GPS Data Preprocessing & Cleaning Fundamentals
Production-grade preprocessing architecture for cleaning, aligning, and projecting raw telematics into reliable trajectories.
Build mobility pipelines that survive real-world GPS data.
RouteMatching is a working library of production-grade techniques for fleet telematics and mobility data processing. Every page is a deep dive — clean noisy GPS traces, align spatiotemporal streams, detect stops and dwell time, reconstruct trajectories, and snap them to a road network using probabilistic map matching.
The material targets mobility engineers, fleet managers, Python GIS developers, and logistics platform builders who have to debug the messy edge cases — signal loss, drift, memory bottlenecks, coordinate system drift, and ingestion at scale.
Pick a pillar below to start. Each one anchors a sub-tree of focused, Python-first guides with copy-pasteable code.
Explore the topics
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.
What you'll find inside
Every guide pairs the architectural reasoning with concrete Python — vectorised operations, state-space estimators, density-based clustering, Viterbi decoding — plus the operational realities of scaling pipelines from a few thousand pings to petabyte-class fleets.