Python · Telematics · GIS

Build mobility pipelines that survive real-world GPS data.

RouteMatching is a production-grade reference for fleet telematics and mobility data engineering in Python. Every page is a focused deep dive — from denoising raw pings and aligning multi-device timestamps, through spatial stop clustering and dwell-time accounting, to probabilistic map matching, speed profiling, and integrating open-source routing engines at scale.

The material is written for mobility engineers, fleet platform developers, and Python GIS practitioners who need to reason through the hard edge cases: signal drop-outs, coordinate system drift, high-frequency outlier bursts, timezone boundary crossings, and heterogeneous OBD-II plus mobile device feeds.

GPS Data Preprocessing Clean raw telematics streams: timestamps, CRS conversion, Kalman smoothing, outlier removal. Stop Detection & Dwell Time DBSCAN clustering, time-window dwell calculation, POI matching, confidence scoring. Trajectory & Map Matching HMM map matching, speed profiling, heading sync, multi-modal fleet routing. Routing Engine Integration Wire OSRM, Valhalla and GraphHopper into Python pipelines: map matching, self-hosting, rate limits.

Start here — featured guides

Kalman Filtering for GPS Noise Reduction Apply a discrete Kalman filter to GPS latitude/longitude sequences in Python to suppress sensor noise without smearing real manoeuvres. DBSCAN for Fleet Stop Clustering Use density-based spatial clustering to separate genuine delivery stops from slow-traffic noise in vehicle telematics data. HMM Map Matching in Python Snap noisy GPS traces to road network edges using a Hidden Markov Model and Viterbi decoding with OSRM as the routing backend. Outlier Removal in Raw Telematics Streams Detect and discard speed-implausible jumps, duplicate pings, and positional spikes before they corrupt downstream metrics. Time-Window Based Dwell Calculation Compute accurate on-site dwell times from clustered stops, handling timezone crossings and overlapping cluster boundaries. Speed Profiling from Raw GPS Coordinates Derive instantaneous and rolling-average speed profiles from lat/lon sequences using the Haversine formula and finite differences. Choosing a Routing Engine: OSRM vs Valhalla vs GraphHopper A decision guide comparing map-matching quality, speed, memory and self-host effort across the three main open-source routing engines. Streaming Map-Matching Pipelines Match GPS to the road network online over a Kafka stream with per-vehicle windowed state and exactly-once emission.

GPS Data Preprocessing & Cleaning Fundamentals

Production-grade preprocessing architecture for cleaning, aligning, and projecting raw telematics into reliable trajectories.

Stop Detection & Dwell Time Analytics

Transform continuous telemetry into discrete, actionable events with spatial clustering, dwell windows, and contextual enrichment.

Trajectory Analysis & Map Matching Techniques

Project noisy GPS sequences onto a road network with probabilistic models, then segment behaviour for downstream analytics.

Routing Engine Integration for Fleet Telematics

Snap traces to the road network and pull distances, ETAs and turn restrictions from open-source routing engines — and decide which one fits your fleet.

What you'll find inside

Every guide pairs the architectural reasoning with concrete Python — vectorised Haversine operations, state-space Kalman estimators, DBSCAN density clustering, Viterbi-decoded HMM map matching — plus the operational realities of scaling telematics pipelines from a few thousand pings to petabyte-class fleets.