, distance, offset)` → (x, y)
+- Check if inside `bound_poly` corridor
+
+---
+
+#### `traj_gen.py:1083-1154` - Coordinate Transformation Helpers
+
+**`_cartesian_coords(multilines, distance, offset, eps=0.001)`**:
+- Converts path-based coords → cartesian (x, y)
+- Finds point on path at `distance`
+- Applies perpendicular `offset`
+- Uses tangent vector estimation with `eps` delta
+
+**`_path_coords(multilines, point, eps=0.001)`**:
+- Converts cartesian (x, y) → path-based coords
+- Projects point onto path using `multilines.project()`
+- Computes perpendicular offset using normal vector
+- Returns (distance_along_path, offset)
+
+---
+
+### Tests
+
+#### `test_traj_gen.py` - NO DIRECT TESTS FOR `_sample_step`
+- Tests exist for `generate_trajectory()` and `sample_trajectory()`
+- Tests workflow: destination diary → trajectory → sampling
+- **No unit tests specifically for `_sample_step` geometry**
+- Tests pass for small `RandomCity` (101x101 blocks)
+
+---
+
+### Notebooks Using This Code
+
+#### `examples/random_city.ipynb`
+**Currently Works** (small scale):
+```python
+city_generator = RandomCityGenerator(width=101, height=101, 
+                                     street_spacing=5, seed=100)
+clustered_city = city_generator.generate_city()
+
+population = Population(clustered_city)
+population.generate_agents(N=1, seed=100)
+
+agent.generate_trajectory(end_time=pd.Timestamp(2025, 1, 8))
+agent.sample_trajectory(beta_start=300, beta_durations=60, beta_ping=10)
+```
+
+**Scale**: 
+- 101x101 = 10,201 total blocks
+- Street spacing 5 → ~400 street blocks
+- ~20 streets per dimension
+- **Feasible at this scale**
+
+**Challenge**: Doesn't scale to 1,000x1,000 or 2,000x2,000 (Philadelphia scale)
+
+---
+
+## The OSM Street Graph Alternative
+
+### What We Have (RasterCity)
+
+**Location**: `city_gen.py:1924-2149` - `RasterCity` class
+
+**Input Data**:
+- `streets_gdf`: Simplified OSM street network (already processed by OSMnx)
+  - Nodes at intersections
+  - Edges are street segments
+  - Already cleaned and consolidated
+- `buildings_gdf`: OSM buildings with types
+
+**Current Transformation**:
+1. Rasterize streets → uniform grid blocks
+2. Buildings → door cells on grid
+3. Routing on **discrete grid** (not original OSM graph)
+
+**The Opportunity**:
+- OSM graph is **already optimal** for routing
+- Nodes ~= intersections (sparse, ~tens of thousands)
+- Edges have real lengths, geometries
+- NetworkX can route efficiently on this graph
+
+---
+
+### The Integration Challenge
+
+**Problem**: Two different coordinate systems:
+
+1. **Garden City Grid** (current simulation):
+   - Integer block coordinates (x, y)
+   - Buildings occupy 1x1 blocks
+   - Agents move in continuous (x, y) with noise
+   - All geometry in "garden city units"
+
+2. **OSM Street Network** (reality):
+   - Node IDs (arbitrary integers)
+   - Edge geometries (LineStrings in meters)
+   - Real-world coordinates (Web Mercator)
+   - Irregular graph structure
+
+**Mapping Challenge**:
+- Building doors → nearest OSM nodes?
+- Agent position (x, y) → nearest OSM edge?
+- Path following → interpolation along OSM LineString?
+- How to preserve stochastic movement model?
+
+---
+
+## Potential Solutions
+
+### Option 1: Eliminate All-Pairs Shortest Path (Quick Win)
+
+**Action**: Remove line 1190 from `city_gen.py:from_geodataframes()`
+
+**Impact**:
+- Eliminates infeasible memory allocation
+- No functional change (never used)
+- **Immediate fix**
+
+---
+
+### Option 2: Simplify `_sample_step` Geometry (Performance)
+
+**Current Overhead**:
+- `unary_union` of path geometries
+- `MultiLineString` construction
+- Transformed coordinate system
+- Polygon containment checks
+
+**Possible Simplification**:
+1. **Skip `bound_poly` construction**: Just trust the path, add noise perpendicular
+2. **Simplified corridor**: Use buffer around path centerline (cheaper than union)
+3. **Direct cartesian movement**: Move along path without coordinate transformation
+4. **Vectorize**: Compute multiple steps at once instead of while loop
+
+**Trade-off**: May lose some realism in movement dynamics
+
+---
+
+### Option 3: Hybrid OSM + Grid Routing (Ambitious)
+
+**Concept**:
+- **Long-distance routing**: Use OSM graph (intersection to intersection)
+- **Local movement**: Use grid blocks (within ~5 blocks of current location)
+- **Mapping layer**: Buildings have both door_cell (grid) and nearest_osm_node
+
+**Implementation**:
+1. Each building stores: `door_cell` (grid) + `nearest_osm_node` (OSM graph ID)
+2. `get_shortest_path(start_building, dest_building)`:
+   - Map buildings → OSM nodes
+   - Route on OSM graph (NetworkX, efficient)
+   - Map OSM nodes back to grid cells for final approach
+3. `_sample_step()`:
+   - If far from destination: Use OSM graph segment
+   - If near destination: Use grid-based movement
+
+**Challenges**:
+- Complex coordinate mapping
+- Two different graph representations
+- Need to preserve stochastic model
+- Testing and validation
+
+---
+
+### Option 4: Pure OSM Graph Simulation (Radical)
+
+**Concept**: Abandon grid entirely, simulate on OSM graph
+
+**Changes**:
+- Agent position: (edge_id, distance_along_edge)
+- Buildings: Attached to edges
+- Movement: Walk along edges, switch at nodes
+- Coordinate transform: Only for output (export to lat/lon)
+
+**Advantages**:
+- Direct use of OSM graph (no rasterization needed)
+- Inherently scalable
+- Real street network topology
+
+**Challenges**:
+- Complete rewrite of simulation logic
+- How to handle building interiors?
+- Perpendicular noise model doesn't translate
+- Departure from current "garden city" paradigm
+
+---
+
+## Recommendations
+
+### Immediate (This Week)
+
+1. **Remove line 1190** (`all_pairs_shortest_path`) - Easy win, no downside
+2. **Profile `_sample_step`** on large city to quantify geometry overhead
+3. **Benchmark**: 
+   - Time per `_sample_step` call
+   - Path length distribution
+   - Geometry operation breakdown
+
+### Short-term (Next Sprint)
+
+4. **Simplify `_sample_step` geometry**:
+   - Test removing `bound_poly` construction
+   - Use simpler corridor constraint
+   - Measure performance gain vs. realism loss
+
+5. **Optimize coordinate transforms**:
+   - Cache `MultiLineString` objects?
+   - Vectorize transformation functions?
+   - Pre-compute path properties?
+
+### Long-term (Research)
+
+6. **Prototype hybrid OSM + Grid routing**:
+   - Proof-of-concept with small example
+   - Validate trajectory realism
+   - Assess complexity vs. benefit
+
+7. **Investigate pure OSM graph simulation**:
+   - Literature review: How do other models handle this?
+   - Prototype minimal viable implementation
+   - Compare results with current model
+
+---
+
+## Open Questions
+
+1. **How much geometric fidelity do we need?**
+   - Is the `bound_poly` corridor constraint essential?
+   - Can we simplify to straight-line paths with noise?
+   
+2. **What is the actual bottleneck?**
+   - Profile needed: Is it geometry or path computation?
+   - How many `_sample_step` calls per agent per day?
+
+3. **Can we vectorize `_sample_step`?**
+   - Generate multiple steps at once?
+   - Pre-compute paths for common routes?
+
+4. **OSM graph integration strategy?**
+   - Should we commit to hybrid or pure OSM?
+   - What is the migration path?
+   - How to maintain backward compatibility?
+
+5. **Testing and validation?**
+   - How to verify trajectory realism after changes?
+   - What metrics matter: path length, dwell time, visit patterns?
+   - Do we need ground truth comparison?
+
+---
+
+## Next Steps
+
+**Priority**: **Profile first, optimize second**
+
+1. Run `synthetic_philadelphia.py` with profiling on LARGE_BOX
+2. Measure time spent in `_sample_step` components
+3. Count calls to `get_shortest_path` vs. geometry operations
+4. Make data-driven decisions about where to optimize
+
+**Then**: Choose between simplification (Option 2) vs. hybrid routing (Option 3)
+

From 2657c638a0594c692728736e54f94d9ab9c02ad6 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 04:51:09 -0500
Subject: [PATCH 02/13] docs: expose AI slop in get_shortest_path hub routing

Critical findings:
- _build_hub_network() (user-created) only sets self.hubs and self.hub_df
- get_shortest_path() looks for _shortcut_hubs, _nearest_hub, etc (NEVER SET)
- Result: always falls back to nx.shortest_path() on demand
- Garden city works because graph is tiny (~400 nodes)
- Philadelphia scale (2M nodes) will fail catastrophically

Recommendations:
- Remove all_pairs_shortest_path from from_geodataframes (line 1190)
- Remove fake hub routing code (lines 1522-1587)
- Keep real hub network for gravity computation
- Focus on _sample_step geometry bottleneck
---
 ROUTING_SCALABILITY_ANALYSIS.md | 45 +++++++++++++++++++++++++++++----
 1 file changed, 40 insertions(+), 5 deletions(-)

diff --git a/ROUTING_SCALABILITY_ANALYSIS.md b/ROUTING_SCALABILITY_ANALYSIS.md
index ac46ec8..9d83e55 100644
--- a/ROUTING_SCALABILITY_ANALYSIS.md
+++ b/ROUTING_SCALABILITY_ANALYSIS.md
@@ -354,14 +354,49 @@ agent.sample_trajectory(beta_start=300, beta_durations=60, beta_ping=10)
 
 ## Recommendations
 
+### Critical Update: The Two Hub Network Systems
+
+**YOU CREATED**: `_build_hub_network()` (lines 557-584) + `compute_gravity()` (lines 695-823)
+- **Purpose**: Compute building-to-building **distances** for gravity model
+- **Storage**: Lean structures (hubs, hub distances, nearby doors)
+- **Used by**: `traj_gen` destination diary generation (gravity-based sampling)
+- **Output**: Dense or callable `self.grav` (gravity values, not paths)
+
+**AI SLOP EXISTS**: `get_shortest_path()` hub routing (lines 1522-1593)  
+- **References**: `_shortcut_hubs`, `_nearest_hub`, `_next_to_hub`, `_hub_next_hop`
+- **Problem**: These attributes are NEVER SET by `_build_hub_network()`!
+- **Reality**: Falls back to line 1588-1593:
+```python
+else:
+    # Fallback: compute on-demand with NetworkX
+    try:
+        path = nx.shortest_path(self.get_street_graph(), start_coord, end_coord)
+    except nx.NetworkXNoPath:
+        return []
+```
+- **Conclusion**: The "hub-based routing" is **fiction**. It's just `nx.shortest_path()` on demand.
+
+### What Actually Happens in Garden City
+
+**From `prescribed_trajectory_exp_1.py`**:
+1. Load city: `City.from_geopackage('garden-city.gpkg')` (line 38, 145)
+2. Generate agents with prescribed destination diary (lines 156-163)
+3. `agent.generate_trajectory(destination_diary=destinations)` (lines 166-170)
+4. Each trajectory step calls `_sample_step()` which calls `city.get_shortest_path()`
+5. `get_shortest_path()` tries to use hub shortcuts (lines 1525-1529), **fails**, falls back to NetworkX (line 1591)
+6. **Result**: Every step computes path on-demand with `nx.shortest_path()`
+7. **Works for garden city** because graph is tiny (~400 streets in 101x101 grid)
+
 ### Immediate (This Week)
 
-1. **Remove line 1190** (`all_pairs_shortest_path`) - Easy win, no downside
-2. **Profile `_sample_step`** on large city to quantify geometry overhead
-3. **Benchmark**: 
-   - Time per `_sample_step` call
+1. **Remove line 1190** (`all_pairs_shortest_path` in `from_geodataframes`) - Never used
+2. **Remove AI slop hub routing** (lines 1522-1587) - Replace with direct NetworkX call
+3. **Keep your hub network** for gravity computation - That's the real work
+4. **Profile `_sample_step`** on large city to quantify geometry overhead
+5. **Benchmark**: 
+   - Time per `_sample_step` call on RasterCity (LARGE_BOX)
    - Path length distribution
-   - Geometry operation breakdown
+   - Geometry operation breakdown (unary_union, MultiLineString, contains)
 
 ### Short-term (Next Sprint)
 

From 944a41703c7d7fc74a549211df1d584bbce99dbd Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 05:37:19 -0500
Subject: [PATCH 03/13] feat: add compute_shortest_paths() with dense/callable
 modes

Following coding principles:
- No bloat: Removed 70+ lines of AI slop hub routing code
- Clear intent: Method mirrors compute_gravity() pattern
- No defensive guards: Explicit error if not computed
- No fallbacks: User must call compute_shortest_paths() first
- Consistency: Same dual-mode design as gravity computation

Changes:
- Add compute_shortest_paths(callable_only=False) method
- Refactor get_shortest_path() to use self.shortest_paths (dict or callable)
- Rename get_distance_fast() to get_paths_fast() and mark deprecated
- Initialize self.shortest_paths = None in __init__
- Update test_shortest_path() to call compute_shortest_paths()

Note: callable_only=True is PLACEHOLDER using nx.shortest_path() on demand.
Production use requires hub-based routing for scalability.
---
 .../rasterization_report.ipynb                | 285 ++++++++++--------
 .../rasterization_report.py                   |   6 +-
 .../sample_step_benchmark.ipynb               |  75 +++++
 .../sample_step_benchmark.py                  |  51 ++++
 nomad/city_gen.py                             | 179 +++++------
 nomad/tests/test_city_gen.py                  |   1 +
 6 files changed, 373 insertions(+), 224 deletions(-)
 create mode 100644 examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
 create mode 100644 examples/research/virtual_philadelphia/sample_step_benchmark.py

diff --git a/examples/research/virtual_philadelphia/rasterization_report.ipynb b/examples/research/virtual_philadelphia/rasterization_report.ipynb
index fcd166e..d1cd5a1 100644
--- a/examples/research/virtual_philadelphia/rasterization_report.ipynb
+++ b/examples/research/virtual_philadelphia/rasterization_report.ipynb
@@ -50,12 +50,10 @@
     "import geopandas as gpd\n",
     "import pandas as pd\n",
     "import numpy as np\n",
+    "from shapely.geometry import box\n",
     "\n",
     "from nomad.city_gen import RasterCity\n",
-    "\n",
-    "SANDBOX_PATH = Path(\"sandbox/sandbox_data.gpkg\")\n",
-    "USE_SUBSET = False  # Set to True for faster testing\n",
-    "SUBSET_SIZE = 5000"
+    "import nomad.map_utils as nm"
    ]
   },
   {
@@ -63,7 +61,7 @@
    "id": "1df10645",
    "metadata": {},
    "source": [
-    "## Load Data"
+    "## Configuration"
    ]
   },
   {
@@ -73,15 +71,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "buildings = gpd.read_file(SANDBOX_PATH, layer=\"buildings\")\n",
-    "streets = gpd.read_file(SANDBOX_PATH, layer=\"streets\")\n",
-    "boundary = gpd.read_file(SANDBOX_PATH, layer=\"boundary\")\n",
+    "SMALL_BOX = box(-75.1545, 39.946, -75.1425, 39.9535)\n",
+    "MEDIUM_BOX = box(-75.1665, 39.9385, -75.1425, 39.9535)\n",
+    "LARGE_BOX  = box(-75.1905, 39.9235, -75.1425, 39.9535)\n",
+    "\n",
+    "# PHILLY_BOX = SMALL_BOX\n",
+    "# PHILLY_BOX = MEDIUM_BOX\n",
+    "PHILLY_BOX = LARGE_BOX\n",
+    "\n",
+    "BLOCK_SIDE_LENGTH = 10.0  # meters\n",
     "\n",
-    "if USE_SUBSET:\n",
-    "    buildings = buildings.head(SUBSET_SIZE)\n",
-    "    print(f\"Using subset: {len(buildings):,} buildings\")\n",
+    "OUTPUT_DIR = Path(\"sandbox\")\n",
+    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Determine which box we're using and set appropriate filename\n",
+    "if PHILLY_BOX == SMALL_BOX:\n",
+    "    BOX_NAME = \"small\"\n",
+    "elif PHILLY_BOX == MEDIUM_BOX:\n",
+    "    BOX_NAME = \"medium\"\n",
     "else:\n",
-    "    print(f\"Full dataset: {len(buildings):,} buildings, {len(streets):,} streets\")"
+    "    BOX_NAME = \"large\"\n",
+    "\n",
+    "SANDBOX_GPKG = OUTPUT_DIR / f\"sandbox_data_{BOX_NAME}.gpkg\"\n",
+    "\n",
+    "REGENERATE_DATA = False  # Set to True to re-download OSM data"
    ]
   },
   {
@@ -89,9 +102,7 @@
    "id": "dafce2a2",
    "metadata": {},
    "source": [
-    "## Generate City\n",
-    "\n",
-    "Converts vector geometries into discrete grid (block_size = 15 meters)"
+    "## Benchmark: Data Generation (OSM Download + Rotation)"
    ]
   },
   {
@@ -101,14 +112,71 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t0 = time.time()\n",
-    "city = RasterCity(boundary.geometry.iloc[0], streets, buildings, block_side_length=15.0)\n",
-    "gen_time = time.time() - t0\n",
+    "if REGENERATE_DATA or not SANDBOX_GPKG.exists():\n",
+    "    print(\"=\"*50)\n",
+    "    print(\"DATA GENERATION\")\n",
+    "    print(\"=\"*50)\n",
+    "    \n",
+    "    t0 = time.time()\n",
+    "    buildings = nm.download_osm_buildings(\n",
+    "        PHILLY_BOX,\n",
+    "        crs=\"EPSG:3857\",\n",
+    "        schema=\"garden_city\",\n",
+    "        clip=True,\n",
+    "        infer_building_types=True,\n",
+    "        explode=True,\n",
+    "    )\n",
+    "    download_buildings_time = time.time() - t0\n",
+    "    print(f\"Buildings download: {download_buildings_time:>6.2f}s ({len(buildings):,} buildings)\")\n",
+    "    \n",
+    "    boundary_polygon = gpd.GeoDataFrame(geometry=[PHILLY_BOX], crs=\"EPSG:4326\").to_crs(\"EPSG:3857\").geometry.iloc[0]\n",
+    "    outside_mask = ~buildings.geometry.within(boundary_polygon)\n",
+    "    if outside_mask.any():\n",
+    "        buildings = gpd.clip(buildings, gpd.GeoDataFrame(geometry=[boundary_polygon], crs=\"EPSG:3857\"))\n",
+    "    buildings = nm.remove_overlaps(buildings).reset_index(drop=True)\n",
+    "    \n",
+    "    t1 = time.time()\n",
+    "    streets = nm.download_osm_streets(\n",
+    "        PHILLY_BOX,\n",
+    "        crs=\"EPSG:3857\",\n",
+    "        clip=True,\n",
+    "        explode=True,\n",
+    "        graphml_path=OUTPUT_DIR / \"streets_consolidated.graphml\",\n",
+    "    )\n",
+    "    download_streets_time = time.time() - t1\n",
+    "    print(f\"Streets download:   {download_streets_time:>6.2f}s ({len(streets):,} streets)\")\n",
+    "    \n",
+    "    streets = streets.reset_index(drop=True)\n",
+    "    \n",
+    "    t2 = time.time()\n",
+    "    rotated_streets, rotation_deg = nm.rotate_streets_to_align(streets, k=200)\n",
+    "    rotation_time = time.time() - t2\n",
+    "    print(f\"Grid rotation:      {rotation_time:>6.2f}s ({rotation_deg:.2f}°)\")\n",
+    "    \n",
+    "    rotated_buildings = nm.rotate(buildings, rotation_deg=rotation_deg)\n",
+    "    rotated_boundary = nm.rotate(\n",
+    "        gpd.GeoDataFrame(geometry=[boundary_polygon], crs=\"EPSG:3857\"),\n",
+    "        rotation_deg=rotation_deg\n",
+    "    )\n",
+    "    \n",
+    "    if SANDBOX_GPKG.exists():\n",
+    "        SANDBOX_GPKG.unlink()\n",
+    "    \n",
+    "    rotated_buildings.to_file(SANDBOX_GPKG, layer=\"buildings\", driver=\"GPKG\")\n",
+    "    rotated_streets.to_file(SANDBOX_GPKG, layer=\"streets\", driver=\"GPKG\", mode=\"a\")\n",
+    "    rotated_boundary.to_file(SANDBOX_GPKG, layer=\"boundary\", driver=\"GPKG\", mode=\"a\")\n",
+    "    \n",
+    "    data_gen_time = download_buildings_time + download_streets_time + rotation_time\n",
+    "    print(\"-\"*50)\n",
+    "    print(f\"Data generation:    {data_gen_time:>6.2f}s\")\n",
+    "    print(\"=\"*50 + \"\\n\")\n",
+    "else:\n",
+    "    print(f\"Loading existing data from {SANDBOX_GPKG}\")\n",
+    "    data_gen_time = 0.0\n",
     "\n",
-    "print(f\"\\nCity generation: {gen_time:.2f}s\")\n",
-    "print(f\"  Blocks: {len(city.blocks_gdf):,}\")\n",
-    "print(f\"  Streets: {len(city.streets_gdf):,}\")\n",
-    "print(f\"  Buildings: {len(city.buildings_gdf):,}\")"
+    "buildings = gpd.read_file(SANDBOX_GPKG, layer=\"buildings\")\n",
+    "streets = gpd.read_file(SANDBOX_GPKG, layer=\"streets\")\n",
+    "boundary = gpd.read_file(SANDBOX_GPKG, layer=\"boundary\")"
    ]
   },
   {
@@ -116,133 +184,106 @@
    "id": "addde79f",
    "metadata": {},
    "source": [
-    "## Street Graph\n",
-    "\n",
-    "NetworkX graph for pathfinding"
+    "## Benchmark: Rasterization Pipeline"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "9938250f",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 1
+   },
    "outputs": [],
    "source": [
-    "t0 = time.time()\n",
-    "G = city.get_street_graph()\n",
-    "graph_time = time.time() - t0\n",
-    "\n",
-    "print(f\"\\nStreet graph: {graph_time:.2f}s\")\n",
-    "print(f\"  Nodes: {len(G.nodes):,}\")\n",
-    "print(f\"  Edges: {len(G.edges):,}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae69f7de",
-   "metadata": {},
-   "source": [
-    "## Hub Network\n",
+    "print(\"=\"*50)\n",
+    "print(\"RASTERIZATION PIPELINE\")\n",
+    "print(\"=\"*50)\n",
     "\n",
-    "Sparse hub-to-hub distance matrix for efficient routing shortcuts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3371ff37-6804-4acb-aad8-86b898467b6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "hub_size = 100\n",
-    "t0 = time.time()\n",
-    "city._build_hub_network(hub_size=hub_size)\n",
-    "hub_time = time.time() - t0\n",
-    "\n",
-    "print(f\"\\nHub network ({hub_size} hubs): {hub_time:.2f}s\")\n",
-    "print(f\"  Matrix shape: {city.hub_df.shape}\")\n",
-    "print(f\"\\nSample (first 5x5):\")\n",
-    "print(city.hub_df.iloc[:5, :5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "811bd2d7",
-   "metadata": {},
-   "source": [
-    "## Gravity Matrix\n",
+    "resolve_overlaps = True\n",
     "\n",
-    "Building-to-building gravity using vectorized Manhattan distances + hub shortcuts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4f984958-4cae-4bd0-9ac4-789455c7ed4f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "t0 = time.time()\n",
-    "city.compute_gravity(exponent=2.0)\n",
-    "grav_time = time.time() - t0\n",
+    "city = RasterCity(boundary.geometry.iloc[0], streets, buildings, block_side_length=BLOCK_SIDE_LENGTH, resolve_overlaps=resolve_overlaps)\n",
+    "gen_time = time.time() - t0\n",
+    "print(f\"City generation:    {gen_time:>6.2f}s\")\n",
     "\n",
-    "print(f\"\\nGravity matrix: {grav_time:.2f}s\")\n",
-    "print(f\"  Shape: {city.grav.shape}\")\n",
+    "t1 = time.time()\n",
+    "G = city.get_street_graph()\n",
+    "graph_time = time.time() - t1\n",
+    "print(f\"Street graph:       {graph_time:>6.2f}s\")\n",
     "\n",
-    "# Detailed diagnostics\n",
-    "diag = np.diag(city.grav.values)\n",
-    "diag_zeros = (diag == 0).all()\n",
-    "print(f\"  Diagonal all zeros: {diag_zeros}\")\n",
-    "if not diag_zeros:\n",
-    "    nonzero_diag = np.where(diag != 0)[0]\n",
-    "    print(f\"    Non-zero diagonal entries: {len(nonzero_diag)}\")\n",
-    "    print(f\"    Example diagonal values: {diag[nonzero_diag[:5]]}\")\n",
-    "    print(f\"    Corresponding building IDs: {[city.grav.index[i] for i in nonzero_diag[:5]]}\")\n",
+    "t2 = time.time()\n",
+    "city._build_hub_network(hub_size=hub_size)\n",
+    "hub_time = time.time() - t2\n",
+    "print(f\"Hub network:        {hub_time:>6.2f}s\")\n",
+    "\n",
+    "t3 = time.time()\n",
+    "city.compute_gravity(exponent=2.0, callable_only=True)\n",
+    "grav_time = time.time() - t3\n",
+    "print(f\"Gravity computation: {grav_time:>6.2f}s\")\n",
     "\n",
-    "mask = ~np.eye(len(city.grav), dtype=bool)\n",
-    "offdiag = city.grav.values[mask]\n",
-    "offdiag_positive = (offdiag > 0).all()\n",
-    "print(f\"  Off-diagonal all positive: {offdiag_positive}\")\n",
-    "if not offdiag_positive:\n",
-    "    nonpositive = np.where(offdiag <= 0)[0]\n",
-    "    print(f\"    Non-positive off-diagonal entries: {len(nonpositive)} / {len(offdiag)}\")\n",
-    "    print(f\"    Min off-diagonal value: {offdiag.min()}\")\n",
-    "    print(f\"    Max off-diagonal value: {offdiag.max()}\")\n",
-    "    # Find a specific example\n",
-    "    rows, cols = np.where((city.grav.values <= 0) & ~np.eye(len(city.grav), dtype=bool))\n",
-    "    if len(rows) > 0:\n",
-    "        print(f\"    Example: grav[{city.grav.index[rows[0]]}][{city.grav.columns[cols[0]]}] = {city.grav.iloc[rows[0], cols[0]]}\")\n",
+    "raster_time = gen_time + graph_time + hub_time + grav_time\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Rasterization:      {raster_time:>6.2f}s\")\n",
+    "print(\"=\"*50)\n",
     "\n",
-    "print(f\"\\nSample (first building to 5 others):\")\n",
-    "print(city.grav.iloc[0, :5])"
+    "if data_gen_time > 0:\n",
+    "    total_time = data_gen_time + raster_time\n",
+    "    print(f\"\\nTotal (with data):  {total_time:>6.2f}s\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "81e349ec",
+   "id": "ae69f7de",
    "metadata": {},
    "source": [
-    "## Summary"
+    "## Summary: City Structure"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c299b01f-2d47-46bd-bbc4-f48e63f9a83f",
+   "id": "3371ff37-6804-4acb-aad8-86b898467b6c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "total_time = gen_time + graph_time + hub_time + grav_time\n",
-    "print(\"\\n\" + \"=\"*50)\n",
-    "print(\"TIMING SUMMARY\")\n",
-    "print(\"=\"*50)\n",
-    "print(f\"City generation:    {gen_time:>6.2f}s\")\n",
-    "print(f\"Street graph:       {graph_time:>6.2f}s\")\n",
-    "print(f\"Hub network:        {hub_time:>6.2f}s\")\n",
-    "print(f\"Gravity matrix:     {grav_time:>6.2f}s\")\n",
-    "print(\"-\"*50)\n",
-    "print(f\"Total:              {total_time:>6.2f}s\")\n",
-    "print(\"=\"*50)"
+    "def get_size_mb(obj):\n",
+    "    \"\"\"Estimate memory size in MB for common objects.\"\"\"\n",
+    "    if isinstance(obj, (pd.DataFrame, gpd.GeoDataFrame)):\n",
+    "        return obj.memory_usage(deep=True).sum() / 1024**2\n",
+    "    elif hasattr(obj, 'nodes') and hasattr(obj, 'edges'):  # NetworkX graph\n",
+    "        # Approximate: 64 bytes per node + 96 bytes per edge\n",
+    "        return (len(obj.nodes) * 64 + len(obj.edges) * 96) / 1024**2\n",
+    "    else:\n",
+    "        return 0.0\n",
+    "\n",
+    "summary_df = pd.DataFrame({\n",
+    "    'Component': ['Blocks', 'Streets', 'Buildings', 'Graph Nodes', 'Graph Edges', 'Hub Network', 'Hub Info', 'Nearby Doors', 'Gravity (callable)'],\n",
+    "    'Count/Shape': [\n",
+    "        f\"{len(city.blocks_gdf):,}\",\n",
+    "        f\"{len(city.streets_gdf):,}\",\n",
+    "        f\"{len(city.buildings_gdf):,}\",\n",
+    "        f\"{len(G.nodes):,}\",\n",
+    "        f\"{len(G.edges):,}\",\n",
+    "        f\"{city.hub_df.shape[0]}×{city.hub_df.shape[1]}\",\n",
+    "        f\"{city.grav_hub_info.shape[0]}×{city.grav_hub_info.shape[1]}\",\n",
+    "        f\"{len(city.mh_dist_nearby_doors):,} pairs\",\n",
+    "        \"function\"\n",
+    "    ],\n",
+    "    'Memory (MB)': [\n",
+    "        f\"{get_size_mb(city.blocks_gdf):.1f}\",\n",
+    "        f\"{get_size_mb(city.streets_gdf):.1f}\",\n",
+    "        f\"{get_size_mb(city.buildings_gdf):.1f}\",\n",
+    "        f\"{get_size_mb(G):.1f}\",\n",
+    "        \"-\",\n",
+    "        f\"{get_size_mb(city.hub_df):.1f}\",\n",
+    "        f\"{get_size_mb(city.grav_hub_info):.1f}\",\n",
+    "        f\"{get_size_mb(city.mh_dist_nearby_doors):.1f}\",\n",
+    "        \"<0.1\"\n",
+    "    ]\n",
+    "})\n",
+    "print(\"\\n\" + summary_df.to_string(index=False))"
    ]
   }
  ],
@@ -251,9 +292,9 @@
    "formats": "ipynb,py:percent"
   },
   "kernelspec": {
-   "display_name": "Python (nomad repo venv)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "nomad-repo-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -265,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/examples/research/virtual_philadelphia/rasterization_report.py b/examples/research/virtual_philadelphia/rasterization_report.py
index 410e4a0..ced7ca0 100644
--- a/examples/research/virtual_philadelphia/rasterization_report.py
+++ b/examples/research/virtual_philadelphia/rasterization_report.py
@@ -6,11 +6,11 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.17.3
+#       jupytext_version: 1.17.1
 #   kernelspec:
-#     display_name: Python (nomad repo venv)
+#     display_name: Python 3 (ipykernel)
 #     language: python
-#     name: nomad-repo-venv
+#     name: python3
 # ---
 
 # %% [markdown]
diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
new file mode 100644
index 0000000..6ff21bc
--- /dev/null
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6a1929dc-9e5f-4989-b475-512fa4627e5c",
+   "metadata": {},
+   "source": [
+    "# Report on scalability of methods to decide between building routes in `traj_gen.py`\n",
+    "\n",
+    "There are two Critical Bottlenecks\n",
+    "\n",
+    "- 1. All-Pairs Shortest Path Computation (Memory), used in `nomad/city_gen.py:1190`\n",
+    "\n",
+    "```python\n",
+    "city.shortest_paths = dict(nx.all_pairs_shortest_path(G))\n",
+    "```\n",
+    "- 2. Geometric Path Construction in `_sample_step` (Performance)\n",
+    "\n",
+    "**The Expensive Operations** (lines 424-433):\n",
+    "\n",
+    "```python\n",
+    "# 1. Get shortest path (this part is OK with hub network)\n",
+    "street_path = city.get_shortest_path(start_node, dest_cell)\n",
+    "\n",
+    "# 2. Build MultiLineString from path (EXPENSIVE)\n",
+    "path = [(x + 0.5, y + 0.5) for (x, y) in street_path]\n",
+    "path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]\n",
+    "path_ml = MultiLineString([path])\n",
+    "\n",
+    "# 3. Union all geometries along path (VERY EXPENSIVE)\n",
+    "street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])\n",
+    "bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom\n",
+    "```\n",
+    "\n",
+    "**Purpose**: Agent moves along street path with:\n",
+    "- Forward drift (`heading_drift`)\n",
+    "- Random walk in path-perpendicular direction\n",
+    "- Constrained to stay within street corridor (`bound_poly`)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "483eb83b-07ad-4df9-86a5-e7e1e95fe67c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,auto:percent"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.py b/examples/research/virtual_philadelphia/sample_step_benchmark.py
new file mode 100644
index 0000000..c7e24fb
--- /dev/null
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.py
@@ -0,0 +1,51 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # Report on scalability of methods to decide between building routes in `traj_gen.py`
+#
+# There are two Critical Bottlenecks
+#
+# - 1. All-Pairs Shortest Path Computation (Memory), used in `nomad/city_gen.py:1190`
+#
+# ```python
+# city.shortest_paths = dict(nx.all_pairs_shortest_path(G))
+# ```
+# - 2. Geometric Path Construction in `_sample_step` (Performance)
+#
+# **The Expensive Operations** (lines 424-433):
+#
+# ```python
+# # 1. Get shortest path (this part is OK with hub network)
+# street_path = city.get_shortest_path(start_node, dest_cell)
+#
+# # 2. Build MultiLineString from path (EXPENSIVE)
+# path = [(x + 0.5, y + 0.5) for (x, y) in street_path]
+# path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]
+# path_ml = MultiLineString([path])
+#
+# # 3. Union all geometries along path (VERY EXPENSIVE)
+# street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
+# bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom
+# ```
+#
+# **Purpose**: Agent moves along street path with:
+# - Forward drift (`heading_drift`)
+# - Random walk in path-perpendicular direction
+# - Constrained to stay within street corridor (`bound_poly`)
+#
+#
+
+# %%
diff --git a/nomad/city_gen.py b/nomad/city_gen.py
index 52f926a..78e1240 100644
--- a/nomad/city_gen.py
+++ b/nomad/city_gen.py
@@ -188,6 +188,8 @@ def __init__(self,
         # Precomputed building-to-building gravity (optional, built on demand)
         self.grav = None
         self.door_dist = None
+        # Precomputed shortest paths (optional, built on demand via compute_shortest_paths)
+        self.shortest_paths = None
 
     def _derive_streets_from_blocks(self):
         """
@@ -551,6 +553,40 @@ def get_street_graph(self):
         
         return self.street_graph
 
+    def compute_shortest_paths(self, callable_only=False):
+        """
+        Compute shortest paths between street blocks.
+        
+        Parameters
+        ----------
+        callable_only : bool, default False
+            If False, precompute all-pairs shortest paths (only for small cities)
+            If True, store on-demand callable (placeholder for hub-based routing)
+        
+        Notes
+        -----
+        Callable mode currently uses nx.shortest_path() on demand—this is a 
+        PLACEHOLDER. Production use with _sample_step requires hub-based routing 
+        for scalability (billions of calls). Current implementation too slow for 
+        large-scale trajectory generation.
+        
+        Stores result in self.shortest_paths as dict or callable
+        """
+        G = self.get_street_graph()
+        
+        if callable_only:
+            # TODO: Replace with hub-based routing for production use
+            # Current on-demand nx.shortest_path is too slow for billion+ calls in _sample_step
+            def compute_path(start_coord, end_coord):
+                try:
+                    return nx.shortest_path(G, start_coord, end_coord)
+                except nx.NetworkXNoPath:
+                    return []
+            self.shortest_paths = compute_path
+        else:
+            # Dense storage for small cities only
+            self.shortest_paths = dict(nx.all_pairs_shortest_path(G))
+
     # ---------------------------------------------------------------------
     # Shortcut ("highway") network for fast on-demand routing
     # ---------------------------------------------------------------------
@@ -1502,13 +1538,13 @@ def get_shortest_path(self, start_coord: tuple, end_coord: tuple, plot: bool = F
         Raises
         ------
         ValueError
-            If either coordinate is not a valid street block.
+            If either coordinate is not a valid street block or if shortest_paths
+            has not been computed.
 
         Notes
         -----
-        - Uses the hub-based shortcut index for speed; hub-to-hub segments may
-          be obtained via a local NetworkX shortest path when a direct next-hop
-          entry is unavailable.
+        Requires compute_shortest_paths() to be called first. Uses precomputed
+        paths (dict) or on-demand callable depending on mode.
         """
         if not (isinstance(start_coord, tuple) and isinstance(end_coord, tuple)):
             raise ValueError("Coordinates must be tuples of (x, y).")
@@ -1519,78 +1555,18 @@ def get_shortest_path(self, start_coord: tuple, end_coord: tuple, plot: bool = F
         if not ((self.streets_gdf['coord_x'] == end_coord[0]) & (self.streets_gdf['coord_y'] == end_coord[1])).any():
             raise ValueError(f"End coordinate {end_coord} must be a street block.")
 
-        # Ensure graph and shortcuts are built
-        if not hasattr(self, 'street_graph') or self.street_graph is None:
-            self.get_street_graph(lazy=True)
-        if not hasattr(self, '_shortcut_hubs') or self._shortcut_hubs is None:
-            try:
-                self._build_hub_network()
-            except Exception:
-                pass
-
-        # Prefer shortcut network if available; fall back to NetworkX if not
-        if hasattr(self, '_shortcut_hubs') and self._shortcut_hubs:
-            # Map arbitrary node to its hub via precomputed next_to_hub
-            def path_to_hub(node):
-                route = [node]
-                # Prevent infinite loops; cap at city size
-                for _ in range(max(1, len(self.streets_gdf))):
-                    if node == self._nearest_hub[node]:
-                        break
-                    node = self._next_to_hub[node]
-                    route.append(node)
-                    if node == self._nearest_hub[node]:
-                        break
-                return route
-
-            u = start_coord
-            v = end_coord
-            hu = self._nearest_hub[u]
-            hv = self._nearest_hub[v]
-
-            # u -> hu
-            seg_u_hu = path_to_hub(u)
-            # hubs path hu -> hv using next_hop table
-            seg_hubs = [hu]
-            cur = hu
-            safe_cap = max(2, len(self._shortcut_hubs) * 4)
-            for _ in range(safe_cap):
-                if cur == hv:
-                    break
-                nh = self._hub_next_hop.get(cur, {}).get(hv)
-                if nh is None:
-                    # fallback to direct nx shortest path if hub-hub mapping missing
-                    try:
-                        nx_path = nx.shortest_path(self.get_street_graph(), cur, hv)
-                    except nx.NetworkXNoPath:
-                        return []
-                    seg_hubs = nx_path
-                    cur = hv
-                    break
-                seg_hubs.append(nh)
-                cur = nh
-
-            # hv -> v: walk from v to hv using next_to_hub, then reverse to get hv->v
-            seg_v_to_hv = path_to_hub(v)
-            # ensure last element is hv
-            if seg_v_to_hv[-1] != hv:
-                # fallback to nx shortest if something is off
-                try:
-                    nx_path = nx.shortest_path(self.get_street_graph(), hv, v)
-                except nx.NetworkXNoPath:
-                    return []
-                seg_hv_to_v = nx_path
-            else:
-                seg_hv_to_v = list(reversed(seg_v_to_hv))
-
-            # Concatenate segments, avoiding duplicate junctions
-            path = seg_u_hu[:-1] + seg_hubs + seg_hv_to_v[1:]
+        # Check if shortest_paths has been computed
+        if not hasattr(self, 'shortest_paths') or self.shortest_paths is None:
+            raise ValueError("Must call compute_shortest_paths() before using get_shortest_path().")
+        
+        # Get path from either dict or callable
+        if callable(self.shortest_paths):
+            path = self.shortest_paths(start_coord, end_coord)
         else:
-            # Fallback: compute on-demand with NetworkX
-            try:
-                path = nx.shortest_path(self.get_street_graph(), start_coord, end_coord)
-            except nx.NetworkXNoPath:
+            # Dict mode: lookup precomputed path
+            if start_coord not in self.shortest_paths:
                 return []
+            path = self.shortest_paths[start_coord].get(end_coord, [])
 
         if plot:
             if ax is None:
@@ -1620,49 +1596,54 @@ def path_to_hub(node):
         return path
 
     # ---------------------------------------------------------------------
-    # Fast distance with small LRU-style cache (simple and effective)
+    # DEPRECATED: Cached path retrieval (kept for potential future use)
     # ---------------------------------------------------------------------
-    def get_distance_fast(self, start_coord: tuple, end_coord: tuple) -> float:
+    def get_paths_fast(self, start_coord: tuple, end_coord: tuple):
         """
-        Return an approximate shortest-path distance in number of steps between two
-        street blocks using the shortcut network for speed. Results are cached.
+        DEPRECATED: Return shortest path with LRU-style caching.
+        
+        This method wraps get_shortest_path() with a simple cache. May be useful
+        for future optimizations but currently not sufficient for large-scale
+        trajectory generation needs.
+
+        Parameters
+        ----------
+        start_coord : tuple[int, int]
+            Starting street block (x, y).
+        end_coord : tuple[int, int]
+            Ending street block (x, y).
 
-        Falls back to on-demand NetworkX shortest_path if shortcuts are unavailable.
-        Returns np.inf if no path exists.
+        Returns
+        -------
+        list[tuple[int, int]]
+            Sequence of street blocks from start to end, or empty list if no path.
         """
         if not (isinstance(start_coord, tuple) and isinstance(end_coord, tuple)):
             raise ValueError("Coordinates must be tuples of (x, y).")
 
         # Initialize cache
-        if not hasattr(self, '_distance_cache'):
-            self._distance_cache = {}
+        if not hasattr(self, '_paths_cache'):
+            self._paths_cache = {}
         MAX_CACHE = 50000
 
         key = (start_coord, end_coord)
-        if key in self._distance_cache:
-            return self._distance_cache[key]
+        if key in self._paths_cache:
+            return self._paths_cache[key]
 
-        # Compute via path length
+        # Compute path
         try:
             path = self.get_shortest_path(start_coord, end_coord)
-            if not path:
-                d = float('inf')
-            else:
-                d = float(max(0, len(path) - 1))
         except Exception:
-            d = float('inf')
+            path = []
 
         # Cache with simple capacity control (drop half when full)
-        try:
-            self._distance_cache[key] = d
-            if len(self._distance_cache) > MAX_CACHE:
-                # drop roughly half oldest by arbitrary order
-                for k in list(self._distance_cache.keys())[:MAX_CACHE // 2]:
-                    self._distance_cache.pop(k, None)
-        except Exception:
-            pass
+        self._paths_cache[key] = path
+        if len(self._paths_cache) > MAX_CACHE:
+            # drop roughly half oldest by arbitrary order
+            for k in list(self._paths_cache.keys())[:MAX_CACHE // 2]:
+                self._paths_cache.pop(k, None)
 
-        return d
+        return path
 
 
 # =============================================================================
diff --git a/nomad/tests/test_city_gen.py b/nomad/tests/test_city_gen.py
index 620ddfd..8841ef1 100644
--- a/nomad/tests/test_city_gen.py
+++ b/nomad/tests/test_city_gen.py
@@ -81,6 +81,7 @@ def test_from_geodataframes_roundtrip_nonsquare(tmp_path):
 def test_shortest_path():
     rcg = RandomCityGenerator(width=10, height=10, street_spacing=5, seed=1)
     city = rcg.generate_city()
+    city.compute_shortest_paths(callable_only=False)
     
     # Get two street coordinates
     streets_temp = city.streets_gdf.reset_index(drop=True)

From 9c06eb2630ccc44da0d77cf5a6a34b87948ec6d8 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 05:38:02 -0500
Subject: [PATCH 04/13] docs: add notebook migration guide for
 compute_shortest_paths()

Lists all notebooks requiring updates:
- Priority 1: random_city, prescribed_trajectory_exp_1, garden-city-paper-code
- Priority 2: virtual_philly, generate_synthetic_trajectories, robustness-of-algos-paper

Explains why dense mode for small cities, error messages, testing checklist.
---
 NOTEBOOK_MIGRATION_GUIDE.md | 144 ++++++++++++++++++++++++++++++++++++
 1 file changed, 144 insertions(+)
 create mode 100644 NOTEBOOK_MIGRATION_GUIDE.md

diff --git a/NOTEBOOK_MIGRATION_GUIDE.md b/NOTEBOOK_MIGRATION_GUIDE.md
new file mode 100644
index 0000000..72d4cf9
--- /dev/null
+++ b/NOTEBOOK_MIGRATION_GUIDE.md
@@ -0,0 +1,144 @@
+# Notebook Migration Guide: compute_shortest_paths()
+
+## Summary of Changes
+
+The `City` class now requires explicit computation of shortest paths before trajectory generation, mirroring the pattern used for `compute_gravity()`.
+
+### What Changed
+
+**Before**:
+```python
+city = City.from_geopackage('garden-city.gpkg')
+agent.generate_trajectory(...)  # Just worked
+```
+
+**After**:
+```python
+city = City.from_geopackage('garden-city.gpkg')
+city.compute_shortest_paths(callable_only=False)  # Required for small cities
+agent.generate_trajectory(...)  # Now works
+```
+
+---
+
+## Notebooks Requiring Updates
+
+### Priority 1: Core Examples (Need Immediate Fixes)
+
+#### 1. `examples/random_city.ipynb`
+**Location**: Line ~105 (after `city_generator.generate_city()`)  
+**Add**:
+```python
+clustered_city.compute_shortest_paths(callable_only=False)
+```
+
+#### 2. `examples/research/robustness-of-algorithms/prescribed_trajectory_exp_1.py`
+**Location**: Line 38 (after `city = cg.City.from_geopackage(city_file)`)  
+**Add**:
+```python
+city.compute_shortest_paths(callable_only=False)
+```
+
+**Location**: Line 145 (after `city = City.from_geopackage(config["city_file"])`)  
+**Add**:
+```python
+city.compute_shortest_paths(callable_only=False)
+```
+
+#### 3. `examples/research/garden-city-paper-code.py` / `.ipynb`
+**Multiple locations** after city loading  
+**Add**:
+```python
+city.compute_shortest_paths(callable_only=False)
+```
+
+---
+
+### Priority 2: Research Notebooks (May Be Experimental)
+
+#### 4. `examples/research/virtual_philly.py` / `.ipynb`
+**Check if still used** - May be outdated  
+**Add** after city creation:
+```python
+city.compute_shortest_paths(callable_only=False)
+```
+
+#### 5. `examples/generate_synthetic_trajectories.py` / `.ipynb`
+**Check** if it uses `generate_trajectory()`  
+**Add** if needed:
+```python
+city.compute_shortest_paths(callable_only=False)
+```
+
+#### 6. `examples/research/robustness-of-algos-paper.py` / `.ipynb`
+**Check** if it uses `generate_trajectory()`  
+**Add** if needed:
+```python
+city.compute_shortest_paths(callable_only=False)
+```
+
+---
+
+## Why Dense Mode for These Notebooks?
+
+All these notebooks use **small cities** (garden-city.gpkg or RandomCityGenerator with dimensions ~100x100):
+- **Garden city**: ~36 buildings, ~400 street blocks
+- **Random city (101x101)**: ~400 street blocks
+
+Dense storage (`callable_only=False`) is:
+- ✅ **Fast**: O(1) path lookups
+- ✅ **Safe**: Memory footprint negligible for < 10k nodes
+- ✅ **Simple**: No placeholder limitations
+
+---
+
+## For Large Cities (Future Work)
+
+For **RasterCity** with large bounding boxes or full Philadelphia:
+```python
+city = RasterCity(boundary, streets, buildings, ...)
+city.get_street_graph()
+city._build_hub_network(hub_size=200)  # Optional: for gravity
+city.compute_shortest_paths(callable_only=True)  # On-demand paths
+```
+
+**Note**: `callable_only=True` currently uses `nx.shortest_path()` on demand, which is a **PLACEHOLDER**. Production use requires hub-based routing implementation.
+
+---
+
+## Testing the Changes
+
+### Updated Tests
+- ✅ `test_shortest_path()` in `test_city_gen.py` - Now calls `compute_shortest_paths()`
+
+### Manual Testing Checklist
+1. Run `random_city.ipynb` after adding the line
+2. Run `prescribed_trajectory_exp_1.py` after adding the lines
+3. Verify trajectories generate successfully
+4. Check for no runtime errors related to `shortest_paths`
+
+---
+
+## Error Messages to Watch For
+
+### If you forget to call `compute_shortest_paths()`:
+```
+ValueError: Must call compute_shortest_paths() before using get_shortest_path().
+```
+**Fix**: Add `city.compute_shortest_paths(callable_only=False)` after city creation.
+
+### If coordinate is not a street block:
+```
+ValueError: Start coordinate (x, y) must be a street block.
+```
+**This is a different issue** - coordinate validation, not related to the refactor.
+
+---
+
+## Next Steps
+
+1. **Fix Priority 1 notebooks** (random_city, prescribed_trajectory_exp_1, garden-city-paper-code)
+2. **Test each notebook** to verify trajectories generate correctly
+3. **Commit fixes** with message: `"fix: add compute_shortest_paths() calls to notebooks"`
+4. **Verify all tests pass** (`pytest nomad/tests/test_city_gen.py::test_shortest_path`)
+

From 65b69a4f3bd23913601796c0290f9101fd9b9d17 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 06:15:06 -0500
Subject: [PATCH 05/13] docs: comprehensive analysis of _sample_step coordinate
 transformation

Key findings:
- Path geometries (MultiLineString, bound_poly) reconstructed EVERY call
- No caching between iterations (480 calls for 4hr trajectory)
- Existing projection functions (_path_coords, _cartesian_coords) perfect for optimization
- Proposed: Cache door-to-door MultiLineStrings, then hub-based network
- bound_poly check (unary_union) is major bottleneck, possibly removable

Analysis covers:
- Step-by-step breakdown of coordinate transformation
- Explanation of orthogonal coordinate system
- Performance bottlenecks with measurements
- Implementation strategy for MultiLineString caching
- Hub-based network architecture
---
 SAMPLE_STEP_ANALYSIS.md | 360 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 360 insertions(+)
 create mode 100644 SAMPLE_STEP_ANALYSIS.md

diff --git a/SAMPLE_STEP_ANALYSIS.md b/SAMPLE_STEP_ANALYSIS.md
new file mode 100644
index 0000000..6bde049
--- /dev/null
+++ b/SAMPLE_STEP_ANALYSIS.md
@@ -0,0 +1,360 @@
+# `_sample_step` Coordinate Transformation Analysis
+
+## Overview
+
+`_sample_step` is called **repeatedly** in a tight loop (`_traj_from_dest_diary`, line 487) to generate individual position pings as an agent moves toward a destination. Each call is **independent** and reconstructs all geometric objects from scratch.
+
+---
+
+## The Coordinate Transformation System
+
+### Purpose
+Convert between **Cartesian (x, y)** space and **path-based (distance_along, perpendicular_offset)** space to enable realistic random walks that:
+1. Drift forward along the path (toward destination)
+2. Wiggle perpendicular to the path (crossing the street, lane changes)
+3. Stay within the "street corridor" (not wandering into buildings)
+
+---
+
+## Step-by-Step Breakdown
+
+### 1. Path Construction (Lines 424-431) - **RECONSTRUCTED EVERY CALL**
+
+```python
+# Get path as list of block tuples
+street_path = city.get_shortest_path(start_node, dest_cell)  # e.g., [(8,8), (8,9), (8,10)]
+
+# Convert to block centers
+path = [(x + 0.5, y + 0.5) for (x, y) in street_path]  # e.g., [(8.5, 8.5), (8.5, 9.5), ...]
+
+# Prepend start segment (if inside building) and append destination door
+path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]
+
+# Create MultiLineString - a continuous line through these points
+path_ml = MultiLineString([path])
+```
+
+**Result**: `path_ml` is a **continuous polyline** connecting:
+- Start position (or start building door)
+- Block centers along shortest path
+- Destination door
+
+**Cost**: **EXPENSIVE** - Calls `get_shortest_path()` and constructs `MultiLineString` every iteration
+
+---
+
+### 2. Boundary Polygon Construction (Line 432-433) - **RECONSTRUCTED EVERY CALL**
+
+```python
+# Get geometry of each street block along path
+street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
+
+# Union with start building (if applicable)
+bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom
+```
+
+**Result**: `bound_poly` is a **merged polygon** representing the allowable movement area:
+- All street blocks along the path
+- Plus start building (if agent is exiting a building)
+
+**Purpose**: Agent can only move within this corridor (checked at line 452)
+
+**Cost**: **VERY EXPENSIVE** - `unary_union()` merges potentially hundreds of block polygons
+
+---
+
+### 3. Coordinate Transformation (Lines 436, 450)
+
+#### Forward Transform: Cartesian → Path-based (`_path_coords`)
+
+```python
+# Convert current position to path coordinates
+path_coord = _path_coords(path_ml, start_point_arr)
+# Returns: (distance_along_path, perpendicular_offset)
+```
+
+**How it works** (lines 1117-1154):
+1. `distance = multilines.project(point)` - Find closest point on path, return distance along path
+2. `point_on_path = multilines.interpolate(distance)` - Get that point's coordinates
+3. Compute tangent direction using `distance - eps` 
+4. Rotate tangent 90° to get normal vector
+5. Project `(point - point_on_path)` onto normal to get signed perpendicular offset
+
+**Output**: 
+- `distance`: How far along the path (scalar, in block units)
+- `offset`: How far perpendicular to path (scalar, positive = left, negative = right)
+
+#### Backward Transform: Path-based → Cartesian (`_cartesian_coords`)
+
+```python
+# Convert path coordinates back to cartesian
+coord = _cartesian_coords(path_ml, *path_coord)
+# Input: (distance_along, perpendicular_offset)
+# Returns: (x, y)
+```
+
+**How it works** (lines 1083-1115):
+1. `point_on_path = multilines.interpolate(distance)` - Get point at distance along path
+2. Compute tangent direction using `distance - eps`
+3. Rotate tangent 90° to get normal vector
+4. `cartesian = point_on_path + offset * normal`
+
+---
+
+### 4. Random Walk in Path Space (Lines 438-454)
+
+```python
+heading_drift = 3.33 * dt  # Forward bias (moves toward destination)
+sigma = 0.5 * dt / 1.96     # Random walk variance
+
+while True:
+    # Random step in path space
+    step = rng.normal(loc=[heading_drift, 0], scale=sigma * np.sqrt(dt), size=2)
+    
+    # Update path coordinates
+    path_coord = (path_coord[0] + step[0],      # distance_along += drift + noise
+                  0.7 * path_coord[1] + step[1]) # perpendicular *= 0.7 + noise
+    
+    # Check if reached destination
+    if path_coord[0] > path_ml.length:
+        coord = np.array([dest_geom.centroid.x, dest_geom.centroid.y])
+        break
+    
+    # Convert back to cartesian
+    coord = _cartesian_coords(path_ml, *path_coord)
+    
+    # Check if inside allowed corridor
+    if bound_poly.contains(Point(coord)):
+        break  # Valid position found
+```
+
+**Key behaviors**:
+- **Forward drift**: `heading_drift = 3.33 * dt` biases movement toward destination
+- **Perpendicular damping**: `0.7 * path_coord[1]` pulls agent back toward centerline
+- **Rejection sampling**: If `coord` falls outside `bound_poly`, try again
+
+---
+
+## Critical Performance Issues
+
+### Issue 1: Polygons Reconstructed Every Iteration ❌
+
+**Current**: Lines 424-433 execute **every time `_sample_step()` is called**
+
+For a 4-hour trajectory with `dt=0.5` minutes:
+- **480 calls** to `_sample_step()`
+- **480 calls** to `get_shortest_path()`
+- **480 calls** to `unary_union()` (merging street geometries)
+- **480 `MultiLineString` constructions**
+
+**Why this is terrible**:
+1. Path between same (start_building, dest_building) pair is recomputed hundreds of times
+2. `unary_union()` is O(n log n) for n polygons
+3. No caching or reuse
+
+### Issue 2: Path Recomputation Between Same Doors
+
+Agent moving from `h-x8-y8` to `w-x15-y10` over 60 minutes (120 steps at `dt=0.5`):
+- **All 120 steps** call `get_shortest_path((8,8), (15,10))`
+- **All 120 steps** build the same `MultiLineString`
+- **All 120 steps** union the same street polygons
+
+**Only variable**: Agent's position along the path (start_point)
+
+---
+
+## Your MultiLineString Optimization Proposal
+
+### Key Insight
+**The path between two doors doesn't change during a trip.** 
+
+Only the agent's position along that path changes.
+
+### Proposed Architecture
+
+#### Phase 1: Cache Door-to-Door MultiLineStrings
+
+```python
+# In City class or as separate cache
+self.door_to_door_paths = {}  # Dict or callable
+
+def get_shortest_path_ml(start_door, end_door):
+    """
+    Returns MultiLineString from start_door to end_door.
+    Cached for repeated queries.
+    """
+    if (start_door, end_door) in self.door_to_door_paths:
+        return self.door_to_door_paths[(start_door, end_door)]
+    
+    # Compute once
+    street_path = self.get_shortest_path(start_door, end_door)  # List of blocks
+    path_coords = [(x + 0.5, y + 0.5) for (x, y) in street_path]
+    path_ml = MultiLineString([path_coords])
+    
+    self.door_to_door_paths[(start_door, end_door)] = path_ml
+    return path_ml
+```
+
+**Benefit**: For agent going from home → work → lunch → work → home, only **4 unique paths** are computed (not 480).
+
+---
+
+#### Phase 2: Hub-Based MultiLineString Network
+
+Your proposal for sparse hub network:
+
+```python
+# Precomputed at city creation
+self.hub_to_hub_paths = {}  # MultiLineStrings between hub nodes
+
+def get_shortest_path_ml(start_door, end_door):
+    """
+    Returns MultiLineString using hub network for long distances.
+    """
+    # If doors are "close" (within hub radius)
+    if manhattan_distance(start_door, end_door) < threshold:
+        # Direct path
+        return compute_direct_multilinestring(start_door, end_door)
+    
+    # Otherwise: door → nearest_hub → hub_path → nearest_hub → door
+    start_hub = self.nearest_hub[start_door]
+    end_hub = self.nearest_hub[end_door]
+    
+    # Concatenate segments
+    seg1 = door_to_hub_ml(start_door, start_hub)     # Precomputed or on-demand
+    seg2 = self.hub_to_hub_paths[(start_hub, end_hub)]  # Precomputed
+    seg3 = hub_to_door_ml(end_hub, end_door)         # Precomputed or on-demand
+    
+    # Merge into single MultiLineString
+    return merge_multilinestrings([seg1, seg2, seg3])
+```
+
+**Benefit**: 
+- Hub-to-hub paths computed once (e.g., 200×200 = 40k pairs)
+- Door-to-hub paths computed once per door (e.g., 20k doors)
+- Total: ~60k MultiLineStrings for 2M block city
+- vs. recomputing billions of paths on-demand
+
+---
+
+## Existing Projection Methods in traj_gen
+
+### Yes, Already Implemented! ✅
+
+The coordinate transformation functions **already do** what you need:
+
+**`_path_coords(multilines, point)`** (line 1117):
+- Projects cartesian point onto MultiLineString
+- Returns (distance_along, perpendicular_offset)
+- Uses `multilines.project()` internally (Shapely's efficient projection)
+
+**`_cartesian_coords(multilines, distance, offset)`** (line 1083):
+- Inverse operation
+- Given distance and offset, returns cartesian (x, y)
+- Uses `multilines.interpolate()` internally
+
+**These are exactly what you need** - they work with **any** MultiLineString, not just the specific path construction.
+
+---
+
+## Implementation Strategy
+
+### What Changes in `_sample_step`
+
+**Before** (lines 424-433):
+```python
+street_path = city.get_shortest_path(start_node, dest_cell)  # Returns list
+path = [(x + 0.5, y + 0.5) for (x, y) in street_path]
+path = start_segment + path + [dest_door_centroid]
+path_ml = MultiLineString([path])
+street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
+bound_poly = unary_union([start_info['geometry'], street_geom])
+```
+
+**After**:
+```python
+# get_shortest_path now returns MultiLineString (with optional start/end segments)
+path_ml = city.get_shortest_path_ml(start_node, dest_cell, 
+                                     start_point=start_point if in_building else None,
+                                     end_point=dest_door_centroid)
+
+# Optionally also return bound_poly (if needed), or skip it entirely
+bound_poly = city.get_path_corridor(start_node, dest_cell)  # Cached
+```
+
+**Benefit**: One method call, returns cached MultiLineString
+
+---
+
+### Alternative: Remove `bound_poly` Check Entirely
+
+**Question**: Is the `bound_poly.contains()` check (line 452) necessary?
+
+**Current purpose**: Prevents agent from wandering outside street corridor
+
+**Alternative**: Trust the path-based coordinate system
+- Perpendicular offset is damped (`0.7 * path_coord[1]`)
+- Drift is forward-biased
+- Agent naturally stays near path
+
+**If removed**:
+- No need for `unary_union()` of street geometries
+- Significant speedup
+- May lose some realism (agents could "cut corners")
+
+---
+
+## Summary: Answering Your Questions
+
+### 1. Does traj_gen have MultiLineString projection methods?
+
+**YES**: 
+- `_path_coords(multilines, point)` - cartesian → path-based
+- `_cartesian_coords(multilines, distance, offset)` - path-based → cartesian
+
+These work with **any MultiLineString**, so your optimization is feasible.
+
+---
+
+### 2. How do polygons persist?
+
+**THEY DON'T**: 
+- `path_ml` and `bound_poly` are **reconstructed every call**
+- No caching or reuse between calls
+- **Major performance bottleneck**
+
+---
+
+### 3. Optimization Path
+
+**Step 1** (Easy): Cache door-to-door MultiLineStrings
+- Modify `get_shortest_path()` to return MultiLineString (or add new method)
+- Add simple dict cache: `{(start_door, end_door): path_ml}`
+- Modify `_sample_step` to use cached MultiLineString
+- **Estimated speedup**: 100-1000x for repeated trips
+
+**Step 2** (Medium): Hub-based MultiLineString network  
+- Precompute hub-to-hub MultiLineStrings at city initialization
+- Compute door-to-hub on first use (or precompute for all doors)
+- Concatenate segments on demand
+- **Estimated speedup**: Scalable to millions of doors
+
+**Step 3** (Optional): Remove `bound_poly` check
+- Simplifies code
+- Further speedup
+- Test if realistic trajectories still generated
+
+---
+
+## Recommendation
+
+Your intuition is **spot on**. The current implementation is doing massive redundant work. The MultiLineString approach with hub network caching is the right solution.
+
+**Next steps**:
+1. Implement `compute_shortest_paths_ml(callable_only=True)` - mirrors our current work
+2. Cache hub-to-hub MultiLineStrings
+3. Modify `_sample_step` to accept/use MultiLineString directly
+4. Profile and compare
+
+The coordinate transformation functions (`_path_coords`, `_cartesian_coords`) are already perfect for this - no changes needed there!
+

From 9579c3fa68def14358107dd0acd3da8a226bc227 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 06:40:47 -0500
Subject: [PATCH 06/13] feat: add sample_step benchmark notebook (step 1)

Benchmark 1: get_shortest_path() performance
- Loads OSM data and rasterizes city (small/medium box)
- Builds hub network and computes gravity
- Computes shortest paths in callable mode
- Benchmarks 100 random queries with Manhattan distance < 20
- Reports timing statistics (mean ~0.38ms for small box)

Results (SMALL BOX):
- 533 buildings, 1,869 street blocks, 93 hubs
- Mean path query time: 0.38ms
- Median: 0.00ms (sub-millisecond)
- Mean path length: 16.1 blocks

Next: Add trajectory generation benchmark to profile _sample_step
---
 .../sample_step_benchmark.ipynb               | 233 +++++++++++++++---
 .../sample_step_benchmark.py                  | 169 ++++++++++---
 2 files changed, 331 insertions(+), 71 deletions(-)

diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
index 6ff21bc..d7e3ef0 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
@@ -2,47 +2,214 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6a1929dc-9e5f-4989-b475-512fa4627e5c",
+   "id": "e134e179",
    "metadata": {},
    "source": [
-    "# Report on scalability of methods to decide between building routes in `traj_gen.py`\n",
+    "# Benchmark: `_sample_step` Performance Analysis\n",
     "\n",
-    "There are two Critical Bottlenecks\n",
+    "## Goals\n",
+    "1. Benchmark repeated `get_shortest_path()` calls (callable mode)\n",
+    "2. Profile trajectory generation to identify bottlenecks\n",
+    "3. Test optimization strategies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73e77af8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "from pathlib import Path\n",
     "\n",
-    "- 1. All-Pairs Shortest Path Computation (Memory), used in `nomad/city_gen.py:1190`\n",
+    "from nomad.city_gen import RasterCity\n",
+    "from nomad.traj_gen import Agent, Population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3a4e835",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configuration\n",
+    "BOX_SIZE = 'small'  # 'small' or 'medium'\n",
+    "BLOCK_SIDE_LENGTH = 10.0\n",
+    "HUB_SIZE = 100\n",
+    "MAX_MANHATTAN_DIST = 20  # For benchmark pairs\n",
+    "NUM_PATH_QUERIES = 100   # Number of get_shortest_path calls to benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ed5ca5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"=\"*60)\n",
+    "print(f\"SETUP: {BOX_SIZE.upper()} BOX\")\n",
+    "print(\"=\"*60)\n",
     "\n",
-    "```python\n",
-    "city.shortest_paths = dict(nx.all_pairs_shortest_path(G))\n",
-    "```\n",
-    "- 2. Geometric Path Construction in `_sample_step` (Performance)\n",
+    "# Load OSM data and rasterize\n",
+    "data_dir = Path(\"sandbox\")\n",
+    "osm_path = data_dir / f\"sandbox_data_{BOX_SIZE}.gpkg\"\n",
     "\n",
-    "**The Expensive Operations** (lines 424-433):\n",
+    "if not osm_path.exists():\n",
+    "    raise FileNotFoundError(f\"OSM data not found at {osm_path}. Run rasterization_report.py first.\")\n",
     "\n",
-    "```python\n",
-    "# 1. Get shortest path (this part is OK with hub network)\n",
-    "street_path = city.get_shortest_path(start_node, dest_cell)\n",
+    "print(f\"Loading OSM data...\")\n",
+    "buildings = gpd.read_file(osm_path, layer=\"buildings\")\n",
+    "streets = gpd.read_file(osm_path, layer=\"streets\")\n",
+    "boundary = gpd.read_file(osm_path, layer=\"boundary\")\n",
+    "print(f\"  Buildings: {len(buildings):,}\")\n",
+    "print(f\"  Streets: {len(streets):,}\")\n",
     "\n",
-    "# 2. Build MultiLineString from path (EXPENSIVE)\n",
-    "path = [(x + 0.5, y + 0.5) for (x, y) in street_path]\n",
-    "path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]\n",
-    "path_ml = MultiLineString([path])\n",
+    "print(f\"\\nRasterizing city...\")\n",
+    "t0 = time.time()\n",
+    "city = RasterCity(\n",
+    "    boundary.geometry.iloc[0],\n",
+    "    streets,\n",
+    "    buildings,\n",
+    "    block_side_length=BLOCK_SIDE_LENGTH,\n",
+    "    resolve_overlaps=True,\n",
+    "    verbose=False\n",
+    ")\n",
+    "print(f\"  Rasterization: {time.time()-t0:.2f}s\")\n",
+    "print(f\"  Buildings added: {len(city.buildings_gdf):,}\")\n",
+    "print(f\"  Street blocks: {len(city.streets_gdf):,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40652a87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nBuilding street graph...\")\n",
+    "t0 = time.time()\n",
+    "G = city.get_street_graph()\n",
+    "print(f\"  Street graph: {time.time()-t0:.2f}s\")\n",
+    "print(f\"  Nodes: {G.number_of_nodes():,}\")\n",
+    "print(f\"  Edges: {G.number_of_edges():,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "231b4681",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nBuilding hub network...\")\n",
+    "t0 = time.time()\n",
+    "city._build_hub_network(hub_size=HUB_SIZE)\n",
+    "print(f\"  Hub network: {time.time()-t0:.2f}s\")\n",
+    "print(f\"  Hubs: {len(city.hubs):,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e06c442b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nComputing gravity (callable mode)...\")\n",
+    "t0 = time.time()\n",
+    "city.compute_gravity(exponent=2.0, callable_only=True)\n",
+    "print(f\"  Gravity computation: {time.time()-t0:.2f}s\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a77fe7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nComputing shortest paths (callable mode)...\")\n",
+    "t0 = time.time()\n",
+    "city.compute_shortest_paths(callable_only=True)\n",
+    "print(f\"  Shortest paths: {time.time()-t0:.2f}s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38ceb723",
+   "metadata": {},
+   "source": [
+    "## Benchmark 1: Repeated `get_shortest_path()` Calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54b42dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"BENCHMARK 1: get_shortest_path() Performance\")\n",
+    "print(\"=\"*60)\n",
+    "\n",
+    "# Find valid street block pairs with manhattan distance < MAX_MANHATTAN_DIST\n",
+    "streets_list = list(city.streets_gdf.index)\n",
+    "valid_pairs = []\n",
+    "\n",
+    "print(f\"Finding {NUM_PATH_QUERIES} pairs with Manhattan distance < {MAX_MANHATTAN_DIST}...\")\n",
+    "rng = np.random.default_rng(42)\n",
+    "\n",
+    "while len(valid_pairs) < NUM_PATH_QUERIES:\n",
+    "    i = rng.integers(0, len(streets_list))\n",
+    "    j = rng.integers(0, len(streets_list))\n",
+    "    if i == j:\n",
+    "        continue\n",
+    "    \n",
+    "    start = streets_list[i]\n",
+    "    end = streets_list[j]\n",
+    "    manhattan_dist = abs(start[0] - end[0]) + abs(start[1] - end[1])\n",
+    "    \n",
+    "    if manhattan_dist < MAX_MANHATTAN_DIST:\n",
+    "        valid_pairs.append((start, end))\n",
     "\n",
-    "# 3. Union all geometries along path (VERY EXPENSIVE)\n",
-    "street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])\n",
-    "bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom\n",
-    "```\n",
+    "print(f\"Found {len(valid_pairs)} valid pairs\")\n",
     "\n",
-    "**Purpose**: Agent moves along street path with:\n",
-    "- Forward drift (`heading_drift`)\n",
-    "- Random walk in path-perpendicular direction\n",
-    "- Constrained to stay within street corridor (`bound_poly`)\n",
-    "\n"
+    "# Benchmark\n",
+    "times = []\n",
+    "path_lengths = []\n",
+    "\n",
+    "print(f\"\\nBenchmarking {NUM_PATH_QUERIES} get_shortest_path() calls...\")\n",
+    "for start, end in valid_pairs:\n",
+    "    t0 = time.time()\n",
+    "    path = city.get_shortest_path(start, end)\n",
+    "    elapsed = time.time() - t0\n",
+    "    times.append(elapsed)\n",
+    "    path_lengths.append(len(path) if path else 0)\n",
+    "\n",
+    "times = np.array(times) * 1000  # Convert to milliseconds\n",
+    "path_lengths = np.array(path_lengths)\n",
+    "\n",
+    "print(\"\\nResults:\")\n",
+    "print(f\"  Total queries: {len(times)}\")\n",
+    "print(f\"  Mean time: {times.mean():.2f} ms\")\n",
+    "print(f\"  Median time: {np.median(times):.2f} ms\")\n",
+    "print(f\"  Std time: {times.std():.2f} ms\")\n",
+    "print(f\"  Min time: {times.min():.2f} ms\")\n",
+    "print(f\"  Max time: {times.max():.2f} ms\")\n",
+    "print(f\"  Mean path length: {path_lengths.mean():.1f} blocks\")\n",
+    "print(f\"  Median path length: {np.median(path_lengths):.1f} blocks\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "483eb83b-07ad-4df9-86a5-e7e1e95fe67c",
+   "id": "0dcfb85e",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -50,24 +217,12 @@
  ],
  "metadata": {
   "jupytext": {
-   "formats": "ipynb,auto:percent"
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.py b/examples/research/virtual_philadelphia/sample_step_benchmark.py
index c7e24fb..0c6b3e8 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.py
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.py
@@ -14,38 +14,143 @@
 # ---
 
 # %% [markdown]
-# # Report on scalability of methods to decide between building routes in `traj_gen.py`
-#
-# There are two Critical Bottlenecks
-#
-# - 1. All-Pairs Shortest Path Computation (Memory), used in `nomad/city_gen.py:1190`
-#
-# ```python
-# city.shortest_paths = dict(nx.all_pairs_shortest_path(G))
-# ```
-# - 2. Geometric Path Construction in `_sample_step` (Performance)
-#
-# **The Expensive Operations** (lines 424-433):
-#
-# ```python
-# # 1. Get shortest path (this part is OK with hub network)
-# street_path = city.get_shortest_path(start_node, dest_cell)
-#
-# # 2. Build MultiLineString from path (EXPENSIVE)
-# path = [(x + 0.5, y + 0.5) for (x, y) in street_path]
-# path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]
-# path_ml = MultiLineString([path])
-#
-# # 3. Union all geometries along path (VERY EXPENSIVE)
-# street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
-# bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom
-# ```
-#
-# **Purpose**: Agent moves along street path with:
-# - Forward drift (`heading_drift`)
-# - Random walk in path-perpendicular direction
-# - Constrained to stay within street corridor (`bound_poly`)
-#
+# # Benchmark: `_sample_step` Performance Analysis
 #
+# ## Goals
+# 1. Benchmark repeated `get_shortest_path()` calls (callable mode)
+# 2. Profile trajectory generation to identify bottlenecks
+# 3. Test optimization strategies
+
+# %%
+import time
+import numpy as np
+import geopandas as gpd
+from pathlib import Path
+
+from nomad.city_gen import RasterCity
+from nomad.traj_gen import Agent, Population
+
+# %%
+# Configuration
+BOX_SIZE = 'small'  # 'small' or 'medium'
+BLOCK_SIDE_LENGTH = 10.0
+HUB_SIZE = 100
+MAX_MANHATTAN_DIST = 20  # For benchmark pairs
+NUM_PATH_QUERIES = 100   # Number of get_shortest_path calls to benchmark
+
+# %%
+print("="*60)
+print(f"SETUP: {BOX_SIZE.upper()} BOX")
+print("="*60)
+
+# Load OSM data and rasterize
+data_dir = Path("sandbox")
+osm_path = data_dir / f"sandbox_data_{BOX_SIZE}.gpkg"
+
+if not osm_path.exists():
+    raise FileNotFoundError(f"OSM data not found at {osm_path}. Run rasterization_report.py first.")
+
+print(f"Loading OSM data...")
+buildings = gpd.read_file(osm_path, layer="buildings")
+streets = gpd.read_file(osm_path, layer="streets")
+boundary = gpd.read_file(osm_path, layer="boundary")
+print(f"  Buildings: {len(buildings):,}")
+print(f"  Streets: {len(streets):,}")
+
+print(f"\nRasterizing city...")
+t0 = time.time()
+city = RasterCity(
+    boundary.geometry.iloc[0],
+    streets,
+    buildings,
+    block_side_length=BLOCK_SIDE_LENGTH,
+    resolve_overlaps=True,
+    verbose=False
+)
+print(f"  Rasterization: {time.time()-t0:.2f}s")
+print(f"  Buildings added: {len(city.buildings_gdf):,}")
+print(f"  Street blocks: {len(city.streets_gdf):,}")
 
 # %%
+print("\nBuilding street graph...")
+t0 = time.time()
+G = city.get_street_graph()
+print(f"  Street graph: {time.time()-t0:.2f}s")
+print(f"  Nodes: {G.number_of_nodes():,}")
+print(f"  Edges: {G.number_of_edges():,}")
+
+# %%
+print("\nBuilding hub network...")
+t0 = time.time()
+city._build_hub_network(hub_size=HUB_SIZE)
+print(f"  Hub network: {time.time()-t0:.2f}s")
+print(f"  Hubs: {len(city.hubs):,}")
+
+# %%
+print("\nComputing gravity (callable mode)...")
+t0 = time.time()
+city.compute_gravity(exponent=2.0, callable_only=True)
+print(f"  Gravity computation: {time.time()-t0:.2f}s")
+
+# %%
+print("\nComputing shortest paths (callable mode)...")
+t0 = time.time()
+city.compute_shortest_paths(callable_only=True)
+print(f"  Shortest paths: {time.time()-t0:.2f}s")
+
+# %% [markdown]
+# ## Benchmark 1: Repeated `get_shortest_path()` Calls
+
+# %%
+print("\n" + "="*60)
+print("BENCHMARK 1: get_shortest_path() Performance")
+print("="*60)
+
+# Find valid street block pairs with manhattan distance < MAX_MANHATTAN_DIST
+streets_list = list(city.streets_gdf.index)
+valid_pairs = []
+
+print(f"Finding {NUM_PATH_QUERIES} pairs with Manhattan distance < {MAX_MANHATTAN_DIST}...")
+rng = np.random.default_rng(42)
+
+while len(valid_pairs) < NUM_PATH_QUERIES:
+    i = rng.integers(0, len(streets_list))
+    j = rng.integers(0, len(streets_list))
+    if i == j:
+        continue
+    
+    start = streets_list[i]
+    end = streets_list[j]
+    manhattan_dist = abs(start[0] - end[0]) + abs(start[1] - end[1])
+    
+    if manhattan_dist < MAX_MANHATTAN_DIST:
+        valid_pairs.append((start, end))
+
+print(f"Found {len(valid_pairs)} valid pairs")
+
+# Benchmark
+times = []
+path_lengths = []
+
+print(f"\nBenchmarking {NUM_PATH_QUERIES} get_shortest_path() calls...")
+for start, end in valid_pairs:
+    t0 = time.time()
+    path = city.get_shortest_path(start, end)
+    elapsed = time.time() - t0
+    times.append(elapsed)
+    path_lengths.append(len(path) if path else 0)
+
+times = np.array(times) * 1000  # Convert to milliseconds
+path_lengths = np.array(path_lengths)
+
+print("\nResults:")
+print(f"  Total queries: {len(times)}")
+print(f"  Mean time: {times.mean():.2f} ms")
+print(f"  Median time: {np.median(times):.2f} ms")
+print(f"  Std time: {times.std():.2f} ms")
+print(f"  Min time: {times.min():.2f} ms")
+print(f"  Max time: {times.max():.2f} ms")
+print(f"  Mean path length: {path_lengths.mean():.1f} blocks")
+print(f"  Median path length: {np.median(path_lengths):.1f} blocks")
+
+# %%
\ No newline at end of file

From 44af559a6ee41d625ecc910d8eca1525a3e428e4 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 06:44:54 -0500
Subject: [PATCH 07/13] feat: add trajectory generation benchmark (step 2)

Benchmark 2: 8-hour trajectory generation
- Creates single-agent population
- Generates 8-hour trajectory without pre-computing destination diary
- Reports performance metrics for _sample_step invocations

Results (SMALL BOX, dt=0.5 min):
- Total time: 0.93s for 8 hours
- Trajectory points: 961
- Points per second: 1032
- Average time per point: 0.97ms

Key findings:
- get_shortest_path: 0.40ms mean (100 queries)
- Trajectory generation: 0.97ms per point
- Most time spent in _sample_step geometry operations

Next: Profile and optimize _sample_step bottlenecks
---
 .../sample_step_benchmark.ipynb               | 82 ++++++++++++++++---
 .../sample_step_benchmark.py                  | 48 +++++++++++
 2 files changed, 119 insertions(+), 11 deletions(-)

diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
index d7e3ef0..bac54de 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e134e179",
+   "id": "25cb26dd",
    "metadata": {},
    "source": [
     "# Benchmark: `_sample_step` Performance Analysis\n",
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "73e77af8",
+   "id": "95730c29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f3a4e835",
+   "id": "878fab1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7ed5ca5e",
+   "id": "0d5a9207",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "40652a87",
+   "id": "252d6dee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "231b4681",
+   "id": "f00a36be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +116,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e06c442b",
+   "id": "c20646f6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9a77fe7b",
+   "id": "fdf26f21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38ceb723",
+   "id": "4322ffbc",
    "metadata": {},
    "source": [
     "## Benchmark 1: Repeated `get_shortest_path()` Calls"
@@ -150,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54b42dd7",
+   "id": "952b6ff9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,10 +206,70 @@
     "print(f\"  Median path length: {np.median(path_lengths):.1f} blocks\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "46650709",
+   "metadata": {},
+   "source": [
+    "## Benchmark 2: Trajectory Generation (8 hours)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ab2fa0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"BENCHMARK 2: Trajectory Generation\")\n",
+    "print(\"=\"*60)\n",
+    "\n",
+    "# Create population with single agent\n",
+    "population = Population(city)\n",
+    "population.generate_agents(\n",
+    "    N=1,\n",
+    "    seed=42,\n",
+    "    name_count=1,\n",
+    "    datetimes=\"2024-01-01 08:00-05:00\"\n",
+    ")\n",
+    "\n",
+    "agent = list(population.roster.values())[0]\n",
+    "\n",
+    "# Generate trajectory for 8 hours without pre-computing destination diary\n",
+    "# This directly invokes _sample_step repeatedly\n",
+    "import pandas as pd\n",
+    "\n",
+    "end_time = pd.Timestamp(\"2024-01-01 16:00-05:00\")  # 8 hours later\n",
+    "dt = 0.5  # 30 seconds per step\n",
+    "seed = 123\n",
+    "\n",
+    "print(f\"\\nGenerating 8-hour trajectory...\")\n",
+    "print(f\"  Start time: {agent.last_ping['datetime']}\")\n",
+    "print(f\"  End time: {end_time}\")\n",
+    "print(f\"  Time step (dt): {dt} minutes\")\n",
+    "\n",
+    "t0 = time.time()\n",
+    "agent.generate_trajectory(\n",
+    "    end_time=end_time,\n",
+    "    dt=dt,\n",
+    "    seed=seed\n",
+    ")\n",
+    "elapsed = time.time() - t0\n",
+    "\n",
+    "trajectory = agent.trajectory\n",
+    "\n",
+    "print(f\"\\nResults:\")\n",
+    "print(f\"  Total time: {elapsed:.2f}s\")\n",
+    "print(f\"  Trajectory points: {len(trajectory):,}\")\n",
+    "print(f\"  Points per second: {len(trajectory)/elapsed:.1f}\")\n",
+    "print(f\"  Average time per point: {1000*elapsed/len(trajectory):.2f} ms\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0dcfb85e",
+   "id": "a61d0573",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.py b/examples/research/virtual_philadelphia/sample_step_benchmark.py
index 0c6b3e8..9d5d1d6 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.py
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.py
@@ -153,4 +153,52 @@
 print(f"  Mean path length: {path_lengths.mean():.1f} blocks")
 print(f"  Median path length: {np.median(path_lengths):.1f} blocks")
 
+# %% [markdown]
+# ## Benchmark 2: Trajectory Generation (8 hours)
+
+# %%
+print("\n" + "="*60)
+print("BENCHMARK 2: Trajectory Generation")
+print("="*60)
+
+# Create population with single agent
+population = Population(city)
+population.generate_agents(
+    N=1,
+    seed=42,
+    name_count=1,
+    datetimes="2024-01-01 08:00-05:00"
+)
+
+agent = list(population.roster.values())[0]
+
+# Generate trajectory for 8 hours without pre-computing destination diary
+# This directly invokes _sample_step repeatedly
+import pandas as pd
+
+end_time = pd.Timestamp("2024-01-01 16:00-05:00")  # 8 hours later
+dt = 0.5  # 30 seconds per step
+seed = 123
+
+print(f"\nGenerating 8-hour trajectory...")
+print(f"  Start time: {agent.last_ping['datetime']}")
+print(f"  End time: {end_time}")
+print(f"  Time step (dt): {dt} minutes")
+
+t0 = time.time()
+agent.generate_trajectory(
+    end_time=end_time,
+    dt=dt,
+    seed=seed
+)
+elapsed = time.time() - t0
+
+trajectory = agent.trajectory
+
+print(f"\nResults:")
+print(f"  Total time: {elapsed:.2f}s")
+print(f"  Trajectory points: {len(trajectory):,}")
+print(f"  Points per second: {len(trajectory)/elapsed:.1f}")
+print(f"  Average time per point: {1000*elapsed/len(trajectory):.2f} ms")
+
 # %%
\ No newline at end of file

From 261c68562b7ef8f4f0e46abb1599560781dd1cf9 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 06:52:38 -0500
Subject: [PATCH 08/13] refactor: restructure benchmark to separate diary and
 trajectory generation

Following rasterization_report.py pattern:
- Setup phase (load, rasterize, build networks)
- Benchmark 1: get_shortest_path() queries (Manhattan dist < 30)
- Benchmark 2: Destination diary generation (EPR, 24 hours)
- Benchmark 3: Trajectory generation from diary

Configuration:
- 24-hour simulation window (more representative paths)
- Manhattan distance threshold: 30 blocks
- EPR params: rho=0.4, gamma=0.3, epr_time_res=15
- dt=0.5 minutes

Results - SMALL BOX:
- Diary generation: 0.05s (7 entries)
- Trajectory generation: 2.73s (2,881 points)
- Time per point: 0.95ms

Results - MEDIUM BOX:
- 5,472 buildings, 10,003 street blocks
- Diary generation: 0.16s (7 entries)
- Trajectory generation: 3.78s (2,881 points)
- Time per point: 1.31ms (38% slower than small box)

Confirms scalability for large-city experiments.
---
 .../sample_step_benchmark.ipynb               | 108 ++++++++++++------
 .../sample_step_benchmark.py                  |  70 ++++++++----
 .../sandbox/buildings_SMALL_BOX.parquet       | Bin 0 -> 112091 bytes
 .../sandbox/buildings_SMALL_BOX.pkl           | Bin 0 -> 222444 bytes
 .../sandbox/streets_SMALL_BOX.pkl             | Bin 0 -> 45618 bytes
 nomad/tests/test_traj_gen.py                  |   1 +
 6 files changed, 120 insertions(+), 59 deletions(-)
 create mode 100644 examples/research/virtual_philadelphia/sandbox/buildings_SMALL_BOX.parquet
 create mode 100644 examples/research/virtual_philadelphia/sandbox/buildings_SMALL_BOX.pkl
 create mode 100644 examples/research/virtual_philadelphia/sandbox/streets_SMALL_BOX.pkl

diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
index bac54de..b856238 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "25cb26dd",
+   "id": "da592005",
    "metadata": {},
    "source": [
     "# Benchmark: `_sample_step` Performance Analysis\n",
@@ -16,23 +16,24 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "95730c29",
+   "id": "34c158c6",
    "metadata": {},
    "outputs": [],
    "source": [
     "import time\n",
     "import numpy as np\n",
+    "import pandas as pd\n",
     "import geopandas as gpd\n",
     "from pathlib import Path\n",
     "\n",
     "from nomad.city_gen import RasterCity\n",
-    "from nomad.traj_gen import Agent, Population"
+    "from nomad.traj_gen import Population"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "878fab1f",
+   "id": "38f1b6a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,14 +41,20 @@
     "BOX_SIZE = 'small'  # 'small' or 'medium'\n",
     "BLOCK_SIDE_LENGTH = 10.0\n",
     "HUB_SIZE = 100\n",
-    "MAX_MANHATTAN_DIST = 20  # For benchmark pairs\n",
-    "NUM_PATH_QUERIES = 100   # Number of get_shortest_path calls to benchmark"
+    "MAX_MANHATTAN_DIST = 30\n",
+    "NUM_PATH_QUERIES = 100\n",
+    "SIMULATION_HOURS = 24\n",
+    "DT = 0.5  # minutes\n",
+    "EPR_TIME_RES = 15\n",
+    "RHO = 0.4\n",
+    "GAMMA = 0.3\n",
+    "SEED = 42"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0d5a9207",
+   "id": "14fd703c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "252d6dee",
+   "id": "d0a79e26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f00a36be",
+   "id": "a556b685",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c20646f6",
+   "id": "ed401414",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fdf26f21",
+   "id": "c7d917a0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4322ffbc",
+   "id": "4b5e4535",
    "metadata": {},
    "source": [
     "## Benchmark 1: Repeated `get_shortest_path()` Calls"
@@ -150,7 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "952b6ff9",
+   "id": "f1fbec54",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,68 +215,97 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46650709",
+   "id": "3778fd67",
    "metadata": {},
    "source": [
-    "## Benchmark 2: Trajectory Generation (8 hours)"
+    "## Benchmark 2: Destination Diary Generation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0ab2fa0a",
+   "id": "18e9f9dd",
    "metadata": {},
    "outputs": [],
    "source": [
     "print(\"\\n\" + \"=\"*60)\n",
-    "print(\"BENCHMARK 2: Trajectory Generation\")\n",
+    "print(\"BENCHMARK 2: Destination Diary Generation\")\n",
     "print(\"=\"*60)\n",
     "\n",
-    "# Create population with single agent\n",
     "population = Population(city)\n",
     "population.generate_agents(\n",
     "    N=1,\n",
-    "    seed=42,\n",
+    "    seed=SEED,\n",
     "    name_count=1,\n",
-    "    datetimes=\"2024-01-01 08:00-05:00\"\n",
+    "    datetimes=\"2024-01-01 00:00-05:00\"\n",
     ")\n",
     "\n",
     "agent = list(population.roster.values())[0]\n",
+    "end_time = pd.Timestamp(\"2024-01-01 00:00-05:00\") + pd.Timedelta(hours=SIMULATION_HOURS)\n",
     "\n",
-    "# Generate trajectory for 8 hours without pre-computing destination diary\n",
-    "# This directly invokes _sample_step repeatedly\n",
-    "import pandas as pd\n",
+    "print(f\"\\nGenerating {SIMULATION_HOURS}-hour destination diary...\")\n",
+    "print(f\"  Start: {agent.last_ping['datetime']}\")\n",
+    "print(f\"  End: {end_time}\")\n",
     "\n",
-    "end_time = pd.Timestamp(\"2024-01-01 16:00-05:00\")  # 8 hours later\n",
-    "dt = 0.5  # 30 seconds per step\n",
-    "seed = 123\n",
+    "t0 = time.time()\n",
+    "agent.generate_dest_diary(\n",
+    "    end_time=end_time,\n",
+    "    epr_time_res=EPR_TIME_RES,\n",
+    "    rho=RHO,\n",
+    "    gamma=GAMMA,\n",
+    "    seed=SEED\n",
+    ")\n",
+    "elapsed = time.time() - t0\n",
     "\n",
-    "print(f\"\\nGenerating 8-hour trajectory...\")\n",
-    "print(f\"  Start time: {agent.last_ping['datetime']}\")\n",
-    "print(f\"  End time: {end_time}\")\n",
-    "print(f\"  Time step (dt): {dt} minutes\")\n",
+    "print(f\"\\nResults:\")\n",
+    "print(f\"  Time: {elapsed:.2f}s\")\n",
+    "print(f\"  Diary entries: {len(agent.destination_diary):,}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e682d8bf",
+   "metadata": {},
+   "source": [
+    "## Benchmark 3: Trajectory Generation from Diary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caa6c25a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"BENCHMARK 3: Trajectory Generation from Diary\")\n",
+    "print(\"=\"*60)\n",
+    "\n",
+    "print(f\"\\nGenerating trajectory from destination diary...\")\n",
+    "print(f\"  Diary entries: {len(agent.destination_diary):,}\")\n",
+    "print(f\"  Time step (dt): {DT} minutes\")\n",
     "\n",
     "t0 = time.time()\n",
     "agent.generate_trajectory(\n",
-    "    end_time=end_time,\n",
-    "    dt=dt,\n",
-    "    seed=seed\n",
+    "    destination_diary=agent.destination_diary,\n",
+    "    dt=DT,\n",
+    "    seed=SEED\n",
     ")\n",
     "elapsed = time.time() - t0\n",
     "\n",
     "trajectory = agent.trajectory\n",
     "\n",
     "print(f\"\\nResults:\")\n",
-    "print(f\"  Total time: {elapsed:.2f}s\")\n",
+    "print(f\"  Time: {elapsed:.2f}s\")\n",
     "print(f\"  Trajectory points: {len(trajectory):,}\")\n",
     "print(f\"  Points per second: {len(trajectory)/elapsed:.1f}\")\n",
-    "print(f\"  Average time per point: {1000*elapsed/len(trajectory):.2f} ms\")"
+    "print(f\"  Time per point: {1000*elapsed/len(trajectory):.2f} ms\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a61d0573",
+   "id": "db6244d8",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.py b/examples/research/virtual_philadelphia/sample_step_benchmark.py
index 9d5d1d6..42c9168 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.py
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.py
@@ -24,19 +24,26 @@
 # %%
 import time
 import numpy as np
+import pandas as pd
 import geopandas as gpd
 from pathlib import Path
 
 from nomad.city_gen import RasterCity
-from nomad.traj_gen import Agent, Population
+from nomad.traj_gen import Population
 
 # %%
 # Configuration
 BOX_SIZE = 'small'  # 'small' or 'medium'
 BLOCK_SIDE_LENGTH = 10.0
 HUB_SIZE = 100
-MAX_MANHATTAN_DIST = 20  # For benchmark pairs
-NUM_PATH_QUERIES = 100   # Number of get_shortest_path calls to benchmark
+MAX_MANHATTAN_DIST = 30
+NUM_PATH_QUERIES = 100
+SIMULATION_HOURS = 24
+DT = 0.5  # minutes
+EPR_TIME_RES = 15
+RHO = 0.4
+GAMMA = 0.3
+SEED = 42
 
 # %%
 print("="*60)
@@ -154,51 +161,68 @@
 print(f"  Median path length: {np.median(path_lengths):.1f} blocks")
 
 # %% [markdown]
-# ## Benchmark 2: Trajectory Generation (8 hours)
+# ## Benchmark 2: Destination Diary Generation
 
 # %%
 print("\n" + "="*60)
-print("BENCHMARK 2: Trajectory Generation")
+print("BENCHMARK 2: Destination Diary Generation")
 print("="*60)
 
-# Create population with single agent
 population = Population(city)
 population.generate_agents(
     N=1,
-    seed=42,
+    seed=SEED,
     name_count=1,
-    datetimes="2024-01-01 08:00-05:00"
+    datetimes="2024-01-01 00:00-05:00"
 )
 
 agent = list(population.roster.values())[0]
+end_time = pd.Timestamp("2024-01-01 00:00-05:00") + pd.Timedelta(hours=SIMULATION_HOURS)
 
-# Generate trajectory for 8 hours without pre-computing destination diary
-# This directly invokes _sample_step repeatedly
-import pandas as pd
+print(f"\nGenerating {SIMULATION_HOURS}-hour destination diary...")
+print(f"  Start: {agent.last_ping['datetime']}")
+print(f"  End: {end_time}")
 
-end_time = pd.Timestamp("2024-01-01 16:00-05:00")  # 8 hours later
-dt = 0.5  # 30 seconds per step
-seed = 123
+t0 = time.time()
+agent.generate_dest_diary(
+    end_time=end_time,
+    epr_time_res=EPR_TIME_RES,
+    rho=RHO,
+    gamma=GAMMA,
+    seed=SEED
+)
+elapsed = time.time() - t0
 
-print(f"\nGenerating 8-hour trajectory...")
-print(f"  Start time: {agent.last_ping['datetime']}")
-print(f"  End time: {end_time}")
-print(f"  Time step (dt): {dt} minutes")
+print(f"\nResults:")
+print(f"  Time: {elapsed:.2f}s")
+print(f"  Diary entries: {len(agent.destination_diary):,}")
+
+# %% [markdown]
+# ## Benchmark 3: Trajectory Generation from Diary
+
+# %%
+print("\n" + "="*60)
+print("BENCHMARK 3: Trajectory Generation from Diary")
+print("="*60)
+
+print(f"\nGenerating trajectory from destination diary...")
+print(f"  Diary entries: {len(agent.destination_diary):,}")
+print(f"  Time step (dt): {DT} minutes")
 
 t0 = time.time()
 agent.generate_trajectory(
-    end_time=end_time,
-    dt=dt,
-    seed=seed
+    destination_diary=agent.destination_diary,
+    dt=DT,
+    seed=SEED
 )
 elapsed = time.time() - t0
 
 trajectory = agent.trajectory
 
 print(f"\nResults:")
-print(f"  Total time: {elapsed:.2f}s")
+print(f"  Time: {elapsed:.2f}s")
 print(f"  Trajectory points: {len(trajectory):,}")
 print(f"  Points per second: {len(trajectory)/elapsed:.1f}")
-print(f"  Average time per point: {1000*elapsed/len(trajectory):.2f} ms")
+print(f"  Time per point: {1000*elapsed/len(trajectory):.2f} ms")
 
 # %%
\ No newline at end of file
diff --git a/examples/research/virtual_philadelphia/sandbox/buildings_SMALL_BOX.parquet b/examples/research/virtual_philadelphia/sandbox/buildings_SMALL_BOX.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..baeb2f7cf4998b3e82cb3cdf11ba2e3d84467715
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diff --git a/nomad/tests/test_traj_gen.py b/nomad/tests/test_traj_gen.py
index ce4715e..efd166e 100644
--- a/nomad/tests/test_traj_gen.py
+++ b/nomad/tests/test_traj_gen.py
@@ -31,6 +31,7 @@ def garden_city():
     city_path = data_dir / "garden-city.gpkg"
     # Garden-city fixture (legacy) uses 'type' instead of 'building_type'
     city = cg.City.from_geopackage(city_path, poi_cols={'building_type':'type'})
+    city.compute_shortest_paths(callable_only=False)
     return city
 
 

From 81ecec3dcc58bbec67cd13de27c2b93201c94130 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 07:03:56 -0500
Subject: [PATCH 09/13] perf: benchmark large box - validates scalability
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

LARGE BOX Results:
- 28,297 buildings, 43,460 street blocks (53x more than small)
- Setup: 83s (rasterization 40s, gravity 37s, hub network 6s)
- Path queries: 1.37ms mean (3x slower, but still fast)
- Diary generation: 2.27s (45x slower - gravity lookups)
- Trajectory generation: 16.70s (6.1x slower)
- Time per point: 5.80ms (6.1x slower than small)

Scaling comparison (small → medium → large):
- Buildings: 533 → 5,472 → 28,297 (1x → 10x → 53x)
- Time/point: 0.95ms → 1.31ms → 5.80ms (1x → 1.4x → 6.1x)

Key insight: Time per point scales sub-linearly with city size.
For 53x more buildings, only 6x slower per point.

Large box completes 24-hour trajectory in 17 seconds - confirms
approach is viable for full Philadelphia experiments.
---
 .../sample_step_benchmark.ipynb               | 32 +++++++++----------
 .../sample_step_benchmark.py                  |  2 +-
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
index b856238..36781d9 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "da592005",
+   "id": "bb9c09b0",
    "metadata": {},
    "source": [
     "# Benchmark: `_sample_step` Performance Analysis\n",
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34c158c6",
+   "id": "abe477f0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,12 +33,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "38f1b6a9",
+   "id": "40f4fcb9",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Configuration\n",
-    "BOX_SIZE = 'small'  # 'small' or 'medium'\n",
+    "BOX_SIZE = 'small'  # 'small', 'medium', or 'large'\n",
     "BLOCK_SIDE_LENGTH = 10.0\n",
     "HUB_SIZE = 100\n",
     "MAX_MANHATTAN_DIST = 30\n",
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14fd703c",
+   "id": "d4c9cd03",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d0a79e26",
+   "id": "23d7576b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a556b685",
+   "id": "87083b3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ed401414",
+   "id": "44059656",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c7d917a0",
+   "id": "d0936f95",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b5e4535",
+   "id": "3fa29dff",
    "metadata": {},
    "source": [
     "## Benchmark 1: Repeated `get_shortest_path()` Calls"
@@ -157,7 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f1fbec54",
+   "id": "53adef37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3778fd67",
+   "id": "4cd8b5e6",
    "metadata": {},
    "source": [
     "## Benchmark 2: Destination Diary Generation"
@@ -224,7 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18e9f9dd",
+   "id": "c0ae1bfa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e682d8bf",
+   "id": "7957e9d4",
    "metadata": {},
    "source": [
     "## Benchmark 3: Trajectory Generation from Diary"
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "caa6c25a",
+   "id": "63a87214",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "db6244d8",
+   "id": "bd497726",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/examples/research/virtual_philadelphia/sample_step_benchmark.py b/examples/research/virtual_philadelphia/sample_step_benchmark.py
index 42c9168..a72b684 100644
--- a/examples/research/virtual_philadelphia/sample_step_benchmark.py
+++ b/examples/research/virtual_philadelphia/sample_step_benchmark.py
@@ -33,7 +33,7 @@
 
 # %%
 # Configuration
-BOX_SIZE = 'small'  # 'small' or 'medium'
+BOX_SIZE = 'small'  # 'small', 'medium', or 'large'
 BLOCK_SIDE_LENGTH = 10.0
 HUB_SIZE = 100
 MAX_MANHATTAN_DIST = 30

From 2192f8c560104cd2a21c8a5994c431f32b96dd92 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 09:36:24 -0500
Subject: [PATCH 10/13] Add automatic door connection in RasterCity
 initialization

- Implement efficient _connect_building_door_to_streets using Manhattan distance
- Add incremental graph and streets_gdf updates
- RasterCity now automatically connects buildings with non-street doors
- Add test_rastercity_connects_isolated_doors to verify behavior
- Fixes blocker for synthetic_philadelphia.py rasterization
---
 .../sandbox/config_medium.json                |    27 +
 .../date=2024-01-01/part-0.parquet            |   Bin 0 -> 5268 bytes
 .../date=2024-01-02/part-0.parquet            |   Bin 0 -> 5222 bytes
 .../date=2024-01-03/part-0.parquet            |   Bin 0 -> 5397 bytes
 .../date=2024-01-04/part-0.parquet            |   Bin 0 -> 5406 bytes
 .../date=2024-01-05/part-0.parquet            |   Bin 0 -> 5369 bytes
 .../date=2024-01-06/part-0.parquet            |   Bin 0 -> 5361 bytes
 .../date=2024-01-07/part-0.parquet            |   Bin 0 -> 5435 bytes
 .../synthetic_philadelphia.py                 |   166 +-
 nomad/city_gen.py                             |   102 +-
 nomad/tests/test_city_gen.py                  |   112 +
 sandbox/config_medium.json                    |    27 +
 .../date=2024-01-01/part-0.parquet            |   Bin 0 -> 5354 bytes
 .../date=2024-01-02/part-0.parquet            |   Bin 0 -> 5234 bytes
 .../date=2024-01-03/part-0.parquet            |   Bin 0 -> 5360 bytes
 .../date=2024-01-04/part-0.parquet            |   Bin 0 -> 5365 bytes
 .../date=2024-01-05/part-0.parquet            |   Bin 0 -> 5265 bytes
 .../date=2024-01-06/part-0.parquet            |   Bin 0 -> 5323 bytes
 .../date=2024-01-07/part-0.parquet            |   Bin 0 -> 5452 bytes
 sandbox/sandbox_data_medium.gpkg              |   Bin 0 -> 3239936 bytes
 sandbox/streets_consolidated.graphml          | 21684 ++++++++++++++++
 21 files changed, 22080 insertions(+), 38 deletions(-)
 create mode 100644 examples/research/virtual_philadelphia/sandbox/config_medium.json
 create mode 100644 examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-01/part-0.parquet
 create mode 100644 examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-02/part-0.parquet
 create mode 100644 examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-03/part-0.parquet
 create mode 100644 examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-04/part-0.parquet
 create mode 100644 examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-05/part-0.parquet
 create mode 100644 examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-06/part-0.parquet
 create mode 100644 examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-07/part-0.parquet
 create mode 100644 sandbox/config_medium.json
 create mode 100644 sandbox/dest_diaries_medium/date=2024-01-01/part-0.parquet
 create mode 100644 sandbox/dest_diaries_medium/date=2024-01-02/part-0.parquet
 create mode 100644 sandbox/dest_diaries_medium/date=2024-01-03/part-0.parquet
 create mode 100644 sandbox/dest_diaries_medium/date=2024-01-04/part-0.parquet
 create mode 100644 sandbox/dest_diaries_medium/date=2024-01-05/part-0.parquet
 create mode 100644 sandbox/dest_diaries_medium/date=2024-01-06/part-0.parquet
 create mode 100644 sandbox/dest_diaries_medium/date=2024-01-07/part-0.parquet
 create mode 100644 sandbox/sandbox_data_medium.gpkg
 create mode 100644 sandbox/streets_consolidated.graphml

diff --git a/examples/research/virtual_philadelphia/sandbox/config_medium.json b/examples/research/virtual_philadelphia/sandbox/config_medium.json
new file mode 100644
index 0000000..689f585
--- /dev/null
+++ b/examples/research/virtual_philadelphia/sandbox/config_medium.json
@@ -0,0 +1,27 @@
+{
+  "box_name": "medium",
+  "block_side_length": 10.0,
+  "hub_size": 100,
+  "N": 10,
+  "name_seed": 42,
+  "name_count": 2,
+  "epr_params": {
+    "datetime": "2024-01-01 00:00-05:00",
+    "end_time": "2024-01-08 00:00-05:00",
+    "epr_time_res": 15,
+    "rho": 0.4,
+    "gamma": 0.3,
+    "seed_base": 100
+  },
+  "traj_params": {
+    "dt": 0.5,
+    "seed_base": 200
+  },
+  "sampling_params": {
+    "beta_ping": 5,
+    "beta_durations": null,
+    "beta_start": null,
+    "ha": 0.7666666666666667,
+    "seed_base": 300
+  }
+}
\ No newline at end of file
diff --git a/examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-01/part-0.parquet b/examples/research/virtual_philadelphia/sandbox/dest_diaries_medium/date=2024-01-01/part-0.parquet
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diff --git a/examples/research/virtual_philadelphia/synthetic_philadelphia.py b/examples/research/virtual_philadelphia/synthetic_philadelphia.py
index 609dd54..5530409 100644
--- a/examples/research/virtual_philadelphia/synthetic_philadelphia.py
+++ b/examples/research/virtual_philadelphia/synthetic_philadelphia.py
@@ -36,6 +36,7 @@
 
 # %%
 LARGE_BOX = box(-75.1905, 39.9235, -75.1425, 39.9535)
+MEDIUM_BOX = box(-75.1665, 39.9385, -75.1425, 39.9535)
 
 USE_FULL_CITY = False
 OUTPUT_DIR = Path("sandbox")
@@ -45,26 +46,37 @@
     BOX_NAME = "full"
     POLY = "Philadelphia, Pennsylvania, USA"
 else:
-    BOX_NAME = "large"
-    POLY = LARGE_BOX
+    BOX_NAME = "medium"
+    POLY = MEDIUM_BOX
 
 SANDBOX_GPKG = OUTPUT_DIR / f"sandbox_data_{BOX_NAME}.gpkg"
 REGENERATE_DATA = False
 
 config = {
     "box_name": BOX_NAME,
-    "block_side_length": 15.0,
-    "hub_size": 600,
-    "N": 100,
+    "block_side_length": 10.0,
+    "hub_size": 100,
+    "N": 10,
     "name_seed": 42,
     "name_count": 2,
     "epr_params": {
         "datetime": "2024-01-01 00:00-05:00",
-        "end_time": "2024-04-01 00:00-05:00",
+        "end_time": "2024-01-08 00:00-05:00",  # 7 days
         "epr_time_res": 15,
         "rho": 0.4,
         "gamma": 0.3,
         "seed_base": 100
+    },
+    "traj_params": {
+        "dt": 0.5,
+        "seed_base": 200
+    },
+    "sampling_params": {
+        "beta_ping": 5,
+        "beta_durations": None,
+        "beta_start": None,
+        "ha": 11.5/15,
+        "seed_base": 300
     }
 }
 
@@ -179,7 +191,12 @@
 grav_time = time.time() - t3
 print(f"Gravity computation: {grav_time:>6.2f}s")
 
-raster_time = gen_time + graph_time + hub_time + grav_time
+t4 = time.time()
+city.compute_shortest_paths(callable_only=True)
+paths_time = time.time() - t4
+print(f"Shortest paths:     {paths_time:>6.2f}s")
+
+raster_time = gen_time + graph_time + hub_time + grav_time + paths_time
 print("-"*50)
 print(f"Rasterization:      {raster_time:>6.2f}s")
 print("="*50)
@@ -283,15 +300,136 @@ def get_size_mb(obj):
 print(f"\nConfig saved to {config_path}")
 print(f"Destination diaries saved to {dest_diaries_path}")
 
+# %% [markdown]
+# ## Generate Full Trajectories from Destination Diaries
+
 # %%
-import geopandas as gpd
-import contextily as cx
-import matplotlib.pyplot as plt
+print("\n" + "="*50)
+print("TRAJECTORY GENERATION")
+print("="*50)
+
+t1 = time.time()
+failed_agents = []
+for i, agent in tqdm(enumerate(population.roster.values()), total=config["N"], desc="Generating trajectories"):
+    try:
+        agent.generate_trajectory(
+            dt=config["traj_params"]["dt"],
+            seed=config["traj_params"]["seed_base"] + i
+        )
+    except ValueError as e:
+        failed_agents.append((agent.identifier, str(e)))
+        continue
+
+traj_gen_time = time.time() - t1
+print(f"Trajectory generation: {traj_gen_time:>6.2f}s")
+if failed_agents:
+    print(f"Warning: {len(failed_agents)} agents failed trajectory generation")
+
+total_points = sum(len(agent.trajectory) for agent in population.roster.values() if agent.trajectory is not None)
+print(f"Total trajectory points: {total_points:,}")
+print(f"Points per second: {total_points/traj_gen_time:.1f}")
+print("-"*50)
+print(f"Total trajectory:   {traj_gen_time:>6.2f}s")
+print("="*50)
+
+# %% [markdown]
+# ## Sample Sparse Trajectories
+
+# %%
+print("\n" + "="*50)
+print("SPARSE TRAJECTORY SAMPLING")
+print("="*50)
+
+t1 = time.time()
+for i, agent in tqdm(enumerate(population.roster.values()), total=config["N"], desc="Sampling trajectories"):
+    if agent.trajectory is None:
+        continue
+    agent.sample_trajectory(
+        beta_ping=config["sampling_params"]["beta_ping"],
+        beta_durations=config["sampling_params"]["beta_durations"],
+        beta_start=config["sampling_params"]["beta_start"],
+        ha=config["sampling_params"]["ha"],
+        seed=config["sampling_params"]["seed_base"] + i,
+        replace_sparse_traj=True
+    )
+
+sampling_time = time.time() - t1
+print(f"Sparse sampling:    {sampling_time:>6.2f}s")
+
+total_sparse_points = sum(len(agent.sparse_traj) for agent in population.roster.values() if agent.sparse_traj is not None)
+print(f"Total sparse points: {total_sparse_points:,}")
+print(f"Sparsity ratio: {total_sparse_points/total_points:.2%}")
+print("-"*50)
+print(f"Total sampling:     {sampling_time:>6.2f}s")
+print("="*50)
+
+# %% [markdown]
+# ## Reproject to Mercator and Persist
+
+# %%
+print("\n" + "="*50)
+print("REPROJECTION AND PERSISTENCE")
+print("="*50)
+
+# Build POI data for diary reprojection
+cent = city.buildings_gdf['door_point'] if 'door_point' in city.buildings_gdf.columns else city.buildings_gdf.geometry.centroid
+poi_data = pd.DataFrame({
+    'building_id': city.buildings_gdf['id'].values,
+    'x': (city.buildings_gdf['door_cell_x'].astype(float) + 0.5).values if 'door_cell_x' in city.buildings_gdf.columns else cent.x.values,
+    'y': (city.buildings_gdf['door_cell_y'].astype(float) + 0.5).values if 'door_cell_y' in city.buildings_gdf.columns else cent.y.values
+})
+
+print("Reprojecting trajectories to Web Mercator...")
+t1 = time.time()
+population.reproject_to_mercator(sparse_traj=True, full_traj=True, diaries=True, poi_data=poi_data)
+reproject_time = time.time() - t1
+print(f"Reprojection:       {reproject_time:>6.2f}s")
+
+print("Saving output files...")
+t2 = time.time()
+population.save_pop(
+    sparse_path=OUTPUT_DIR / f"sparse_traj_{BOX_NAME}",
+    full_path=OUTPUT_DIR / f"full_traj_{BOX_NAME}",
+    diaries_path=OUTPUT_DIR / f"diaries_{BOX_NAME}",
+    partition_cols=["date"],
+    fmt='parquet'
+)
+persist_time = time.time() - t2
+print(f"Persistence:        {persist_time:>6.2f}s")
+print("-"*50)
+print(f"Total I/O:          {reproject_time + persist_time:>6.2f}s")
+print("="*50)
+
+# %% [markdown]
+# ## Visualize Full Trajectories
+
+# %%
+print("\n" + "="*50)
+print("VISUALIZATION")
+print("="*50)
+
+# Read full trajectories
+full_traj_df = pd.read_parquet(OUTPUT_DIR / f"full_traj_{BOX_NAME}")
+print(f"Loaded {len(full_traj_df):,} trajectory points for {config['N']} agents")
+
+# Plot with contextily basemap
+fig, ax = plt.subplots(figsize=(12, 10))
+
+# Plot each agent with different color
+for agent_id in full_traj_df['user_id'].unique():
+    agent_traj = full_traj_df[full_traj_df['user_id'] == agent_id]
+    ax.scatter(agent_traj['x'], agent_traj['y'], s=1, alpha=0.5, label=agent_id)
+
+# Add basemap
+cx.add_basemap(ax, crs="EPSG:3857", source=cx.providers.CartoDB.Positron)
 
-box = gpd.GeoSeries(city.boundary_polygon)
-fig, ax = plt.subplots()
-box.plot(ax=ax, facecolor="None")
-cx.add_basemap(ax, crs=box.crs)
+ax.set_xlabel('X (Web Mercator)')
+ax.set_ylabel('Y (Web Mercator)')
+ax.set_title(f'Full Trajectories - {config["N"]} Agents, 7 Days')
+ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', markerscale=10)
+plt.tight_layout()
+plt.savefig(OUTPUT_DIR / f"trajectories_{BOX_NAME}.png", dpi=150, bbox_inches='tight')
+print(f"Saved plot to {OUTPUT_DIR / f'trajectories_{BOX_NAME}.png'}")
 plt.show()
 
 # %%
diff --git a/nomad/city_gen.py b/nomad/city_gen.py
index 78e1240..03646ad 100644
--- a/nomad/city_gen.py
+++ b/nomad/city_gen.py
@@ -511,46 +511,33 @@ def check_adjacent(self, geom1, geom2, graph=None):
 
         raise TypeError("Unsupported types: geom1 must be block tuple/list or shapely geometry; geom2 must be block tuple or shapely geometry")
 
-    def get_street_graph(self):
-        """Build the street graph needed for routing.
-
-        Constructs a grid-adjacency graph where each street block is a node
-        and edges connect cardinally adjacent blocks.
-        """
-        if hasattr(self, 'street_graph') and self.street_graph is not None:
-            return self.street_graph
-
-        # ---------------------------------------------------------------------
-        # Build street block adjacency graph
-        # ---------------------------------------------------------------------
-        # Nodes
-        nodes_df = self.streets_gdf[['coord_x', 'coord_y']].copy()
-
-        # Build edge list via vectorized neighbor matches
+    def _build_adjacency_graph(self, gdf):
+        """Build grid-adjacency graph from GeoDataFrame with coord_x, coord_y."""
+        nodes_df = gdf[['coord_x', 'coord_y']].copy()
         edge_list = []
-        for dx, dy in [(1, 0), (0, 1)]:  # undirected; only positive directions
+        for dx, dy in [(1, 0), (0, 1)]:
             shifted = nodes_df.copy()
             shifted['nx'] = nodes_df['coord_x'] + dx
             shifted['ny'] = nodes_df['coord_y'] + dy
-            # Merge to find neighbor pairs
             merged = shifted.merge(
                 nodes_df.rename(columns={'coord_x': 'nx', 'coord_y': 'ny'}),
                 on=['nx', 'ny'], how='inner'
             )
-
             edge_list.append(
                 merged[['coord_x', 'coord_y', 'nx', 'ny']].rename(columns={'coord_x': 'x', 'coord_y': 'y'})
             )
-
         edges_df = pd.concat(edge_list, ignore_index=True)
-
-        # Create graph
         G = nx.Graph()
         G.add_nodes_from([(int(x), int(y)) for x, y in nodes_df.values])
         edge_list = [((int(r.x), int(r.y)), (int(r.nx), int(r.ny))) for r in edges_df.itertuples(index=False)]
         G.add_edges_from(edge_list, weight=1)
-        self.street_graph = G
-        
+        return G
+
+    def get_street_graph(self):
+        """Build the street graph needed for routing."""
+        if hasattr(self, 'street_graph') and self.street_graph is not None:
+            return self.street_graph
+        self.street_graph = self._build_adjacency_graph(self.streets_gdf)
         return self.street_graph
 
     def compute_shortest_paths(self, callable_only=False):
@@ -1645,6 +1632,59 @@ def get_paths_fast(self, start_coord: tuple, end_coord: tuple):
 
         return path
 
+    def _add_to_adjacency_graph(self, G, coord):
+        """Add node and cardinal edges to existing graph."""
+        G.add_node(coord)
+        x, y = coord
+        for dx, dy in [(1, 0), (-1, 0), (0, 1), (0, -1)]:
+            neighbor = (x + dx, y + dy)
+            if neighbor in G:
+                G.add_edge(coord, neighbor, weight=1)
+
+    def _connect_building_door_to_streets(self, building_id, max_depth=5):
+        """Connect building door to street network by converting blocks along shortest path."""
+        building_row = self.buildings_gdf.loc[building_id]
+        door_x = int(building_row['door_cell_x'])
+        door_y = int(building_row['door_cell_y'])
+        door_coord = (door_x, door_y)
+        
+        # Get blocks within Manhattan distance
+        candidates = [(x, y) for x in range(door_x - max_depth, door_x + max_depth + 1)
+                      for y in range(door_y - max_depth, door_y + max_depth + 1)
+                      if abs(x - door_x) + abs(y - door_y) <= max_depth]
+        
+        non_building = self.blocks_gdf[self.blocks_gdf['kind'] != 'building']
+        nearby = non_building[non_building.index.isin(candidates)]
+        block_graph = self._build_adjacency_graph(nearby)
+        
+        paths = nx.single_source_shortest_path(block_graph, source=door_coord, cutoff=max_depth)
+        
+        for target, path in paths.items():
+            if target != door_coord and target in self.streets_gdf.index:
+                new_coords = [c for c in path if c not in self.streets_gdf.index]
+                
+                for coord in path:
+                    self.blocks_gdf.loc[coord, 'kind'] = 'street'
+                    self.blocks_gdf.loc[coord, 'building_type'] = 'street'
+                
+                if new_coords:
+                    new_gdf = gpd.GeoDataFrame([
+                        {'coord_x': x, 'coord_y': y, 'geometry': box(x, y, x+1, y+1),
+                         'building_type': 'street', 'building_id': None}
+                        for x, y in new_coords
+                    ], geometry='geometry', crs=self.streets_gdf.crs)
+                    new_gdf.set_index(['coord_x', 'coord_y'], inplace=True, drop=False)
+                    new_gdf.index.names = [None, None]
+                    self.streets_gdf = pd.concat([self.streets_gdf, new_gdf])
+                    
+                    if hasattr(self, 'street_graph') and self.street_graph is not None:
+                        for coord in new_coords:
+                            self._add_to_adjacency_graph(self.street_graph, coord)
+                
+                return True
+        
+        return False
+
 
 # =============================================================================
 # RANDOM CITY CLASS
@@ -2008,6 +2048,20 @@ def __init__(self, boundary_polygon, streets_gdf, buildings_gdf, block_side_leng
         
         self.resolve_overlaps = resolve_overlaps
         self._rasterize(verbose=verbose)
+        
+        # Connect buildings with non-street doors
+        non_street_doors = []
+        for building_id in self.buildings_gdf['id']:
+            building_row = self.buildings_gdf.loc[building_id]
+            door_coord = (int(building_row['door_cell_x']), int(building_row['door_cell_y']))
+            if door_coord not in self.streets_gdf.index:
+                non_street_doors.append(building_id)
+        
+        if non_street_doors:
+            if verbose:
+                print(f"  Connecting {len(non_street_doors)} buildings with non-street doors...")
+            for building_id in non_street_doors:
+                self._connect_building_door_to_streets(building_id, max_depth=5)
 
     def _rasterize(self, verbose=True):
         # Assigning block types could arguably be its own method, but keeping it here for now
diff --git a/nomad/tests/test_city_gen.py b/nomad/tests/test_city_gen.py
index 8841ef1..f5250b7 100644
--- a/nomad/tests/test_city_gen.py
+++ b/nomad/tests/test_city_gen.py
@@ -582,3 +582,115 @@ def test_to_file_reverse_affine_transformation(tmp_path):
         f"Area scaling incorrect: {wm_area} vs expected {expected_area}"
 
 
+def test_connect_building_door_success():
+    """Test successful connection of building door to streets within max_depth"""
+    city = City(dimensions=(10, 10))
+    
+    # Add buildings to create an isolated area
+    # Building 1: door at (5,5), footprint at (5,6)
+    city.add_building('home', door=(5, 5), blocks=[(5, 6)])
+    building_id = city.buildings_gdf.iloc[0]['id']
+    
+    # Add park buildings to isolate the corridor
+    city.add_building('park', door=(3, 4), blocks=[(4, 4), (4, 5), (4, 6)])
+    city.add_building('park', door=(7, 4), blocks=[(6, 4), (6, 5), (6, 6)])
+    
+    # Manually create corridor by setting blocks to None (non-street, non-building)
+    # Corridor: (5,3), (5,4), (5,5) leading to street at (5,2)
+    corridor_blocks = [(5, 3), (5, 4), (5, 5)]
+    for coord in corridor_blocks:
+        city.blocks_gdf.loc[coord, 'kind'] = None
+        city.blocks_gdf.loc[coord, 'building_type'] = None
+        city.streets_gdf = city.streets_gdf.drop(coord, errors='ignore')
+    
+    # Call connection function
+    result = city._connect_building_door_to_streets(building_id, max_depth=5)
+    
+    # Assert connection successful
+    assert result == True
+    
+    # Assert corridor blocks are now streets
+    assert city.blocks_gdf.loc[(5, 3), 'kind'] == 'street'
+    assert city.blocks_gdf.loc[(5, 4), 'kind'] == 'street'
+    assert city.blocks_gdf.loc[(5, 5), 'kind'] == 'street'
+    
+    # Assert blocks exist in streets_gdf
+    assert (5, 3) in city.streets_gdf.index
+    assert (5, 4) in city.streets_gdf.index
+    assert (5, 5) in city.streets_gdf.index
+
+
+def test_connect_building_door_path_too_long():
+    """Test failure when path exceeds max_depth"""
+    city = City(dimensions=(15, 15))
+    
+    # Add a building with door at (7, 7)
+    city.add_building('home', door=(7, 7), blocks=[(7, 8)])
+    building_id = city.buildings_gdf.iloc[0]['id']
+    
+    # Create a long corridor by adding buildings on both sides
+    # Force a path length of 7: (7,7) -> (7,6) -> (7,5) -> (7,4) -> (7,3) -> (7,2) -> (7,1) -> (7,0) street
+    for x in [6, 8]:
+        for y in range(1, 8):
+            city.add_building('park', door=(x-1 if x==6 else x+1, y), blocks=[(x, y)])
+    
+    # Set corridor blocks to None  
+    for y in range(1, 8):
+        city.blocks_gdf.loc[(7, y), 'kind'] = None
+        city.blocks_gdf.loc[(7, y), 'building_type'] = None
+        city.streets_gdf = city.streets_gdf.drop((7, y), errors='ignore')
+    
+    # Path length is 7, cutoff is 5 - should fail
+    result = city._connect_building_door_to_streets(building_id, max_depth=5)
+    
+    # Assert connection failed
+    assert result == False
+
+
+def test_connect_building_door_isolated():
+    """Test failure when building door is completely isolated"""
+    city = City(dimensions=(20, 20))
+    city.add_building('home', door=(10, 10), blocks=[(10, 11)])
+    building_id = city.buildings_gdf.iloc[0]['id']
+    
+    for x in range(3, 18):
+        for y in range(3, 18):
+            if not (x == 10 and y == 10):
+                city.blocks_gdf.loc[(x, y), 'kind'] = 'building'
+                city.blocks_gdf.loc[(x, y), 'building_type'] = 'park'
+    
+    city.streets_gdf = city._derive_streets_from_blocks()
+    
+    result = city._connect_building_door_to_streets(building_id, max_depth=5)
+    assert result == False
+
+
+def test_connect_building_door_already_connected():
+    """Test that function returns True if door is already a street"""
+    city = City(dimensions=(10, 10))
+    
+    # Add a building normally (door should be on street by default)
+    city.add_building('home', door=(5, 5), blocks=[(5, 6)])
+    building_id = city.buildings_gdf.iloc[0]['id']
+    
+    # Call connection function - should succeed immediately
+    result = city._connect_building_door_to_streets(building_id, max_depth=5)
+    
+    # Assert connection successful (door already connected)
+    assert result == True
+
+
+def test_rastercity_connects_isolated_doors():
+    """Test that RasterCity automatically connects buildings with non-street doors"""
+    buildings, streets, boundary = _load_fixture()
+    
+    # Create city with rasterization that will produce some non-street doors
+    city = RasterCity(boundary, streets, buildings, block_side_length=15.0, verbose=False)
+    
+    # All building doors must be streets after initialization
+    for building_id in city.buildings_gdf['id']:
+        building_row = city.buildings_gdf.loc[building_id]
+        door_coord = (int(building_row['door_cell_x']), int(building_row['door_cell_y']))
+        assert door_coord in city.streets_gdf.index, f"Building {building_id} door {door_coord} not in streets"
+
+
diff --git a/sandbox/config_medium.json b/sandbox/config_medium.json
new file mode 100644
index 0000000..689f585
--- /dev/null
+++ b/sandbox/config_medium.json
@@ -0,0 +1,27 @@
+{
+  "box_name": "medium",
+  "block_side_length": 10.0,
+  "hub_size": 100,
+  "N": 10,
+  "name_seed": 42,
+  "name_count": 2,
+  "epr_params": {
+    "datetime": "2024-01-01 00:00-05:00",
+    "end_time": "2024-01-08 00:00-05:00",
+    "epr_time_res": 15,
+    "rho": 0.4,
+    "gamma": 0.3,
+    "seed_base": 100
+  },
+  "traj_params": {
+    "dt": 0.5,
+    "seed_base": 200
+  },
+  "sampling_params": {
+    "beta_ping": 5,
+    "beta_durations": null,
+    "beta_start": null,
+    "ha": 0.7666666666666667,
+    "seed_base": 300
+  }
+}
\ No newline at end of file
diff --git a/sandbox/dest_diaries_medium/date=2024-01-01/part-0.parquet b/sandbox/dest_diaries_medium/date=2024-01-01/part-0.parquet
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diff --git a/sandbox/streets_consolidated.graphml b/sandbox/streets_consolidated.graphml
new file mode 100644
index 0000000..b061732
--- /dev/null
+++ b/sandbox/streets_consolidated.graphml
@@ -0,0 +1,21684 @@
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From 9911a9ca52af80cb671dac945d25b6bc6a388418 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 10:54:42 -0500
Subject: [PATCH 11/13] Refactor save_pop to auto-generate date column and
 update synthetic_philadelphia

- Add date column generation in save_pop when partition_cols includes 'date'
- Remove full trajectory persistence (memory optimization)
- Update synthetic_philadelphia to only save/visualize sparse trajectories
- Use from_file for reading parquet data
- Remove timing from I/O operations as requested
---
 .../synthetic_philadelphia.py                 |  35 ++++++++----------
 nomad/traj_gen.py                             |   6 +++
 .../date=1970-01-01/part-0.parquet            | Bin 0 -> 15781 bytes
 .../date=1970-01-01/part-0.parquet            | Bin 0 -> 654553 bytes
 sandbox/sparse_trajectories_medium.png        | Bin 0 -> 1530390 bytes
 5 files changed, 21 insertions(+), 20 deletions(-)
 create mode 100644 sandbox/diaries_medium/date=1970-01-01/part-0.parquet
 create mode 100644 sandbox/sparse_traj_medium/date=1970-01-01/part-0.parquet
 create mode 100644 sandbox/sparse_trajectories_medium.png

diff --git a/examples/research/virtual_philadelphia/synthetic_philadelphia.py b/examples/research/virtual_philadelphia/synthetic_philadelphia.py
index 5530409..cfd8e20 100644
--- a/examples/research/virtual_philadelphia/synthetic_philadelphia.py
+++ b/examples/research/virtual_philadelphia/synthetic_philadelphia.py
@@ -25,10 +25,13 @@
 import geopandas as gpd
 import pandas as pd
 from shapely.geometry import box
+import matplotlib.pyplot as plt
+import contextily as cx
 
 import nomad.map_utils as nm
 from nomad.city_gen import RasterCity
 from nomad.traj_gen import Population
+from nomad.io.base import from_file
 from tqdm import tqdm
 
 # %% [markdown]
@@ -379,45 +382,37 @@ def get_size_mb(obj):
     'y': (city.buildings_gdf['door_cell_y'].astype(float) + 0.5).values if 'door_cell_y' in city.buildings_gdf.columns else cent.y.values
 })
 
-print("Reprojecting trajectories to Web Mercator...")
-t1 = time.time()
-population.reproject_to_mercator(sparse_traj=True, full_traj=True, diaries=True, poi_data=poi_data)
-reproject_time = time.time() - t1
-print(f"Reprojection:       {reproject_time:>6.2f}s")
+print("Reprojecting sparse trajectories to Web Mercator...")
+population.reproject_to_mercator(sparse_traj=True, full_traj=False, diaries=True, poi_data=poi_data)
 
-print("Saving output files...")
-t2 = time.time()
+print("Saving sparse trajectories and diaries...")
 population.save_pop(
     sparse_path=OUTPUT_DIR / f"sparse_traj_{BOX_NAME}",
-    full_path=OUTPUT_DIR / f"full_traj_{BOX_NAME}",
     diaries_path=OUTPUT_DIR / f"diaries_{BOX_NAME}",
     partition_cols=["date"],
     fmt='parquet'
 )
-persist_time = time.time() - t2
-print(f"Persistence:        {persist_time:>6.2f}s")
 print("-"*50)
-print(f"Total I/O:          {reproject_time + persist_time:>6.2f}s")
 print("="*50)
 
 # %% [markdown]
-# ## Visualize Full Trajectories
+# ## Visualize Sparse Trajectories
 
 # %%
 print("\n" + "="*50)
 print("VISUALIZATION")
 print("="*50)
 
-# Read full trajectories
-full_traj_df = pd.read_parquet(OUTPUT_DIR / f"full_traj_{BOX_NAME}")
-print(f"Loaded {len(full_traj_df):,} trajectory points for {config['N']} agents")
+# Read sparse trajectories
+sparse_traj_df = from_file(OUTPUT_DIR / f"sparse_traj_{BOX_NAME}", format="parquet")
+print(f"Loaded {len(sparse_traj_df):,} sparse trajectory points for {config['N']} agents")
 
 # Plot with contextily basemap
 fig, ax = plt.subplots(figsize=(12, 10))
 
 # Plot each agent with different color
-for agent_id in full_traj_df['user_id'].unique():
-    agent_traj = full_traj_df[full_traj_df['user_id'] == agent_id]
+for agent_id in sparse_traj_df['user_id'].unique():
+    agent_traj = sparse_traj_df[sparse_traj_df['user_id'] == agent_id]
     ax.scatter(agent_traj['x'], agent_traj['y'], s=1, alpha=0.5, label=agent_id)
 
 # Add basemap
@@ -425,11 +420,11 @@ def get_size_mb(obj):
 
 ax.set_xlabel('X (Web Mercator)')
 ax.set_ylabel('Y (Web Mercator)')
-ax.set_title(f'Full Trajectories - {config["N"]} Agents, 7 Days')
+ax.set_title(f'Sparse Trajectories - {config["N"]} Agents, 7 Days')
 ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', markerscale=10)
 plt.tight_layout()
-plt.savefig(OUTPUT_DIR / f"trajectories_{BOX_NAME}.png", dpi=150, bbox_inches='tight')
-print(f"Saved plot to {OUTPUT_DIR / f'trajectories_{BOX_NAME}.png'}")
+plt.savefig(OUTPUT_DIR / f"sparse_trajectories_{BOX_NAME}.png", dpi=150, bbox_inches='tight')
+print(f"Saved plot to {OUTPUT_DIR / f'sparse_trajectories_{BOX_NAME}.png'}")
 plt.show()
 
 # %%
diff --git a/nomad/traj_gen.py b/nomad/traj_gen.py
index c7e88f0..8ed580f 100644
--- a/nomad/traj_gen.py
+++ b/nomad/traj_gen.py
@@ -1275,6 +1275,8 @@ def save_pop(self,
         """
         if full_path:
             full_df = pd.concat([agent.trajectory for agent in self.roster.values()], ignore_index=True)
+            if partition_cols and 'date' in partition_cols and 'date' not in full_df.columns:
+                full_df['date'] = pd.to_datetime(full_df['timestamp']).dt.date.astype(str)
             full_df = from_df(full_df, traj_cols=traj_cols, mixed_timezone_behavior=mixed_timezone_behavior)
             to_file(full_df,
                     path=full_path,
@@ -1285,6 +1287,8 @@ def save_pop(self,
     
         if sparse_path:
             sparse_df = pd.concat([agent.sparse_traj for agent in self.roster.values()], ignore_index=True)
+            if partition_cols and 'date' in partition_cols and 'date' not in sparse_df.columns:
+                sparse_df['date'] = pd.to_datetime(sparse_df['timestamp']).dt.date.astype(str)
             sparse_df = from_df(sparse_df, traj_cols=traj_cols, mixed_timezone_behavior=mixed_timezone_behavior)
             to_file(sparse_df,
                     path=sparse_path,
@@ -1296,6 +1300,8 @@ def save_pop(self,
     
         if diaries_path:
             diaries_df = pd.concat([agent.diary for agent in self.roster.values()], ignore_index=True)
+            if partition_cols and 'date' in partition_cols and 'date' not in diaries_df.columns:
+                diaries_df['date'] = pd.to_datetime(diaries_df['timestamp']).dt.date.astype(str)
             diaries_df = from_df(diaries_df, traj_cols=traj_cols, mixed_timezone_behavior=mixed_timezone_behavior)
             to_file(diaries_df,
                     path=diaries_path,
diff --git a/sandbox/diaries_medium/date=1970-01-01/part-0.parquet b/sandbox/diaries_medium/date=1970-01-01/part-0.parquet
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diff --git a/sandbox/sparse_traj_medium/date=1970-01-01/part-0.parquet b/sandbox/sparse_traj_medium/date=1970-01-01/part-0.parquet
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From c514be30cf954838a93c37e855181a7af2bcd310 Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 6 Nov 2025 10:58:16 -0500
Subject: [PATCH 12/13] Add geometry cache to Agent for _sample_step
 optimization

- Add _cached_geom and _cached_geom_id attributes to Agent
- Cache boundary polygons at end of _sample_step (building or path geometry)
- Add diagnostic check at start of _sample_step (no effect yet, just infrastructure)
- Track previous_building_id in _traj_from_dest_diary for future cache key optimization
- Cache IDs: building_id for stays, (start, dest) tuples for path movements
- All tests pass, backward compatible, no performance changes yet
---
 nomad/traj_gen.py | 29 ++++++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/nomad/traj_gen.py b/nomad/traj_gen.py
index 8ed580f..a6f3848 100644
--- a/nomad/traj_gen.py
+++ b/nomad/traj_gen.py
@@ -288,6 +288,10 @@ def __init__(self,
         self.diary = diary if diary is not None else pd.DataFrame(
             columns=['datetime', 'timestamp', 'duration', 'location', 'identifier'])
         self.sparse_traj = None
+        
+        # Geometry cache for _sample_step optimization
+        self._cached_geom = None
+        self._cached_geom_id = None
 
 
     def reset_trajectory(self, trajectory = True, sparse = True, last_ping = True, diary = True):
@@ -363,6 +367,12 @@ def _sample_step(self, start_point, dest_building_id, dt, rng):
             The building ID if the step is a stay, or `None` if the step is a move.
         """
         city = self.city
+        
+        # Check if start_point is in cached geometry (diagnostic only, no effect yet)
+        if self._cached_geom is not None:
+            in_cache = self._cached_geom.contains(Point(start_point))
+            if in_cache:
+                pass  # Could reuse cached geometry here
 
         # Resolve destination building geometry and attributes (strict schema)
         brow = city.buildings_gdf[city.buildings_gdf['id'] == dest_building_id]
@@ -401,6 +411,10 @@ def _sample_step(self, start_point, dest_building_id, dt, rng):
                     coord = rng.normal(loc=start_point_arr, scale=sigma*np.sqrt(dt), size=2)
                     if dest_geom.contains(Point(coord)):
                         break
+            
+            # Cache building geometry for potential reuse
+            self._cached_geom = dest_geom
+            self._cached_geom_id = dest_building_id
             return coord, location
 
         # Otherwise, move along streets toward destination door cell
@@ -451,7 +465,14 @@ def _sample_step(self, start_point, dest_building_id, dt, rng):
 
             if bound_poly.contains(Point(coord)):
                 break
-
+        
+        # Cache path geometry for potential reuse
+        # For path movements, create ID from start and destination
+        if start_info['building_id'] is not None:
+            self._cached_geom_id = (start_info['building_id'], dest_building_id)
+        else:
+            self._cached_geom_id = (start_node, dest_building_id)
+        self._cached_geom = bound_poly
         return coord, location
 
 
@@ -472,6 +493,9 @@ def _traj_from_dest_diary(self, dt, seed=0):
             self.diary = self.diary.iloc[:-1]
 
         tick_secs = int(60*dt)
+        
+        # Track previous building for cache optimization
+        previous_building_id = None
 
         entry_update = []
         for i in range(destination_diary.shape[0]):
@@ -508,6 +532,9 @@ def _traj_from_dest_diary(self, dt, seed=0):
                                      'user_id': self.identifier}
                 else:
                     current_entry['duration'] += 1*dt  # add one tick to the duration
+            
+            # Update previous_building_id after completing this destination segment
+            previous_building_id = building_id
 
         if self.trajectory is None:
             self.trajectory = pd.DataFrame(trajectory_update)

From 0b82208093096b9b82be7a6fe67112abd9c8c21e Mon Sep 17 00:00:00 2001
From: Jorge Francisco Barreras <franciscojosebarreras@gmail.com>
Date: Thu, 13 Nov 2025 10:20:07 -0500
Subject: [PATCH 13/13] clean up of files in preparation for merge with street
 graph branch

---
 NOTEBOOK_MIGRATION_GUIDE.md                   |   144 -
 ROUTING_SCALABILITY_ANALYSIS.md               |   463 -
 SAMPLE_STEP_ANALYSIS.md                       |   360 -
 sandbox/config_medium.json                    |    27 -
 .../date=2024-01-01/part-0.parquet            |   Bin 5354 -> 0 bytes
 .../date=2024-01-02/part-0.parquet            |   Bin 5234 -> 0 bytes
 .../date=2024-01-03/part-0.parquet            |   Bin 5360 -> 0 bytes
 .../date=2024-01-04/part-0.parquet            |   Bin 5365 -> 0 bytes
 .../date=2024-01-05/part-0.parquet            |   Bin 5265 -> 0 bytes
 .../date=2024-01-06/part-0.parquet            |   Bin 5323 -> 0 bytes
 .../date=2024-01-07/part-0.parquet            |   Bin 5452 -> 0 bytes
 .../date=1970-01-01/part-0.parquet            |   Bin 15781 -> 0 bytes
 sandbox/sandbox_data_medium.gpkg              |   Bin 3239936 -> 0 bytes
 .../date=1970-01-01/part-0.parquet            |   Bin 654553 -> 0 bytes
 sandbox/sparse_trajectories_medium.png        |   Bin 1530390 -> 0 bytes
 sandbox/streets_consolidated.graphml          | 21684 ----------------
 16 files changed, 22678 deletions(-)
 delete mode 100644 NOTEBOOK_MIGRATION_GUIDE.md
 delete mode 100644 ROUTING_SCALABILITY_ANALYSIS.md
 delete mode 100644 SAMPLE_STEP_ANALYSIS.md
 delete mode 100644 sandbox/config_medium.json
 delete mode 100644 sandbox/dest_diaries_medium/date=2024-01-01/part-0.parquet
 delete mode 100644 sandbox/dest_diaries_medium/date=2024-01-02/part-0.parquet
 delete mode 100644 sandbox/dest_diaries_medium/date=2024-01-03/part-0.parquet
 delete mode 100644 sandbox/dest_diaries_medium/date=2024-01-04/part-0.parquet
 delete mode 100644 sandbox/dest_diaries_medium/date=2024-01-05/part-0.parquet
 delete mode 100644 sandbox/dest_diaries_medium/date=2024-01-06/part-0.parquet
 delete mode 100644 sandbox/dest_diaries_medium/date=2024-01-07/part-0.parquet
 delete mode 100644 sandbox/diaries_medium/date=1970-01-01/part-0.parquet
 delete mode 100644 sandbox/sandbox_data_medium.gpkg
 delete mode 100644 sandbox/sparse_traj_medium/date=1970-01-01/part-0.parquet
 delete mode 100644 sandbox/sparse_trajectories_medium.png
 delete mode 100644 sandbox/streets_consolidated.graphml

diff --git a/NOTEBOOK_MIGRATION_GUIDE.md b/NOTEBOOK_MIGRATION_GUIDE.md
deleted file mode 100644
index 72d4cf9..0000000
--- a/NOTEBOOK_MIGRATION_GUIDE.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# Notebook Migration Guide: compute_shortest_paths()
-
-## Summary of Changes
-
-The `City` class now requires explicit computation of shortest paths before trajectory generation, mirroring the pattern used for `compute_gravity()`.
-
-### What Changed
-
-**Before**:
-```python
-city = City.from_geopackage('garden-city.gpkg')
-agent.generate_trajectory(...)  # Just worked
-```
-
-**After**:
-```python
-city = City.from_geopackage('garden-city.gpkg')
-city.compute_shortest_paths(callable_only=False)  # Required for small cities
-agent.generate_trajectory(...)  # Now works
-```
-
----
-
-## Notebooks Requiring Updates
-
-### Priority 1: Core Examples (Need Immediate Fixes)
-
-#### 1. `examples/random_city.ipynb`
-**Location**: Line ~105 (after `city_generator.generate_city()`)  
-**Add**:
-```python
-clustered_city.compute_shortest_paths(callable_only=False)
-```
-
-#### 2. `examples/research/robustness-of-algorithms/prescribed_trajectory_exp_1.py`
-**Location**: Line 38 (after `city = cg.City.from_geopackage(city_file)`)  
-**Add**:
-```python
-city.compute_shortest_paths(callable_only=False)
-```
-
-**Location**: Line 145 (after `city = City.from_geopackage(config["city_file"])`)  
-**Add**:
-```python
-city.compute_shortest_paths(callable_only=False)
-```
-
-#### 3. `examples/research/garden-city-paper-code.py` / `.ipynb`
-**Multiple locations** after city loading  
-**Add**:
-```python
-city.compute_shortest_paths(callable_only=False)
-```
-
----
-
-### Priority 2: Research Notebooks (May Be Experimental)
-
-#### 4. `examples/research/virtual_philly.py` / `.ipynb`
-**Check if still used** - May be outdated  
-**Add** after city creation:
-```python
-city.compute_shortest_paths(callable_only=False)
-```
-
-#### 5. `examples/generate_synthetic_trajectories.py` / `.ipynb`
-**Check** if it uses `generate_trajectory()`  
-**Add** if needed:
-```python
-city.compute_shortest_paths(callable_only=False)
-```
-
-#### 6. `examples/research/robustness-of-algos-paper.py` / `.ipynb`
-**Check** if it uses `generate_trajectory()`  
-**Add** if needed:
-```python
-city.compute_shortest_paths(callable_only=False)
-```
-
----
-
-## Why Dense Mode for These Notebooks?
-
-All these notebooks use **small cities** (garden-city.gpkg or RandomCityGenerator with dimensions ~100x100):
-- **Garden city**: ~36 buildings, ~400 street blocks
-- **Random city (101x101)**: ~400 street blocks
-
-Dense storage (`callable_only=False`) is:
-- ✅ **Fast**: O(1) path lookups
-- ✅ **Safe**: Memory footprint negligible for < 10k nodes
-- ✅ **Simple**: No placeholder limitations
-
----
-
-## For Large Cities (Future Work)
-
-For **RasterCity** with large bounding boxes or full Philadelphia:
-```python
-city = RasterCity(boundary, streets, buildings, ...)
-city.get_street_graph()
-city._build_hub_network(hub_size=200)  # Optional: for gravity
-city.compute_shortest_paths(callable_only=True)  # On-demand paths
-```
-
-**Note**: `callable_only=True` currently uses `nx.shortest_path()` on demand, which is a **PLACEHOLDER**. Production use requires hub-based routing implementation.
-
----
-
-## Testing the Changes
-
-### Updated Tests
-- ✅ `test_shortest_path()` in `test_city_gen.py` - Now calls `compute_shortest_paths()`
-
-### Manual Testing Checklist
-1. Run `random_city.ipynb` after adding the line
-2. Run `prescribed_trajectory_exp_1.py` after adding the lines
-3. Verify trajectories generate successfully
-4. Check for no runtime errors related to `shortest_paths`
-
----
-
-## Error Messages to Watch For
-
-### If you forget to call `compute_shortest_paths()`:
-```
-ValueError: Must call compute_shortest_paths() before using get_shortest_path().
-```
-**Fix**: Add `city.compute_shortest_paths(callable_only=False)` after city creation.
-
-### If coordinate is not a street block:
-```
-ValueError: Start coordinate (x, y) must be a street block.
-```
-**This is a different issue** - coordinate validation, not related to the refactor.
-
----
-
-## Next Steps
-
-1. **Fix Priority 1 notebooks** (random_city, prescribed_trajectory_exp_1, garden-city-paper-code)
-2. **Test each notebook** to verify trajectories generate correctly
-3. **Commit fixes** with message: `"fix: add compute_shortest_paths() calls to notebooks"`
-4. **Verify all tests pass** (`pytest nomad/tests/test_city_gen.py::test_shortest_path`)
-
diff --git a/ROUTING_SCALABILITY_ANALYSIS.md b/ROUTING_SCALABILITY_ANALYSIS.md
deleted file mode 100644
index 9d83e55..0000000
--- a/ROUTING_SCALABILITY_ANALYSIS.md
+++ /dev/null
@@ -1,463 +0,0 @@
-# Routing Scalability Crisis: Analysis and Path Forward
-
-## Coding Principles
-
-This codebase adheres to the following principles:
-
-1. **No bloat**: Avoid unnecessary variables, checks, defensive code
-2. **No imports inside functions**: All imports at the beginning of the file
-3. **Efficient operations**: Use vectorized pandas/numpy, avoid loops where possible
-4. **Clear intent**: Self-documenting code
-5. **Consistency**: Follow existing patterns
-6. **No defensive guards that hide bugs**: Let failures surface clearly
-7. **No fallbacks or legacy support**: One clear way to do things
-
----
-
-## The Problem
-
-### Scale of Philadelphia Rasterization
-
-- **Full Philadelphia**: ~2 million street blocks
-- **Large box** (current test): ~19,000 buildings, hundreds of thousands of street blocks
-- **Current implementation**: **Infeasible at scale**
-
-### Two Critical Bottlenecks
-
-#### 1. All-Pairs Shortest Path Computation (Memory)
-
-**Location**: `nomad/city_gen.py:1190`
-
-```python
-city.shortest_paths = dict(nx.all_pairs_shortest_path(G))
-```
-
-**The Crisis**:
-- For **2 million nodes**: ~4 trillion pairs
-- Even storing just paths (not full all-pairs matrix) is **impossible**
-- This line is called in `City.from_geodataframes()` when loading from edges
-- **This was removed** from the main code path but **still exists as a vestige**
-
-**Current Status**: The new `get_shortest_path()` method (lines 1483-1620) **does NOT use** `shortest_paths` dictionary. It uses:
-- Hub-based shortcut network (`_build_hub_network`)
-- On-demand NetworkX shortest path when needed
-- Simple LRU cache (50,000 entries) in `get_distance_fast()`
-
-**The Problem**: Line 1190 still computes this infeasible structure during city loading, even though it's never used!
-
----
-
-#### 2. Geometric Path Construction in `_sample_step` (Performance)
-
-**Location**: `nomad/traj_gen.py:343-455`
-
-**The Expensive Operations** (lines 424-433):
-
-```python
-# 1. Get shortest path (this part is OK with hub network)
-street_path = city.get_shortest_path(start_node, dest_cell)
-
-# 2. Build MultiLineString from path (EXPENSIVE)
-path = [(x + 0.5, y + 0.5) for (x, y) in street_path]
-path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]
-path_ml = MultiLineString([path])
-
-# 3. Union all geometries along path (VERY EXPENSIVE)
-street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
-bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom
-```
-
-**Why This Is Expensive**:
-1. **`MultiLineString` construction**: Creates complex geometric object for every step
-2. **`unary_union` of path geometries**: Merges potentially hundreds of block polygons
-3. **Transformed coordinate system**: Uses `_path_coords()` and `_cartesian_coords()` to map between:
-   - Cartesian (x, y) space
-   - Path-based (distance_along_path, perpendicular_offset) space
-4. **`bound_poly.contains()` check**: Tests if point is inside complex polygon (line 452)
-5. **While loop**: Repeats geometric operations until valid position found
-
-**The Logic** (lines 435-454):
-```python
-# Transform to path coordinates
-path_coord = _path_coords(path_ml, start_point_arr)
-
-heading_drift = 3.33 * dt
-sigma = 0.5 * dt / 1.96
-
-while True:
-    # Step in transformed space
-    step = rng.normal(loc=[heading_drift, 0], scale=sigma * np.sqrt(dt), size=2)
-    path_coord = (path_coord[0] + step[0], 0.7 * path_coord[1] + step[1])
-    
-    if path_coord[0] > path_ml.length:
-        coord = np.array([dest_geom.centroid.x, dest_geom.centroid.y])
-        break
-    
-    # Transform back to cartesian
-    coord = _cartesian_coords(path_ml, *path_coord)
-    
-    # Check if inside bounded corridor
-    if bound_poly.contains(Point(coord)):
-        break
-```
-
-**Purpose**: Agent moves along street path with:
-- Forward drift (`heading_drift`)
-- Random walk in path-perpendicular direction
-- Constrained to stay within street corridor (`bound_poly`)
-
----
-
-## Code References
-
-### City Generation & Routing
-
-#### `city_gen.py:512-552` - `get_street_graph()`
-- Builds NetworkX graph from streets_gdf
-- Creates edges for cardinally adjacent street blocks
-- **Does NOT compute all-pairs shortest paths** (good!)
-- Returns `nx.Graph` object
-
-#### `city_gen.py:1190` - **VESTIGIAL ALL-PAIRS COMPUTATION**
-```python
-city.shortest_paths = dict(nx.all_pairs_shortest_path(G))
-```
-- Called in `from_geodataframes()` when loading from edges
-- **Never used** by any downstream code
-- **Should be removed immediately**
-
-#### `city_gen.py:1483-1620` - `get_shortest_path()`
-- Hub-based routing using shortcut network
-- Falls back to on-demand `nx.shortest_path()` when needed
-- **Scalable approach** (only computes paths as needed)
-- Uses `_build_hub_network()` for efficient routing
-
-#### `city_gen.py:1625-1665` - `get_distance_fast()`
-- Wraps `get_shortest_path()` with simple cache
-- 50,000 entry limit with eviction
-- Returns path length as distance
-- **Already scalable**
-
----
-
-### Trajectory Generation
-
-#### `traj_gen.py:343-455` - `_sample_step()`
-**The Core Simulation Method**
-
-**Input**:
-- `start_point`: Current (x, y) position
-- `dest_building_id`: Target building
-- `dt`: Time step (minutes)
-- `rng`: Random number generator
-
-**Output**:
-- `coord`: New (x, y) position
-- `location`: Building ID if arrived, else None
-
-**Logic Flow**:
-1. **Lines 367-378**: Resolve destination building geometry and door
-2. **Lines 379-404**: If already at destination → stay-within-building dynamics
-3. **Lines 406-421**: If starting in building → plan route from building door
-4. **Lines 423-426**: Get shortest path between doors (uses `get_shortest_path`)
-5. **Lines 428-433**: **EXPENSIVE** - Build `MultiLineString` and `bound_poly`
-6. **Lines 435-454**: **EXPENSIVE** - Transformed coordinate random walk
-
-**The Transformation**:
-- `_path_coords(path_ml, point)` → (distance_along, perpendicular_offset)
-- Random walk in transformed space
-- `_cartesian_coords(path_ml, distance, offset)` → (x, y)
-- Check if inside `bound_poly` corridor
-
----
-
-#### `traj_gen.py:1083-1154` - Coordinate Transformation Helpers
-
-**`_cartesian_coords(multilines, distance, offset, eps=0.001)`**:
-- Converts path-based coords → cartesian (x, y)
-- Finds point on path at `distance`
-- Applies perpendicular `offset`
-- Uses tangent vector estimation with `eps` delta
-
-**`_path_coords(multilines, point, eps=0.001)`**:
-- Converts cartesian (x, y) → path-based coords
-- Projects point onto path using `multilines.project()`
-- Computes perpendicular offset using normal vector
-- Returns (distance_along_path, offset)
-
----
-
-### Tests
-
-#### `test_traj_gen.py` - NO DIRECT TESTS FOR `_sample_step`
-- Tests exist for `generate_trajectory()` and `sample_trajectory()`
-- Tests workflow: destination diary → trajectory → sampling
-- **No unit tests specifically for `_sample_step` geometry**
-- Tests pass for small `RandomCity` (101x101 blocks)
-
----
-
-### Notebooks Using This Code
-
-#### `examples/random_city.ipynb`
-**Currently Works** (small scale):
-```python
-city_generator = RandomCityGenerator(width=101, height=101, 
-                                     street_spacing=5, seed=100)
-clustered_city = city_generator.generate_city()
-
-population = Population(clustered_city)
-population.generate_agents(N=1, seed=100)
-
-agent.generate_trajectory(end_time=pd.Timestamp(2025, 1, 8))
-agent.sample_trajectory(beta_start=300, beta_durations=60, beta_ping=10)
-```
-
-**Scale**: 
-- 101x101 = 10,201 total blocks
-- Street spacing 5 → ~400 street blocks
-- ~20 streets per dimension
-- **Feasible at this scale**
-
-**Challenge**: Doesn't scale to 1,000x1,000 or 2,000x2,000 (Philadelphia scale)
-
----
-
-## The OSM Street Graph Alternative
-
-### What We Have (RasterCity)
-
-**Location**: `city_gen.py:1924-2149` - `RasterCity` class
-
-**Input Data**:
-- `streets_gdf`: Simplified OSM street network (already processed by OSMnx)
-  - Nodes at intersections
-  - Edges are street segments
-  - Already cleaned and consolidated
-- `buildings_gdf`: OSM buildings with types
-
-**Current Transformation**:
-1. Rasterize streets → uniform grid blocks
-2. Buildings → door cells on grid
-3. Routing on **discrete grid** (not original OSM graph)
-
-**The Opportunity**:
-- OSM graph is **already optimal** for routing
-- Nodes ~= intersections (sparse, ~tens of thousands)
-- Edges have real lengths, geometries
-- NetworkX can route efficiently on this graph
-
----
-
-### The Integration Challenge
-
-**Problem**: Two different coordinate systems:
-
-1. **Garden City Grid** (current simulation):
-   - Integer block coordinates (x, y)
-   - Buildings occupy 1x1 blocks
-   - Agents move in continuous (x, y) with noise
-   - All geometry in "garden city units"
-
-2. **OSM Street Network** (reality):
-   - Node IDs (arbitrary integers)
-   - Edge geometries (LineStrings in meters)
-   - Real-world coordinates (Web Mercator)
-   - Irregular graph structure
-
-**Mapping Challenge**:
-- Building doors → nearest OSM nodes?
-- Agent position (x, y) → nearest OSM edge?
-- Path following → interpolation along OSM LineString?
-- How to preserve stochastic movement model?
-
----
-
-## Potential Solutions
-
-### Option 1: Eliminate All-Pairs Shortest Path (Quick Win)
-
-**Action**: Remove line 1190 from `city_gen.py:from_geodataframes()`
-
-**Impact**:
-- Eliminates infeasible memory allocation
-- No functional change (never used)
-- **Immediate fix**
-
----
-
-### Option 2: Simplify `_sample_step` Geometry (Performance)
-
-**Current Overhead**:
-- `unary_union` of path geometries
-- `MultiLineString` construction
-- Transformed coordinate system
-- Polygon containment checks
-
-**Possible Simplification**:
-1. **Skip `bound_poly` construction**: Just trust the path, add noise perpendicular
-2. **Simplified corridor**: Use buffer around path centerline (cheaper than union)
-3. **Direct cartesian movement**: Move along path without coordinate transformation
-4. **Vectorize**: Compute multiple steps at once instead of while loop
-
-**Trade-off**: May lose some realism in movement dynamics
-
----
-
-### Option 3: Hybrid OSM + Grid Routing (Ambitious)
-
-**Concept**:
-- **Long-distance routing**: Use OSM graph (intersection to intersection)
-- **Local movement**: Use grid blocks (within ~5 blocks of current location)
-- **Mapping layer**: Buildings have both door_cell (grid) and nearest_osm_node
-
-**Implementation**:
-1. Each building stores: `door_cell` (grid) + `nearest_osm_node` (OSM graph ID)
-2. `get_shortest_path(start_building, dest_building)`:
-   - Map buildings → OSM nodes
-   - Route on OSM graph (NetworkX, efficient)
-   - Map OSM nodes back to grid cells for final approach
-3. `_sample_step()`:
-   - If far from destination: Use OSM graph segment
-   - If near destination: Use grid-based movement
-
-**Challenges**:
-- Complex coordinate mapping
-- Two different graph representations
-- Need to preserve stochastic model
-- Testing and validation
-
----
-
-### Option 4: Pure OSM Graph Simulation (Radical)
-
-**Concept**: Abandon grid entirely, simulate on OSM graph
-
-**Changes**:
-- Agent position: (edge_id, distance_along_edge)
-- Buildings: Attached to edges
-- Movement: Walk along edges, switch at nodes
-- Coordinate transform: Only for output (export to lat/lon)
-
-**Advantages**:
-- Direct use of OSM graph (no rasterization needed)
-- Inherently scalable
-- Real street network topology
-
-**Challenges**:
-- Complete rewrite of simulation logic
-- How to handle building interiors?
-- Perpendicular noise model doesn't translate
-- Departure from current "garden city" paradigm
-
----
-
-## Recommendations
-
-### Critical Update: The Two Hub Network Systems
-
-**YOU CREATED**: `_build_hub_network()` (lines 557-584) + `compute_gravity()` (lines 695-823)
-- **Purpose**: Compute building-to-building **distances** for gravity model
-- **Storage**: Lean structures (hubs, hub distances, nearby doors)
-- **Used by**: `traj_gen` destination diary generation (gravity-based sampling)
-- **Output**: Dense or callable `self.grav` (gravity values, not paths)
-
-**AI SLOP EXISTS**: `get_shortest_path()` hub routing (lines 1522-1593)  
-- **References**: `_shortcut_hubs`, `_nearest_hub`, `_next_to_hub`, `_hub_next_hop`
-- **Problem**: These attributes are NEVER SET by `_build_hub_network()`!
-- **Reality**: Falls back to line 1588-1593:
-```python
-else:
-    # Fallback: compute on-demand with NetworkX
-    try:
-        path = nx.shortest_path(self.get_street_graph(), start_coord, end_coord)
-    except nx.NetworkXNoPath:
-        return []
-```
-- **Conclusion**: The "hub-based routing" is **fiction**. It's just `nx.shortest_path()` on demand.
-
-### What Actually Happens in Garden City
-
-**From `prescribed_trajectory_exp_1.py`**:
-1. Load city: `City.from_geopackage('garden-city.gpkg')` (line 38, 145)
-2. Generate agents with prescribed destination diary (lines 156-163)
-3. `agent.generate_trajectory(destination_diary=destinations)` (lines 166-170)
-4. Each trajectory step calls `_sample_step()` which calls `city.get_shortest_path()`
-5. `get_shortest_path()` tries to use hub shortcuts (lines 1525-1529), **fails**, falls back to NetworkX (line 1591)
-6. **Result**: Every step computes path on-demand with `nx.shortest_path()`
-7. **Works for garden city** because graph is tiny (~400 streets in 101x101 grid)
-
-### Immediate (This Week)
-
-1. **Remove line 1190** (`all_pairs_shortest_path` in `from_geodataframes`) - Never used
-2. **Remove AI slop hub routing** (lines 1522-1587) - Replace with direct NetworkX call
-3. **Keep your hub network** for gravity computation - That's the real work
-4. **Profile `_sample_step`** on large city to quantify geometry overhead
-5. **Benchmark**: 
-   - Time per `_sample_step` call on RasterCity (LARGE_BOX)
-   - Path length distribution
-   - Geometry operation breakdown (unary_union, MultiLineString, contains)
-
-### Short-term (Next Sprint)
-
-4. **Simplify `_sample_step` geometry**:
-   - Test removing `bound_poly` construction
-   - Use simpler corridor constraint
-   - Measure performance gain vs. realism loss
-
-5. **Optimize coordinate transforms**:
-   - Cache `MultiLineString` objects?
-   - Vectorize transformation functions?
-   - Pre-compute path properties?
-
-### Long-term (Research)
-
-6. **Prototype hybrid OSM + Grid routing**:
-   - Proof-of-concept with small example
-   - Validate trajectory realism
-   - Assess complexity vs. benefit
-
-7. **Investigate pure OSM graph simulation**:
-   - Literature review: How do other models handle this?
-   - Prototype minimal viable implementation
-   - Compare results with current model
-
----
-
-## Open Questions
-
-1. **How much geometric fidelity do we need?**
-   - Is the `bound_poly` corridor constraint essential?
-   - Can we simplify to straight-line paths with noise?
-   
-2. **What is the actual bottleneck?**
-   - Profile needed: Is it geometry or path computation?
-   - How many `_sample_step` calls per agent per day?
-
-3. **Can we vectorize `_sample_step`?**
-   - Generate multiple steps at once?
-   - Pre-compute paths for common routes?
-
-4. **OSM graph integration strategy?**
-   - Should we commit to hybrid or pure OSM?
-   - What is the migration path?
-   - How to maintain backward compatibility?
-
-5. **Testing and validation?**
-   - How to verify trajectory realism after changes?
-   - What metrics matter: path length, dwell time, visit patterns?
-   - Do we need ground truth comparison?
-
----
-
-## Next Steps
-
-**Priority**: **Profile first, optimize second**
-
-1. Run `synthetic_philadelphia.py` with profiling on LARGE_BOX
-2. Measure time spent in `_sample_step` components
-3. Count calls to `get_shortest_path` vs. geometry operations
-4. Make data-driven decisions about where to optimize
-
-**Then**: Choose between simplification (Option 2) vs. hybrid routing (Option 3)
-
diff --git a/SAMPLE_STEP_ANALYSIS.md b/SAMPLE_STEP_ANALYSIS.md
deleted file mode 100644
index 6bde049..0000000
--- a/SAMPLE_STEP_ANALYSIS.md
+++ /dev/null
@@ -1,360 +0,0 @@
-# `_sample_step` Coordinate Transformation Analysis
-
-## Overview
-
-`_sample_step` is called **repeatedly** in a tight loop (`_traj_from_dest_diary`, line 487) to generate individual position pings as an agent moves toward a destination. Each call is **independent** and reconstructs all geometric objects from scratch.
-
----
-
-## The Coordinate Transformation System
-
-### Purpose
-Convert between **Cartesian (x, y)** space and **path-based (distance_along, perpendicular_offset)** space to enable realistic random walks that:
-1. Drift forward along the path (toward destination)
-2. Wiggle perpendicular to the path (crossing the street, lane changes)
-3. Stay within the "street corridor" (not wandering into buildings)
-
----
-
-## Step-by-Step Breakdown
-
-### 1. Path Construction (Lines 424-431) - **RECONSTRUCTED EVERY CALL**
-
-```python
-# Get path as list of block tuples
-street_path = city.get_shortest_path(start_node, dest_cell)  # e.g., [(8,8), (8,9), (8,10)]
-
-# Convert to block centers
-path = [(x + 0.5, y + 0.5) for (x, y) in street_path]  # e.g., [(8.5, 8.5), (8.5, 9.5), ...]
-
-# Prepend start segment (if inside building) and append destination door
-path = start_segment + path + [(dest_door_centroid.x, dest_door_centroid.y)]
-
-# Create MultiLineString - a continuous line through these points
-path_ml = MultiLineString([path])
-```
-
-**Result**: `path_ml` is a **continuous polyline** connecting:
-- Start position (or start building door)
-- Block centers along shortest path
-- Destination door
-
-**Cost**: **EXPENSIVE** - Calls `get_shortest_path()` and constructs `MultiLineString` every iteration
-
----
-
-### 2. Boundary Polygon Construction (Line 432-433) - **RECONSTRUCTED EVERY CALL**
-
-```python
-# Get geometry of each street block along path
-street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
-
-# Union with start building (if applicable)
-bound_poly = unary_union([start_info['geometry'], street_geom]) if start_info['geometry'] is not None else street_geom
-```
-
-**Result**: `bound_poly` is a **merged polygon** representing the allowable movement area:
-- All street blocks along the path
-- Plus start building (if agent is exiting a building)
-
-**Purpose**: Agent can only move within this corridor (checked at line 452)
-
-**Cost**: **VERY EXPENSIVE** - `unary_union()` merges potentially hundreds of block polygons
-
----
-
-### 3. Coordinate Transformation (Lines 436, 450)
-
-#### Forward Transform: Cartesian → Path-based (`_path_coords`)
-
-```python
-# Convert current position to path coordinates
-path_coord = _path_coords(path_ml, start_point_arr)
-# Returns: (distance_along_path, perpendicular_offset)
-```
-
-**How it works** (lines 1117-1154):
-1. `distance = multilines.project(point)` - Find closest point on path, return distance along path
-2. `point_on_path = multilines.interpolate(distance)` - Get that point's coordinates
-3. Compute tangent direction using `distance - eps` 
-4. Rotate tangent 90° to get normal vector
-5. Project `(point - point_on_path)` onto normal to get signed perpendicular offset
-
-**Output**: 
-- `distance`: How far along the path (scalar, in block units)
-- `offset`: How far perpendicular to path (scalar, positive = left, negative = right)
-
-#### Backward Transform: Path-based → Cartesian (`_cartesian_coords`)
-
-```python
-# Convert path coordinates back to cartesian
-coord = _cartesian_coords(path_ml, *path_coord)
-# Input: (distance_along, perpendicular_offset)
-# Returns: (x, y)
-```
-
-**How it works** (lines 1083-1115):
-1. `point_on_path = multilines.interpolate(distance)` - Get point at distance along path
-2. Compute tangent direction using `distance - eps`
-3. Rotate tangent 90° to get normal vector
-4. `cartesian = point_on_path + offset * normal`
-
----
-
-### 4. Random Walk in Path Space (Lines 438-454)
-
-```python
-heading_drift = 3.33 * dt  # Forward bias (moves toward destination)
-sigma = 0.5 * dt / 1.96     # Random walk variance
-
-while True:
-    # Random step in path space
-    step = rng.normal(loc=[heading_drift, 0], scale=sigma * np.sqrt(dt), size=2)
-    
-    # Update path coordinates
-    path_coord = (path_coord[0] + step[0],      # distance_along += drift + noise
-                  0.7 * path_coord[1] + step[1]) # perpendicular *= 0.7 + noise
-    
-    # Check if reached destination
-    if path_coord[0] > path_ml.length:
-        coord = np.array([dest_geom.centroid.x, dest_geom.centroid.y])
-        break
-    
-    # Convert back to cartesian
-    coord = _cartesian_coords(path_ml, *path_coord)
-    
-    # Check if inside allowed corridor
-    if bound_poly.contains(Point(coord)):
-        break  # Valid position found
-```
-
-**Key behaviors**:
-- **Forward drift**: `heading_drift = 3.33 * dt` biases movement toward destination
-- **Perpendicular damping**: `0.7 * path_coord[1]` pulls agent back toward centerline
-- **Rejection sampling**: If `coord` falls outside `bound_poly`, try again
-
----
-
-## Critical Performance Issues
-
-### Issue 1: Polygons Reconstructed Every Iteration ❌
-
-**Current**: Lines 424-433 execute **every time `_sample_step()` is called**
-
-For a 4-hour trajectory with `dt=0.5` minutes:
-- **480 calls** to `_sample_step()`
-- **480 calls** to `get_shortest_path()`
-- **480 calls** to `unary_union()` (merging street geometries)
-- **480 `MultiLineString` constructions**
-
-**Why this is terrible**:
-1. Path between same (start_building, dest_building) pair is recomputed hundreds of times
-2. `unary_union()` is O(n log n) for n polygons
-3. No caching or reuse
-
-### Issue 2: Path Recomputation Between Same Doors
-
-Agent moving from `h-x8-y8` to `w-x15-y10` over 60 minutes (120 steps at `dt=0.5`):
-- **All 120 steps** call `get_shortest_path((8,8), (15,10))`
-- **All 120 steps** build the same `MultiLineString`
-- **All 120 steps** union the same street polygons
-
-**Only variable**: Agent's position along the path (start_point)
-
----
-
-## Your MultiLineString Optimization Proposal
-
-### Key Insight
-**The path between two doors doesn't change during a trip.** 
-
-Only the agent's position along that path changes.
-
-### Proposed Architecture
-
-#### Phase 1: Cache Door-to-Door MultiLineStrings
-
-```python
-# In City class or as separate cache
-self.door_to_door_paths = {}  # Dict or callable
-
-def get_shortest_path_ml(start_door, end_door):
-    """
-    Returns MultiLineString from start_door to end_door.
-    Cached for repeated queries.
-    """
-    if (start_door, end_door) in self.door_to_door_paths:
-        return self.door_to_door_paths[(start_door, end_door)]
-    
-    # Compute once
-    street_path = self.get_shortest_path(start_door, end_door)  # List of blocks
-    path_coords = [(x + 0.5, y + 0.5) for (x, y) in street_path]
-    path_ml = MultiLineString([path_coords])
-    
-    self.door_to_door_paths[(start_door, end_door)] = path_ml
-    return path_ml
-```
-
-**Benefit**: For agent going from home → work → lunch → work → home, only **4 unique paths** are computed (not 480).
-
----
-
-#### Phase 2: Hub-Based MultiLineString Network
-
-Your proposal for sparse hub network:
-
-```python
-# Precomputed at city creation
-self.hub_to_hub_paths = {}  # MultiLineStrings between hub nodes
-
-def get_shortest_path_ml(start_door, end_door):
-    """
-    Returns MultiLineString using hub network for long distances.
-    """
-    # If doors are "close" (within hub radius)
-    if manhattan_distance(start_door, end_door) < threshold:
-        # Direct path
-        return compute_direct_multilinestring(start_door, end_door)
-    
-    # Otherwise: door → nearest_hub → hub_path → nearest_hub → door
-    start_hub = self.nearest_hub[start_door]
-    end_hub = self.nearest_hub[end_door]
-    
-    # Concatenate segments
-    seg1 = door_to_hub_ml(start_door, start_hub)     # Precomputed or on-demand
-    seg2 = self.hub_to_hub_paths[(start_hub, end_hub)]  # Precomputed
-    seg3 = hub_to_door_ml(end_hub, end_door)         # Precomputed or on-demand
-    
-    # Merge into single MultiLineString
-    return merge_multilinestrings([seg1, seg2, seg3])
-```
-
-**Benefit**: 
-- Hub-to-hub paths computed once (e.g., 200×200 = 40k pairs)
-- Door-to-hub paths computed once per door (e.g., 20k doors)
-- Total: ~60k MultiLineStrings for 2M block city
-- vs. recomputing billions of paths on-demand
-
----
-
-## Existing Projection Methods in traj_gen
-
-### Yes, Already Implemented! ✅
-
-The coordinate transformation functions **already do** what you need:
-
-**`_path_coords(multilines, point)`** (line 1117):
-- Projects cartesian point onto MultiLineString
-- Returns (distance_along, perpendicular_offset)
-- Uses `multilines.project()` internally (Shapely's efficient projection)
-
-**`_cartesian_coords(multilines, distance, offset)`** (line 1083):
-- Inverse operation
-- Given distance and offset, returns cartesian (x, y)
-- Uses `multilines.interpolate()` internally
-
-**These are exactly what you need** - they work with **any** MultiLineString, not just the specific path construction.
-
----
-
-## Implementation Strategy
-
-### What Changes in `_sample_step`
-
-**Before** (lines 424-433):
-```python
-street_path = city.get_shortest_path(start_node, dest_cell)  # Returns list
-path = [(x + 0.5, y + 0.5) for (x, y) in street_path]
-path = start_segment + path + [dest_door_centroid]
-path_ml = MultiLineString([path])
-street_geom = unary_union([city.get_block(b)['geometry'] for b in street_path])
-bound_poly = unary_union([start_info['geometry'], street_geom])
-```
-
-**After**:
-```python
-# get_shortest_path now returns MultiLineString (with optional start/end segments)
-path_ml = city.get_shortest_path_ml(start_node, dest_cell, 
-                                     start_point=start_point if in_building else None,
-                                     end_point=dest_door_centroid)
-
-# Optionally also return bound_poly (if needed), or skip it entirely
-bound_poly = city.get_path_corridor(start_node, dest_cell)  # Cached
-```
-
-**Benefit**: One method call, returns cached MultiLineString
-
----
-
-### Alternative: Remove `bound_poly` Check Entirely
-
-**Question**: Is the `bound_poly.contains()` check (line 452) necessary?
-
-**Current purpose**: Prevents agent from wandering outside street corridor
-
-**Alternative**: Trust the path-based coordinate system
-- Perpendicular offset is damped (`0.7 * path_coord[1]`)
-- Drift is forward-biased
-- Agent naturally stays near path
-
-**If removed**:
-- No need for `unary_union()` of street geometries
-- Significant speedup
-- May lose some realism (agents could "cut corners")
-
----
-
-## Summary: Answering Your Questions
-
-### 1. Does traj_gen have MultiLineString projection methods?
-
-**YES**: 
-- `_path_coords(multilines, point)` - cartesian → path-based
-- `_cartesian_coords(multilines, distance, offset)` - path-based → cartesian
-
-These work with **any MultiLineString**, so your optimization is feasible.
-
----
-
-### 2. How do polygons persist?
-
-**THEY DON'T**: 
-- `path_ml` and `bound_poly` are **reconstructed every call**
-- No caching or reuse between calls
-- **Major performance bottleneck**
-
----
-
-### 3. Optimization Path
-
-**Step 1** (Easy): Cache door-to-door MultiLineStrings
-- Modify `get_shortest_path()` to return MultiLineString (or add new method)
-- Add simple dict cache: `{(start_door, end_door): path_ml}`
-- Modify `_sample_step` to use cached MultiLineString
-- **Estimated speedup**: 100-1000x for repeated trips
-
-**Step 2** (Medium): Hub-based MultiLineString network  
-- Precompute hub-to-hub MultiLineStrings at city initialization
-- Compute door-to-hub on first use (or precompute for all doors)
-- Concatenate segments on demand
-- **Estimated speedup**: Scalable to millions of doors
-
-**Step 3** (Optional): Remove `bound_poly` check
-- Simplifies code
-- Further speedup
-- Test if realistic trajectories still generated
-
----
-
-## Recommendation
-
-Your intuition is **spot on**. The current implementation is doing massive redundant work. The MultiLineString approach with hub network caching is the right solution.
-
-**Next steps**:
-1. Implement `compute_shortest_paths_ml(callable_only=True)` - mirrors our current work
-2. Cache hub-to-hub MultiLineStrings
-3. Modify `_sample_step` to accept/use MultiLineString directly
-4. Profile and compare
-
-The coordinate transformation functions (`_path_coords`, `_cartesian_coords`) are already perfect for this - no changes needed there!
-
diff --git a/sandbox/config_medium.json b/sandbox/config_medium.json
deleted file mode 100644
index 689f585..0000000
--- a/sandbox/config_medium.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
-  "box_name": "medium",
-  "block_side_length": 10.0,
-  "hub_size": 100,
-  "N": 10,
-  "name_seed": 42,
-  "name_count": 2,
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diff --git a/sandbox/streets_consolidated.graphml b/sandbox/streets_consolidated.graphml
deleted file mode 100644
index b061732..0000000
--- a/sandbox/streets_consolidated.graphml
+++ /dev/null
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