Estimating fuel station waiting times accurately without camera feeds or manual user entry requires sophisticated spatial telemetry. In this article, we explain how GPS dwell-time processing, geofencing, and noise filtering algorithmically estimate CNG queue conditions.
1. Geofence Polygons & Approach Vectors
Each CNG station in the QuickNG spatial database is defined by multi-stage polygon boundaries:
- Approach Boundary (50–150m): Tracks vehicles decelerating from main road cruising speeds (e.g. 50 km/h) down to queue creep speeds (under 5 km/h).
- Queue Dwell Zone (10–50m): Monitors stop-and-go movements inside the station forecourt line.
- Dispensing Island Zone (0–10m): Measures static zero-velocity dwell time while the nozzle is connected.
- Exit Acceleration Vector: Confirms a vehicle has completed refueling and accelerated back to arterial road speed.
2. Filtering Out Signal Noise
A critical engineering challenge in spatial queue estimation is filtering out non-refueling stops. Vehicles that park nearby, drivers visiting adjacent tea stalls, or vehicles passing slowly on parallel service lanes generate noise.
QuickNG applies velocity pattern matching: a true refueling event requires a distinct sequence of deceleration, micro-advancements inside the queue lane, a 2-to-4 minute static fill phase, and a distinct departure vector.
📡 Telemetry Accuracy Summary
By aggregating multi-point spatial vectors, the system delivers real-time confidence intervals (e.g., "Medium Queue: 6–10 min wait") with high reliability across dense urban networks.