tanmay7264 · tanmay7264 · Aug 22, 2021 · Aug 22, 2021 · Aug 22, 2021 · Apr 18, 2026
diff --git a/analyze_commute_od.py b/analyze_commute_od.py
@@ -0,0 +1,169 @@
+"""
+Commute OD (Origin-Destination) Traffic Analysis - Pendeln
+Analyzes commuting flows between zones: volumes, directions, peaks, and top corridors.
+"""
+
+import random
+import math
+from collections import defaultdict
+
+random.seed(42)
+
+# --- Zones (districts) ---
+ZONES = {
+    "Z01": "Mitte",
+    "Z02": "Prenzlauer Berg",
+    "Z03": "Friedrichshain",
+    "Z04": "Kreuzberg",
+    "Z05": "Schöneberg",
+    "Z06": "Tempelhof",
+    "Z07": "Neukölln",
+    "Z08": "Treptow",
+    "Z09": "Pankow",
+    "Z10": "Weissensee",
+}
+
+# Zone centroids (x, y) in km
+CENTROIDS = {
+    "Z01": (0.0,  0.0),
+    "Z02": (1.5,  3.0),
+    "Z03": (3.0,  1.0),
+    "Z04": (1.0, -2.0),
+    "Z05": (-2.0, -1.5),
+    "Z06": (-1.0, -5.0),
+    "Z07": (2.0, -4.0),
+    "Z08": (5.0, -3.0),
+    "Z09": (1.0,  6.0),
+    "Z10": (4.0,  5.0),
+}
+
+# Employment attractiveness (higher = more jobs → more in-commuters)
+ATTRACTIVENESS = {
+    "Z01": 3.0, "Z02": 1.2, "Z03": 1.5, "Z04": 1.4,
+    "Z05": 1.1, "Z06": 0.8, "Z07": 0.9, "Z08": 0.7,
+    "Z09": 1.0, "Z10": 0.6,
+}
+
+ZONE_IDS = list(ZONES.keys())
+
+
+def distance(z1: str, z2: str) -> float:
+    x1, y1 = CENTROIDS[z1]
+    x2, y2 = CENTROIDS[z2]
+    return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+
+
+def gravity_trips(origin: str, dest: str, base: int = 800) -> int:
+    """Gravity model: trips ∝ attractiveness / distance²."""
+    if origin == dest:
+        return 0
+    d = max(distance(origin, dest), 0.5)
+    raw = base * ATTRACTIVENESS[dest] / (d ** 1.8)
+    noise = random.gauss(1.0, 0.12)
+    return max(0, int(raw * noise))
+
+
+# --- Build OD matrix ---
+od_matrix: dict[tuple[str, str], int] = {}
+for o in ZONE_IDS:
+    for d in ZONE_IDS:
+        if o != d:
+            od_matrix[(o, d)] = gravity_trips(o, d)
+
+# --- Summary statistics ---
+total_trips = sum(od_matrix.values())
+num_pairs   = len(od_matrix)
+
+print("=" * 60)
+print("   COMMUTE OD TRAFFIC ANALYSIS – PENDELN")
+print("=" * 60)
+print(f"\nZones analysed : {len(ZONES)}")
+print(f"OD pairs       : {num_pairs}")
+print(f"Total trips    : {total_trips:,}")
+print(f"Avg trips/pair : {total_trips / num_pairs:,.1f}")
+
+# --- Top 10 OD corridors ---
+print("\n--- Top 10 OD Corridors ---")
+top_pairs = sorted(od_matrix.items(), key=lambda x: x[1], reverse=True)[:10]
+print(f"{'Rank':<5} {'Origin':<20} {'Destination':<20} {'Trips':>8}  {'Dist(km)':>9}")
+print("-" * 65)
+for rank, ((o, d), trips) in enumerate(top_pairs, 1):
+    dist = distance(o, d)
+    print(f"{rank:<5} {ZONES[o]:<20} {ZONES[d]:<20} {trips:>8,}  {dist:>9.2f}")
+
+# --- Zone-level totals ---
+departures: dict[str, int] = defaultdict(int)
+arrivals:   dict[str, int] = defaultdict(int)
+
+for (o, d), trips in od_matrix.items():
+    departures[o] += trips
+    arrivals[d]   += trips
+
+print("\n--- Zone Summary (Departures / Arrivals / Net) ---")
+print(f"{'Zone':<20} {'Departures':>12} {'Arrivals':>10} {'Net (Arr-Dep)':>14}")
+print("-" * 58)
+for zid in ZONE_IDS:
+    dep = departures[zid]
+    arr = arrivals[zid]
+    net = arr - dep
+    bar = "▲" if net > 0 else "▼"
+    print(f"{ZONES[zid]:<20} {dep:>12,} {arr:>10,} {net:>+13,} {bar}")
+
+# --- Intra-zone share ---
+intra = sum(gravity_trips(z, z, 800) for z in ZONE_IDS)  # all zero by design
+print(f"\nIntra-zone trips: {intra:,} (internal commutes excluded)")
+
+# --- Distance distribution ---
+dist_buckets: dict[str, int] = defaultdict(int)
+for (o, d), trips in od_matrix.items():
+    d_km = distance(o, d)
+    if d_km < 3:
+        bucket = "< 3 km (short)"
+    elif d_km < 6:
+        bucket = "3–6 km (medium)"
+    else:
+        bucket = "> 6 km (long)"
+    dist_buckets[bucket] += trips
+
+print("\n--- Trips by Distance Band ---")
+for band, count in sorted(dist_buckets.items()):
+    share = 100 * count / total_trips
+    bar   = "█" * int(share / 2)
+    print(f"  {band:<22} {count:>8,}  ({share:5.1f}%)  {bar}")
+
+# --- Directional balance (symmetry) ---
+print("\n--- Directional Balance (top asymmetric pairs) ---")
+asymmetry: list[tuple[float, str, str, int, int]] = []
+checked: set[frozenset] = set()
+for (o, d) in od_matrix:
+    key = frozenset([o, d])
+    if key in checked:
+        continue
+    checked.add(key)
+    fwd = od_matrix.get((o, d), 0)
+    bwd = od_matrix.get((d, o), 0)
+    if fwd + bwd == 0:
+        continue
+    ratio = max(fwd, bwd) / (min(fwd, bwd) + 1)
+    asymmetry.append((ratio, o, d, fwd, bwd))
+
+asymmetry.sort(reverse=True)
+print(f"{'Pair':<35} {'A→B':>8} {'B→A':>8} {'Ratio':>7}")
+print("-" * 60)
+for ratio, o, d, fwd, bwd in asymmetry[:8]:
+    pair = f"{ZONES[o]} ↔ {ZONES[d]}"
+    print(f"{pair:<35} {fwd:>8,} {bwd:>8,} {ratio:>7.2f}x")
+
+# --- Key findings ---
+top_origin = max(departures, key=departures.get)
+top_dest   = max(arrivals,   key=arrivals.get)
+top_od     = top_pairs[0]
+
+print("\n--- Key Findings ---")
+print(f"  Strongest origin zone : {ZONES[top_origin]} ({departures[top_origin]:,} departures)")
+print(f"  Strongest dest. zone  : {ZONES[top_dest]} ({arrivals[top_dest]:,} arrivals)")
+print(f"  Busiest corridor      : {ZONES[top_od[0][0]]} → {ZONES[top_od[0][1]]} ({top_od[1]:,} trips)")
+short_pct = 100 * dist_buckets["< 3 km (short)"] / total_trips
+print(f"  Short-distance share  : {short_pct:.1f}% of all commutes are < 3 km")
+print()
+print("Analysis complete.")
diff --git a/commute_od_report.html b/commute_od_report.html
@@ -0,0 +1,221 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Commute OD Traffic Analysis – Pendeln</title>
+<style>
+  * { box-sizing: border-box; margin: 0; padding: 0; }
+  body { font-family: 'Segoe UI', sans-serif; background: #0f1117; color: #e0e0e0; padding: 24px; }
+  h1 { text-align: center; font-size: 1.6rem; color: #fff; margin-bottom: 4px; }
+  .subtitle { text-align: center; color: #888; font-size: 0.9rem; margin-bottom: 28px; }
+
+  .kpi-grid { display: grid; grid-template-columns: repeat(4, 1fr); gap: 14px; margin-bottom: 28px; }
+  .kpi { background: #1a1d27; border: 1px solid #2a2d3e; border-radius: 10px; padding: 18px 16px; text-align: center; }
+  .kpi .value { font-size: 2rem; font-weight: 700; color: #4f9eff; }
+  .kpi .label { font-size: 0.75rem; color: #888; margin-top: 4px; text-transform: uppercase; letter-spacing: .5px; }
+
+  .grid2 { display: grid; grid-template-columns: 1fr 1fr; gap: 18px; margin-bottom: 18px; }
+  .card { background: #1a1d27; border: 1px solid #2a2d3e; border-radius: 10px; padding: 20px; }
+  .card h2 { font-size: 0.85rem; text-transform: uppercase; letter-spacing: .8px; color: #aaa; margin-bottom: 14px; border-bottom: 1px solid #2a2d3e; padding-bottom: 8px; }
+
+  table { width: 100%; border-collapse: collapse; font-size: 0.85rem; }
+  th { color: #888; font-weight: 600; text-align: left; padding: 6px 8px; border-bottom: 1px solid #2a2d3e; font-size: 0.75rem; text-transform: uppercase; }
+  td { padding: 7px 8px; border-bottom: 1px solid #1f2235; }
+  tr:last-child td { border-bottom: none; }
+  tr:hover td { background: #1f2235; }
+  .rank { color: #555; font-size: 0.8rem; }
+  .num { text-align: right; font-variant-numeric: tabular-nums; }
+  .pos { color: #4ade80; }
+  .neg { color: #f87171; }
+  .arrow-up { color: #4ade80; }
+  .arrow-dn { color: #f87171; }
+
+  .bar-wrap { display: flex; align-items: center; gap: 8px; }
+  .bar-track { flex: 1; background: #0f1117; border-radius: 4px; height: 10px; }
+  .bar-fill { height: 100%; border-radius: 4px; background: linear-gradient(90deg, #4f9eff, #7b5ea7); }
+  .bar-label { font-size: 0.78rem; color: #aaa; width: 48px; text-align: right; }
+
+  .dist-row { margin-bottom: 14px; }
+  .dist-name { font-size: 0.82rem; color: #ccc; margin-bottom: 4px; }
+  .dist-bar-track { width: 100%; background: #0f1117; border-radius: 4px; height: 14px; position: relative; }
+  .dist-bar-fill { height: 100%; border-radius: 4px; }
+  .dist-short { background: linear-gradient(90deg, #4ade80, #22c55e); }
+  .dist-medium { background: linear-gradient(90deg, #4f9eff, #3b82f6); }
+  .dist-long { background: linear-gradient(90deg, #f87171, #ef4444); }
+  .dist-pct { font-size: 0.78rem; color: #aaa; margin-top: 3px; }
+
+  .ratio-badge { display: inline-block; background: #2a1f3d; color: #c084fc; border-radius: 4px; padding: 2px 7px; font-size: 0.78rem; font-weight: 700; }
+
+  @media (max-width: 700px) {
+    .kpi-grid { grid-template-columns: 1fr 1fr; }
+    .grid2 { grid-template-columns: 1fr; }
+  }
+</style>
+</head>
+<body>
+
+<h1>Commute OD Traffic Analysis</h1>
+<p class="subtitle">Pendeln — Origin-Destination flows across 10 city zones &nbsp;|&nbsp; Gravity model</p>
+
+<!-- KPI cards -->
+<div class="kpi-grid">
+  <div class="kpi"><div class="value">10</div><div class="label">Zones</div></div>
+  <div class="kpi"><div class="value">90</div><div class="label">OD Pairs</div></div>
+  <div class="kpi"><div class="value">7,044</div><div class="label">Total Trips</div></div>
+  <div class="kpi"><div class="value">78.3</div><div class="label">Avg Trips / Pair</div></div>
+</div>
+
+<div class="grid2">
+
+  <!-- Top 10 corridors -->
+  <div class="card">
+    <h2>Top 10 OD Corridors</h2>
+    <table>
+      <thead>
+        <tr>
+          <th>#</th>
+          <th>Origin</th>
+          <th>Destination</th>
+          <th class="num">Trips</th>
+          <th class="num">km</th>
+          <th style="width:120px">Volume</th>
+        </tr>
+      </thead>
+      <tbody id="corridors"></tbody>
+    </table>
+  </div>
+
+  <!-- Zone summary -->
+  <div class="card">
+    <h2>Zone Departure / Arrival Balance</h2>
+    <table>
+      <thead>
+        <tr>
+          <th>Zone</th>
+          <th class="num">Dep.</th>
+          <th class="num">Arr.</th>
+          <th class="num">Net</th>
+        </tr>
+      </thead>
+      <tbody id="zones"></tbody>
+    </table>
+  </div>
+
+</div>
+
+<div class="grid2">
+
+  <!-- Distance bands -->
+  <div class="card">
+    <h2>Trips by Distance Band</h2>
+    <div id="dist"></div>
+  </div>
+
+  <!-- Directional balance -->
+  <div class="card">
+    <h2>Directional Asymmetry (top pairs)</h2>
+    <table>
+      <thead>
+        <tr>
+          <th>Pair</th>
+          <th class="num">A→B</th>
+          <th class="num">B→A</th>
+          <th class="num">Ratio</th>
+        </tr>
+      </thead>
+      <tbody id="asym"></tbody>
+    </table>
+  </div>
+
+</div>
+
+<script>
+const corridors = [
+  ["Kreuzberg","Mitte",580,2.24],
+  ["Schöneberg","Mitte",497,2.50],
+  ["Neukölln","Kreuzberg",308,2.24],
+  ["Friedrichshain","Mitte",303,3.16],
+  ["Prenzlauer Berg","Mitte",275,3.35],
+  ["Mitte","Kreuzberg",259,2.24],
+  ["Prenzlauer Berg","Friedrichshain",237,2.50],
+  ["Mitte","Schöneberg",183,2.50],
+  ["Friedrichshain","Prenzlauer Berg",182,2.50],
+  ["Mitte","Friedrichshain",147,3.16],
+];
+const maxTrips = corridors[0][2];
+const tbody = document.getElementById("corridors");
+corridors.forEach(([o,d,t,km],i)=>{
+  const pct = (t/maxTrips*100).toFixed(1);
+  tbody.innerHTML += `<tr>
+    <td class="rank">${i+1}</td>
+    <td>${o}</td>
+    <td>${d}</td>
+    <td class="num">${t.toLocaleString()}</td>
+    <td class="num">${km}</td>
+    <td><div class="bar-wrap"><div class="bar-track"><div class="bar-fill" style="width:${pct}%"></div></div></div></td>
+  </tr>`;
+});
+
+const zones = [
+  ["Mitte",837,2207],
+  ["Prenzlauer Berg",849,670],
+  ["Friedrichshain",809,852],
+  ["Kreuzberg",1102,1126],
+  ["Schöneberg",873,583],
+  ["Tempelhof",494,303],
+  ["Neukölln",792,462],
+  ["Treptow",452,261],
+  ["Pankow",400,351],
+  ["Weissensee",436,229],
+];
+const ztbody = document.getElementById("zones");
+zones.forEach(([z,dep,arr])=>{
+  const net = arr - dep;
+  const cls = net >= 0 ? "pos" : "neg";
+  const arrow = net >= 0 ? "▲" : "▼";
+  ztbody.innerHTML += `<tr>
+    <td>${z}</td>
+    <td class="num">${dep.toLocaleString()}</td>
+    <td class="num">${arr.toLocaleString()}</td>
+    <td class="num ${cls}">${net >= 0 ? "+" : ""}${net.toLocaleString()} ${arrow}</td>
+  </tr>`;
+});
+
+const distData = [
+  ["< 3 km  (short)","dist-short",2361,33.5],
+  ["3–6 km  (medium)","dist-medium",3919,55.6],
+  ["> 6 km  (long)","dist-long",764,10.8],
+];
+const distDiv = document.getElementById("dist");
+distData.forEach(([label,cls,count,pct])=>{
+  distDiv.innerHTML += `<div class="dist-row">
+    <div class="dist-name">${label} &nbsp;<strong>${count.toLocaleString()}</strong> trips</div>
+    <div class="dist-bar-track"><div class="dist-bar-fill ${cls}" style="width:${pct}%"></div></div>
+    <div class="dist-pct">${pct}% of all commutes</div>
+  </div>`;
+});
+
+const asym = [
+  ["Mitte","Weissensee",16,81,4.76],
+  ["Mitte","Tempelhof",33,133,3.91],
+  ["Mitte","Treptow",24,94,3.76],
+  ["Mitte","Neukölln",39,145,3.62],
+  ["Mitte","Pankow",30,99,3.19],
+  ["Mitte","Schöneberg",183,497,2.70],
+  ["Kreuzberg","Neukölln",115,308,2.66],
+  ["Mitte","Prenzlauer Berg",106,275,2.57],
+];
+const atbody = document.getElementById("asym");
+asym.forEach(([a,b,fwd,bwd,ratio])=>{
+  atbody.innerHTML += `<tr>
+    <td>${a} ↔ ${b}</td>
+    <td class="num">${fwd.toLocaleString()}</td>
+    <td class="num">${bwd.toLocaleString()}</td>
+    <td class="num"><span class="ratio-badge">${ratio}x</span></td>
+  </tr>`;
+});
+</script>
+
+</body>
+</html>