Mode Detection

Mode detection (also called transportation mode classification) is a machine learning task that assigns a travel mode label to each segment of a GPSGPSThe Global Positioning System (GPS) is a satellite-based navigation system operated by the U.S. Space Force that prov... trajectory based on movement characteristics such as speed, acceleration, heading change rate, and proximity to transit infrastructure.

Classical approaches extract handcrafted features—average speed, maximum speed, acceleration variance, stop frequency—from GPSGPSThe Global Positioning System (GPS) is a satellite-based navigation system operated by the U.S. Space Force that prov... segments and feed them into decision trees, random forests, or support vector machines trained on labeled trajectory data. Walking segments exhibit low speeds and high heading variability; driving shows higher speeds with smooth trajectories; bus travel combines moderate speeds with frequent stops at known bus stop locations. Deep learning approaches use convolutional or recurrent neural networks applied directly to raw coordinate sequences, learning discriminative features automatically. Integration with transit schedule data and road network context improves accuracy for distinguishing bus from car travel, which share similar speed profiles.

Transportation agencies use mode detection to estimate modal split—the share of trips made by each mode—from passively collected mobile data, replacing expensive household travel surveys. Urban planners assess cycling infrastructureCycling InfrastructureCycling Infrastructure encompasses the network of bike lanes, cycle tracks, shared-use paths, and supporting faciliti... utilization. Carbon footprint calculators assign emissions factors based on detected modes. Mobility-as-a-Service platforms use real-time mode detection to provide seamless trip tracking across walking, transit, and ride-hailing legs. Mode detection adds the behavioral dimension to mobility dataMobility DataMobility data consists of anonymized location observations from mobile devices that capture how people move through g..., revealing not just where and when people travel but how they choose to get there.