CUMTA GPS Trajectory Analysis — Chennai Transit
Analysis and visualization of GPS trajectory data from Chennai's public transit fleet, producing animated route maps, hexagonal density analysis, and fleet performance dashboards.
Interactive map — scroll and click to explore.
Context
GPS data from public transit vehicles contains rich operational intelligence — route adherence, coverage patterns, frequency distribution, and fleet utilization — that is rarely extracted and visualized in a usable form for planners.
Problem Statement
What do raw GPS trajectories from Chennai's MTC fleet reveal about actual service patterns, route coverage, and operational performance when cleaned, processed, and visualized spatially?
Methodology
GPS data cleaning and validation pipeline. Trajectory mapping with road network alignment. Hexagonal density binning for coverage analysis. Fleet status aggregation for dashboard metrics. Animated visualizations for temporal pattern analysis.
Analysis
Python-based pipeline (GeoPandas, Pandas, Shapely, NumPy, Folium). Multiple visualization types generated: animated trajectories, hexbin density maps, road-aligned animations, vector field visualizations, and MTC fleet dashboard. CMA (Chennai Metropolitan Area) boundary integration for spatial context.
Insights
GPS data reveals significant deviation from scheduled routes in peak hours. Hexbin density maps show coverage concentration in north-south corridors with sparse east-west connectivity. Fleet status data indicates utilization imbalances across depots.
Outcome
Suite of interactive HTML visualizations (animated maps, density heatmaps, fleet dashboard), cleaned GPS dataset, and reusable Python analysis scripts. Analysis feeds into the broader TransitData Hub platform for Chennai.