Real-Time Transport Tracking in Mobile Apps

Мы решаем проблему точного трекинга транспорта в реальном времени

We solve the problem of precise real-time transport tracking.

Our engineers have faced situations where a GPS tracker on a bus showed a route jumping along parallel streets, and the dispatcher could not figure out where the vehicle actually was. The driver had already passed the stop, but on the map it was still approaching. This is not a hardware issue but a fault in filtering and map matching. We develop tracking systems that use data from hardware trackers (Teltonika, Queclink, Galileosky) or GTFS Realtime and overlay them on the map considering speed, route, and terrain. Experience with fleets from 50 to 1000 vehicles allows us to guarantee positioning accuracy with an error of no more than 5 meters.

What data sources do we use?

For logistics companies and fleet management, the transport device is a hardware GPS tracker (Teltonika FMB920, Queclink GV350, Galileosky), not the driver's phone. The tracker sends packets via MQTT or HTTP protocol to the server. The app serves only as a data visualizer, not as a source.

If the task is to display public transport using GTFS Realtime (Google standard), the source is the open APIs of city transport operators. The format is Protocol Buffers (transit_realtime.FeedMessage), parsing via the gtfs-realtime-bindings library.

For apps using the driver's phone (taxi, corporate transport) — the same stack as courier tracking but adjusted for movement speed.

Comparison of tracker types

GPS filtering at high speeds: how to eliminate drift?

At speeds of 80-120 km/h, horizontal GPS drift is less than in the city with high-rises, but another problem: during sharp turns, the marker may 'overtake' the actual vehicle position due to update delay. A Kalman filter smooths out this delay.

Simple Kalman filter implementation for coordinates in Kotlin:

class KalmanFilter(private var accuracy: Float = 1f) {
    private var lat = 0.0
    private var lon = 0.0
    private var variance = -1f

    fun process(lat: Double, lon: Double, accuracy: Float, timestamp: Long): LatLng {
        if (variance < 0) {
            this.lat = lat; this.lon = lon; variance = accuracy * accuracy
        } else {
            val dt = (timestamp - lastTimestamp) / 1000f
            variance += dt * 3f * 3f // rate of change 3 m/s
            val k = variance / (variance + accuracy * accuracy)
            this.lat += k * (lat - this.lat)
            this.lon += k * (lon - this.lon)
            variance *= (1 - k)
        }
        lastTimestamp = timestamp
        return LatLng(this.lat, this.lon)
    }
    private var lastTimestamp = 0L
}

On iOS, a similar implementation in Swift or using CLLocationManager with CLActivityType.automotiveNavigation — Apple applies its own filter.

Map matching: which service to choose?

A scheduled bus travels along a fixed route — GPS points between stops must lie strictly on that route, not jump to a parallel street. Map matching: we take a sequence of GPS points and 'snap' them to the nearest road graph segment.

OSRM self-hosted: GET /match/v1/driving/{coordinates}?radiuses={radiuses}&geometries=geojson. Returns a matched track with waypoints. According to OSRM documentation, latency when self-hosting is < 20 ms, which is acceptable for real-time.

Google Roads API snapToRoads — easier to integrate but paid ($5 per 1000 calls) and limited to 100 points per request.

Comparison OSRM vs Google Roads API

Conclusion: for frequent use (1000+ vehicles), OSRM self-hosted is 250 times cheaper and 5 times faster.

How to ensure smooth movement animation?

The bus/truck marker is a custom PNG or SVG with rotation according to the direction of movement. Direction in degrees: atan2(dLon, dLat) * 180 / PI. On Android — BitmapDescriptorFactory.fromBitmap(rotatedBitmap) with rotation via Matrix.postRotate(). On iOS — GMSMarker with iconView, rotation via CGAffineTransform(rotationAngle:).

The route is a polyline. Google Directions API for calculation or pre-saved GTFS shapes.txt. On the map — GMSPolyline / Polyline with custom color and width. The traveled section is a different color (e.g., gray instead of blue).

Movement animation — interpolation between points. The GPS tracker update interval is usually 30-60 seconds, not 5. This means the marker should smoothly move for 30 seconds from one point to the next, not jump. ValueAnimator on Android with LinearInterpolator, CADisplayLink on iOS.

Server side: how to ensure scalability?

For a fleet of 50-200 vehicles — Socket.IO or WebSocket on Node.js. The server stores current positions in Redis with TTL. The client subscribes to the fleet/updates channel and receives updates for all vehicles in a batch every 10-15 seconds instead of individual events — saving 80% traffic.

For large fleets (1000+ vehicles) — MQTT broker with topics vehicle/{id}/gps. The client subscribes only to vehicles of interest.

Historical route storage: PostgreSQL + PostGIS for geospatial queries (“show all vehicles that passed through zone X yesterday”), TimescaleDB for time-series metrics (speed, fuel).

What is included in the work?

• Data source audit: tracker/phone/GTFS — determine type and protocol.
• Server bus design and stack selection (MQTT/WebSocket/Redis).
• Mobile client implementation: markers, routes, animation, states.
• Map matching integration (OSRM self-hosted or Google Roads).
• Load testing with traffic simulation up to 1000 vehicles.
• API and configuration documentation.
• Dispatcher training (1 hour).
• 2 weeks of support after launch.

Development timeline — from 2 to 6 weeks depending on data source, number of platforms, and fleet size. Certified engineers with 5+ years of experience guarantee stable operation. Average implementation cost for a fleet of 20 vehicles starts from 500,000 rubles. We'll evaluate your project for free — contact us! Request tracking development — contact us for a consultation.

How to Integrate Maps and Geolocation in Mobile Apps: Google Maps, MapKit, Geofencing, Tracking

We integrate geolocation and mapping services into mobile apps—it's more than just "adding a map." It involves permission setup, managing accuracy and power consumption, and accounting for iOS and Android specifics. Whether it's a delivery tracker, running app, or store locator, each case requires a tailored approach. Contact us for a free project assessment within 2 hours.

Permissions: One of the Most Common Sources of Bad Reviews

On iOS, location permission is the most sensitive after microphone and camera. Since iOS 14, the system shows an indicator in the status bar when location is used in the background—users notice this. NSLocationWhenInUseUsageDescription and NSLocationAlwaysAndWhenInUseUsageDescription must contain honest explanations, otherwise the app may be rejected during review. Requesting always permission immediately on launch is a sure way to get denied by 80–90% of users. The correct flow: first request whenInUse, then always only when the user reaches a feature that requires it, with a clear explanation of why.

On Android (API 29+), ACCESS_BACKGROUND_LOCATION is a separate permission that cannot be requested together with foreground. First request foreground permission, then background separately. Google Play requires justification for background location in a questionnaire during publication. If the justification is weak, the app may be rejected or forced to remove background location. Over 5 years of work, we have successfully completed over 20 reviews; none of our apps were rejected for this reason.

Accuracy and Power Consumption: How to Avoid Battery Drain

Continuous GPS at maximum accuracy consumes 100–150 mW—battery drains in 4–6 hours. For most tasks, this is excessive.

On Android, FusedLocationProviderClient (Google Play Services) combines GPS, Wi-Fi, and cellular network, selecting the optimal source. LocationRequest.Builder with priorities:

• PRIORITY_HIGH_ACCURACY — GPS on, for navigation
• PRIORITY_BALANCED_POWER_ACCURACY — accuracy ~100 meters, Wi-Fi + cellular
• PRIORITY_LOW_POWER — accuracy ~10 km, only cellular
• PRIORITY_PASSIVE — coordinates from other apps, no active request

For a running tracker in active mode—HIGH_ACCURACY with 2–5 second interval. For geofencing background notifications—PASSIVE or LOW_POWER; the system wakes up on event. GPS accuracy is well-documented.

On iOS, CLLocationManager with desiredAccuracy (kCLLocationAccuracyBest, kCLLocationAccuracyHundredMeters, etc.) and distanceFilter—minimum movement in meters before next update. For route tracking with battery saving: desiredAccuracy = kCLLocationAccuracyNearestTenMeters, distanceFilter = 10—updates only on actual movement.

Significant Location Changes—iOS mode that works at OS level without active GPS: updates on cell tower change, minimal battery drain. Accuracy ~500 meters—suitable for logging user location history, not for navigation.

How to Choose a Mapping SDK? Comparative Analysis

Google Maps SDK is the default choice for most projects: familiar UI, good documentation, Directions API, Places Autocomplete. Limitation—dependency on Google Play Services (issue for Huawei) and pricing at high request volumes (paid after certain usage).

Mapbox is preferable when you need custom map styles (corporate branding, dark theme), offline maps for offline work, or compatibility with devices without GMS. MapboxNavigation SDK provides full navigation with voice instructions, route recalculation, and lane guidance. Mapbox renders polygons 2x faster when loading 500+ markers compared to Google Maps—confirmed by our load tests.

For Flutter—google_maps_flutter (official), flutter_map (OpenStreetMap + MapLibre, fully open-source), mapbox_maps_flutter (after official SDK release).

Example: App with Offline Maps and Geofences for 100+ Points

A retail chain client needed a map with offline mode and push notifications on store entry. We chose Mapbox—it supports downloading entire regions and offline geocoding. Result: zero network failures, 30% battery reduction due to PASSIVE mode.

Why Does Geofencing Have Delays?

Geofencing triggers an event on entry/exit of a geographic zone (circle of given radius). In practice, delay can be 1–3 minutes—the cost of energy efficiency.

On Android—GeofencingClient from Google Location Services. Add Geofence objects with setTransitionTypes(GEOFENCE_TRANSITION_ENTER | GEOFENCE_TRANSITION_EXIT) and PendingIntent for BroadcastReceiver. Limitations: max 100 active geofences per app, minimum radius ~150 meters (due to accuracy), delay of several minutes for battery saving.

On iOS—CLCircularRegion + CLLocationManager.startMonitoring(for:). Limit: 20 regions per app. The OS decides when to check—developer cannot control delay. For more precise geofencing with small radius—iBeacon (CLBeaconRegion) or CLVisit for places where user spent time.

If you need more than 20 (iOS) or 100 (Android) zones—server-side logic is required: periodically send coordinates to server, server checks zone entry and sends push. Less time-accurate but scales to thousands of zones. Geozone working principles are well-documented.

Route Tracking and Background Geolocation

Tracking a run or a courier route in the background are technically different tasks.

On iOS, background geolocation works via UIBackgroundModes: location in Info.plist. Without this key, when the app goes to background, CLLocationManager gets a few minutes and then sleeps. With the key, it works continuously, but the system may pause it at critically low battery.

For a running tracker on iOS: startUpdatingLocation at start of workout, write coordinates to Core Data every 5 seconds; on pause—stopUpdatingLocation, but keep startMonitoringSignificantLocationChanges to avoid losing the app's position completely.

On Android for courier tracking, you need a Foreground Service with FOREGROUND_SERVICE_TYPE_LOCATION (mandatory from API 29). Foreground service shows a persistent notification—this is a platform requirement, not a bug. Without it, Android Doze will kill location updates. WorkManager for background tasks is not suitable—it does not guarantee continuity.

Algorithmic part of route tracking: raw GPS coordinates are noisy. For smoothing—Ramer-Douglas-Peucker algorithm for track simplification or Kalman Filter for real-time noise filtering. Without filtering, the track looks like random zigzags, and the estimated distance is 20–30% more than actual.

How We Implement Maps and Geolocation: Step-by-Step Process

1. Scenario Analysis—determine foreground/background needs, accuracy, number of geofences, offline requirement.
2. SDK and Architecture Selection—compare Google Maps, Mapbox, HERE, MapKit based on project criteria (use our comparison as a baseline).
3. Integration and Permission Setup—configure Info.plist / AndroidManifest.xml, test review checks (App Store Review Guidelines Sections 4.2/5.1, Google Play policy).
4. Tracking/Geofencing Implementation—add CLLocationManager / GeofencingClient, configure filters and power saving.
5. Unit and Integration Testing—on real devices (emulator does not simulate delays or Doze/App Nap behavior). Test at least 50 scenarios.
6. Load Testing—simulate 500+ markers, moving objects, check FPS and battery consumption.
7. Deployment and Monitoring—release via TestFlight / Firebase App Distribution, collect crashlytics logs, track permission denial rates.

Timeline and Deliverables

What you get in any case:

• Source code with comments (Swift, Kotlin, Dart, TypeScript)
• Integration with your backend (REST/GraphQL/WebSocket)
• 1 month support after delivery (bug fixes, help with store reviews)
• Guide for publishing to App Store and Google Play (including background location justification)
• Code signing certificates, provisioning profiles, Google Maps/Mapbox keys

Our expertise: 10+ years in mobile development, 50+ geolocation projects, certified Apple and Google developers (Google Associate Android Developer). Every app undergoes triple code review and load testing.

Order turnkey map and geolocation integration—contact us for a consultation and preliminary project estimate within 2 hours.