Urban Travel Mobility Exploring With Large-Scale Trajectory Data

Abstract Taxi GPS trajectories data contain massive spatial and temporal information of urban human activity and mobility. Taking taxis as mobile sensors, the information derived from taxi trips benefits the city and transportation planning. The original data used in this study are collected from > 1100 taxi drivers in Harbin city. We firstly divide the city area into 400 different transportation districts and analyze the origin and destination distribution in urban areas on weekdays and weekends. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to cluster pick-up and drop-off locations. Furthermore, four spatial interaction models are calibrated and compared, based on trajectories in the shopping center of Harbin, to study pick-up location searching behavior. By extracting taxi trips from GPS data, travel distance, time and average speed in occupied and nonoccupied status are then used to investigate human mobility. Next, we use the observed OD matrix of the center area in Harbin to model the traffic distribution patterns based on an entropy-maximizing method, and the estimation performance effectiveness is verified in case study. Finally, a dilatation index based on the weighted average distance among trips is applied to analyze the spatial structure of an urban area. Furthermore, hotspots are identified from local density of locations with different thresholds as determined by the Lorenz curve.