Google Traffic Scraper

Fetch real-time traffic data from Google Maps as GeoJSON — road-level speed levels, congestion status, and road segments for any coordinate and zoom level.

Powered by Google Maps internal tile API. Data is returned as a GeoJSON FeatureCollection with LineString geometries, ready to plug into Mapbox, Leaflet, QGIS, or any GIS tool.

---

Input

{
  "lat": "-6.219320790773214",
  "lon": "106.81002294222947",
  "zoom": 17,
  "radius": 2,
  "seconds": 391039
}

Field	Type	Required	Description
lat	string	✅	Latitude of the center point
lon	string	✅	Longitude of the center point
zoom	int	✅	Map zoom level (10–20). Higher = more detail
radius	int	✅	Tile radius around center. Total tiles = (2r+1)²
seconds	int	❌	Seconds into week (see below). -1 = current time

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zoom — choosing the right level

Zoom	Coverage per tile	Best for
10–12	City-wide	Heatmap, macro overview
13–15	District / kawasan	Area analysis
16–17	Street level	Per-road congestion ✅ recommended
18–20	Lane level	High-detail, niche use cases

Recommended: zoom: 17 for street-level detail with reasonable tile count.

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radius — area coverage

Radius controls how many tiles are fetched around the center point.

Radius	Tiles fetched	Approx. area (zoom 17)
0	1	~300m × 300m
1	9	~900m × 900m
2	25	~1.5km × 1.5km ✅ recommended
3	49	~2.2km × 2.2km
5	121	~3.7km × 3.7km

Higher radius = more HTTP requests = higher cost and longer run time.

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seconds — historical traffic

Pass -1 to get current live traffic.

To fetch traffic for a specific time in the week, convert a datetime to seconds-into-week (Sunday 00:00 UTC = 0):

from datetime import datetime, timezone

def datetime_to_seconds_into_week(dt: datetime) -> int:
    """Convert datetime (aware) → seconds_into_week (Sunday 00:00 UTC = 0)."""
    dt_utc  = dt.astimezone(timezone.utc)
    day_utc = (dt_utc.weekday() + 1) % 7   # Sun=0, Mon=1, ..., Sat=6
    return (day_utc * 86400
            + dt_utc.hour   * 3600
            + dt_utc.minute * 60
            + dt_utc.second)

# Example: Monday 08:00 WIB (UTC+7) → UTC = Monday 01:00
dt = datetime(2024, 1, 8, 8, 0, 0, tzinfo=timezone.utc)  # already UTC here
seconds = datetime_to_seconds_into_week(dt)
# → 86400 + 3600 = 90000  (Monday 01:00 UTC)

Note: Google Maps traffic data is based on historical weekly patterns. The seconds value represents a point in the weekly cycle, not an absolute timestamp — so the same value will return the same traffic pattern every week.

---

Output

Returns a GeoJSON FeatureCollection:

{
  "type": "FeatureCollection",
  "metadata": {
    "center_lat": -6.219320790773214,
    "center_lon": 106.81002294222947,
    "zoom": 17,
    "tile_radius": 2,
    "tiles_fetched": 25,
    "total_features": 487,
    "seconds_in_week": 391039,
    "speed_summary": {
      "Free Flow": 210,
      "Slow Traffic": 180,
      "Heavy Congestion": 97
    }
  },
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "LineString",
        "coordinates": [
          [106.8100123, -6.2193456],
          [106.8101234, -6.2194567]
        ]
      },
      "properties": {
        "speed_level": 2,
        "speed_label": "Slow Traffic",
        "color": "#FF8C00",
        "road_class": 32,
        "stroke_weight": 5,
        "seconds_in_week": 391039
      }
    }
  ]
}

Speed levels

speed_level	Label	Color
1	Free Flow	#2DB82D 🟢
2	Slow Traffic	#FF8C00 🟠
3	Moderate Congestion	#FF4500 🔴
4	Heavy Congestion	#CC0000 🔴
6	Road Closed	#000000 ⚫

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Pricing

This Actor uses Pay Per Event billing — you only pay for what you use.

Event	Price	Description
apify-actor-start	$0.00005	Per run start
tile-fetched	$0.010	Per tile fetched from Google

Cost is fully predictable before you run — it depends only on the radius you choose:

Radius	Tiles	Cost per request
0	1	~$0.01
1	9	~$0.09
2	25	~$0.25 ✅
3	49	~$0.49
5	121	~$1.21

No surprise charges — tile count is always (2 × radius + 1)², known upfront.

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Use cases

• Logistics & delivery — route optimization based on real congestion data
• Urban planning — traffic pattern analysis by time of day / day of week
• Real estate — congestion scoring for property valuation
• Retail analytics — foot traffic and accessibility scoring near stores
• Insurance — road risk scoring by location and time

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Notes

• Data reflects Google Maps traffic patterns and is updated periodically by Google.
• Historical data (seconds parameter) is based on weekly recurring patterns, not archived snapshots.
• Higher zoom levels and larger radius values increase both cost and run time proportionally.