GeoJSON Traffic Events API with BBox and Radius Queries

ll table scans) | High (Client-side bbox/radius filtering) | | Proprietary Format + Client Parser | 310–450 | 1,100–1,500 | Medium (Server-side basic filtering) | Medium-High (Format conversion + coordinate math) | | Road511 GeoJSON API (PostGIS + Spatial Index) | 35–65 | 120–210 | High (ST_Intersects, ST_DWithin, ST_Buffer) | Near-zero (Direct FeatureCollection injection) |

Key Findings:

• Native application/geo+json responses eliminate format conversion overhead entirely.
• PostGIS spatial indexes reduce query latency by ~85% compared to unindexed or client-side approaches.
• Standardized FeatureCollections drop directly into mapping libraries without intermediate transformation layers.

Core Solution

Road511 abstracts spatial complexity by exposing standardized GeoJSON endpoints backed by PostGIS. Every list endpoint includes a /geojson variant that returns a standard FeatureCollection with Content-Type: application/geo+json.

GeoJSON Endpoints

GET /api/v1/events/geojson
GET /api/v1/features/geojson?type=cameras
GET /api/v1/truck/corridor/geojson

Bounding Box Query

Map viewport filtering is handled server-side using bbox parameters, preventing over-fetching:

const bounds = map.getBounds();
const bbox = [
  bounds.getWest(), bounds.getSouth(),
  bounds.getEast(), bounds.getNorth()
].join(',');

const res = await fetch(
  `https://api.road511.com/api/v1/events/geojson?bbox=${bbox}&status=active`,
  { headers: { 'X-API-Key': key } }
);
const geojson = await res.json();
L.geoJSON(geojson).addTo(map);

Radius Search

Proximity-based queries leverage geographic distance calculations:

curl "https://api.road511.com/api/v1/events/geojson?lat=34.05&lng=-118.24&radius_km=50&status=active" \
  -H "X-API-Key: your_key"

Multiple Feature Types

Layered mapping is achieved by iterating over feature types and applying type-specific rendering:

const types = ['cameras', 'signs', 'weather_stations', 'rest_areas'];

for (const type of types) {
  const res = await fetch(
    `https://api.road511.com/api/v1/features/geojson?type=${type}&jurisdiction=CA`,
    { headers: { 'X-API-Key': key } }
  );
  const geojson = await res.json();
  L.geoJSON(geojson, {
    pointToLayer: (f, latlng) => L.marker(latlng, { icon: icons[type] })
  }).addTo(layerGroups[type]);
}

LineString Geometry

Non-point features are preserved with native geometry types, enabling accurate corridor and route rendering:

{
  "type": "Feature",
  "geometry": {
    "type": "LineString",
    "coordinates": [[-87.63, 41.88], [-87.62, 41.89], [-87.60, 41.90]]
  },
  "properties": {
    "id": "fhwa-il-staa-001",
    "feature_type": "truck_routes",
    "name": "I-90 STAA National Network"
  }
}

Spatial Queries Under the Hood

The architecture relies on PostGIS for all spatial operations. Every feature includes a geometry column backed by a GiST spatial index. Bounding box queries execute ST_Intersects against the index, while radius searches use ST_DWithin on geography types to account for Earth's curvature. The truck corridor endpoint dynamically constructs a buffer geometry using ST_Buffer and intersects it against all truck-related features, returning optimized LineString and MultiLineString geometries without client-side computation.

Pitfall Guide

1. Coordinate Order Reversal (Lat/Lng vs Lng/Lat): GeoJSON strictly requires [longitude, latitude] order. Mixing up coordinates in bbox or lat/lng parameters causes silent query failures or returns data from entirely different geographic regions. Always validate coordinate order before constructing requests.
2. Ignoring Spatial Indexes & Query Planning: Running unbounded radius queries (e.g., radius_km=500) or omitting bbox filters forces full table scans. Always pair spatial queries with status=active, jurisdiction, or viewport constraints to leverage GiST indexes and maintain sub-100ms latency.
3. Over-fetching with Large Radius Queries: Radius searches return all features within the circle, which can exceed payload limits or rate thresholds in dense urban areas. Implement client-side clustering or paginate results by zoom level to maintain rendering performance.
4. Unhandled Geometry Type Variance: Mapping libraries expect consistent geometry handling. If your pipeline assumes all features are Point types, LineString or Polygon geometries will break rendering or cause silent drops. Use L.geoJSON or equivalent libraries that automatically handle mixed geometry types within a FeatureCollection.
5. Missing Status or Jurisdiction Filters: Traffic data includes historical, cancelled, or cross-jurisdiction events. Failing to filter by status=active or jurisdiction inflates payloads and clutters the map with irrelevant data. Always apply business-logic filters at the API level.
6. Antimeridian & Global BBox Edge Cases: Bounding boxes crossing the 180° meridian or spanning multiple hemispheres can break standard bbox parsing. For global applications, split queries by hemisphere or use radius-based queries with lat/lng centers to avoid coordinate wrap-around failures.

Deliverables

• GeoJSON Traffic Map Integration Blueprint: Step-by-step architectural guide covering endpoint selection, spatial query routing, PostGIS index optimization, and mapping library integration (Leaflet/Mapbox/MapLibre).
• Spatial Query Validation Checklist: Pre-deployment verification matrix including coordinate order validation, index utilization confirmation, payload size thresholds, geometry type handling, and rate-limit configuration.
• Configuration Templates: Ready-to-use API client wrappers, map layer group configurations, and PostGIS spatial index DDL snippets for self-hosted extensions or hybrid deployments.