ルート最適化API: 50行のコードで配送コストを30パーセント削減する

import requests
import json

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.mapatlas.eu/v1"

# Depot (start and end point)
depot = {
    "lat": 52.3402,
    "lng": 4.8952,
    "name": "Warehouse - Sloterdijk"
}

# Delivery stops with optional time windows
stops = [
    { "lat": 52.3726, "lng": 4.8971, "name": "Albert Heijn Jordaan",
      "time_window": { "start": "09:00", "end": "12:00" } },
    { "lat": 52.3601, "lng": 4.9123, "name": "Café De Jaren",
      "time_window": { "start": "08:00", "end": "11:00" } },
    { "lat": 52.3780, "lng": 4.8801, "name": "Westergasfabriek Events",
      "time_window": { "start": "10:00", "end": "14:00" } },
    { "lat": 52.3545, "lng": 4.9041, "name": "Hotel V Nesplein",
      "time_window": None },
    { "lat": 52.3620, "lng": 4.8820, "name": "Vondelpark Paviljoen",
      "time_window": { "start": "07:00", "end": "10:00" } }
]

def optimise_route(depot, stops, vehicle_profile="van-euro6"):
    """
    Request an optimised multi-stop route from the MapAtlas Routing API.
    vehicle_profile options: van-euro6, van-diesel-euro5, electric-van, bike
    """
    waypoints = [
        {
            "lat": s["lat"],
            "lng": s["lng"],
            "name": s["name"],
            **({"time_window": s["time_window"]} if s.get("time_window") else {})
        }
        for s in stops
    ]

    payload = {
        "origin": { "lat": depot["lat"], "lng": depot["lng"] },
        "destination": { "lat": depot["lat"], "lng": depot["lng"] },  # return to depot
        "waypoints": waypoints,
        "optimise": True,
        "vehicle_profile": vehicle_profile,
        "avoid_low_emission_zones": True  # auto-avoids LEZs for non-compliant profiles
    }

    response = requests.post(
        f"{BASE_URL}/routing/optimise",
        json=payload,
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        }
    )
    response.raise_for_status()
    return response.json()

result = optimise_route(depot, stops)

def display_route_summary(result):
    route = result["route"]
    print(f"\n--- Optimised Route Summary ---")
    print(f"Total distance : {route['total_distance_km']:.1f} km")
    print(f"Total duration : {route['total_duration_min']:.0f} min")
    print(f"Stops          : {len(route['waypoints'])}\n")

    print(f"  START  {depot['name']}")
    for i, stop in enumerate(route["waypoints"], 1):
        eta     = stop["eta"]
        tw      = stop.get("time_window")
        on_time = "(on time)" if tw and tw["start"] <= eta <= tw["end"] else ""
        print(f"  {i:>2}.   {stop['name']:<35} ETA {eta}  {on_time}")
    print(f"  END    {depot['name']}")

    print(f"\nEstimated fuel saving vs sequential: "
          f"{result.get('saving_vs_naive_km', 0):.1f} km "
          f"({result.get('saving_pct', 0):.0f}%)")

display_route_summary(result)

--- Optimised Route Summary ---
Total distance : 38.4 km
Total duration : 94 min
Stops          : 5

  START  Warehouse - Sloterdijk
   1.   Vondelpark Paviljoen               ETA 07:48  (on time)
   2.   Café De Jaren                      ETA 08:31  (on time)
   3.   Albert Heijn Jordaan               ETA 09:15  (on time)
   4.   Hotel V Nesplein                   ETA 10:02
   5.   Westergasfabriek Events            ETA 10:44  (on time)
  END    Warehouse - Sloterdijk

Estimated fuel saving vs sequential: 14.2 km (27%)

# Example: diesel Euro 5 van, will be re-routed around Amsterdam ZTL
result_euro5 = optimise_route(depot, stops, vehicle_profile="van-diesel-euro5")

# Example: electric van, LEZ restrictions do not apply
result_electric = optimise_route(depot, stops, vehicle_profile="electric-van")

print(f"Euro 5 route distance  : {result_euro5['route']['total_distance_km']:.1f} km")
print(f"Electric route distance: {result_electric['route']['total_distance_km']:.1f} km")
# Electric route will typically be shorter as it can use LEZ-restricted roads

import mapmetricsgl from '@mapmetrics/mapmetrics-gl';
import '@mapmetrics/mapmetrics-gl/dist/mapmetrics-gl.css';

// routeResult is the parsed API JSON response passed to the frontend
function renderOptimisedRoute(map, routeResult) {
  const { waypoints, geometry, total_distance_km, total_duration_min } = routeResult.route;

  map.on('load', () => {
    // Route line
    map.addSource('optimised-route', { type: 'geojson', data: { type: 'Feature', geometry } });
    map.addLayer({
      id: 'route-line',
      type: 'line',
      source: 'optimised-route',
      layout: { 'line-join': 'round', 'line-cap': 'round' },
      paint: { 'line-color': '#2563EB', 'line-width': 4 }
    });

    // Stop markers with sequence numbers
    waypoints.forEach((stop, i) => {
      const el = document.createElement('div');
      el.textContent = i + 1;
      el.style.cssText = `
        width:28px;height:28px;border-radius:50%;background:#2563EB;color:#fff;
        display:flex;align-items:center;justify-content:center;font-weight:700;
        font-size:13px;border:2px solid #fff;box-shadow:0 2px 6px rgba(0,0,0,0.3)
      `;

      new mapmetricsgl.Marker({ element: el })
        .setLngLat([stop.lng, stop.lat])
        .setPopup(
          new mapmetricsgl.Popup().setHTML(`
            <strong>${i + 1}. ${stop.name}</strong>
            <p>ETA: ${stop.eta}</p>
          `)
        )
        .addTo(map);
    });

    // Fit map to route bounds
    const coords = geometry.coordinates;
    const bounds = coords.reduce(
      (b, c) => b.extend(c),
      new mapmetricsgl.LngLatBounds(coords[0], coords[0])
    );
    map.fitBounds(bounds, { padding: 48 });

    // Summary panel
    document.getElementById('route-summary').innerHTML = `
      <strong>${total_distance_km.toFixed(1)} km</strong> ·
      <strong>${total_duration_min.toFixed(0)} min</strong> ·
      ${waypoints.length} stops
    `;
  });
}

const map = new mapmetricsgl.Map({
  container: 'route-map',
  style: 'https://tiles.mapatlas.eu/styles/basic/style.json?key=YOUR_API_KEY',
  center: [4.9041, 52.3676],
  zoom: 12
});

renderOptimisedRoute(map, routeResult);

def calculate_savings(result, fuel_cost_per_km=0.38, driver_cost_per_hour=22.0,
                       days_per_year=250, fleet_size=10):
    saving_km    = result.get("saving_vs_naive_km", 0)
    saving_hours = saving_km / 50  # assume 50 km/h average

    daily_fuel_saving   = saving_km * fuel_cost_per_km
    daily_driver_saving = saving_hours * driver_cost_per_hour
    daily_total         = daily_fuel_saving + daily_driver_saving

    annual_fleet_saving = daily_total * days_per_year * fleet_size

    print(f"Distance saved per route : {saving_km:.1f} km")
    print(f"Time saved per route     : {saving_hours * 60:.0f} min")
    print(f"Daily saving (1 vehicle) : €{daily_total:.2f}")
    print(f"Annual saving ({fleet_size} vehicles): €{annual_fleet_saving:,.0f}")

calculate_savings(result)

# Time-sensitive stops, the API will schedule these within their windows
stops_with_windows = [
    { "lat": 52.3726, "lng": 4.8971, "name": "Bakery",
      "time_window": { "start": "05:30", "end": "07:00" } },
    { "lat": 52.3620, "lng": 4.8820, "name": "Café",
      "time_window": { "start": "07:00", "end": "09:00" } },
    { "lat": 52.3545, "lng": 4.9041, "name": "Restaurant",
      "time_window": { "start": "13:00", "end": "15:00" } }
]