Estimating Urban Traffic Patterns through GPS Trace Data from Car-Hailing Vehicles: A Temporal–Spatial Analysis

Car-hailing services have become an important data source for urban traffic studies. Extracting useful information from car-hailing trace data is essential for traffic management, whereas discrepancies between car-hailing vehicles and urban traffic should be considered. This paper proposes a generic framework for estimating and analyzing urban traffic patterns using car-hailing trace data. The framework consists of three layers: the data layer, the interactive software layer, and the processing method layer. By preprocessing car-hailing GPS trace data through operations such as data cutting, map matching, and trace correction, the framework generates matrices that estimate traffic patterns for car-hailing vehicle flow and average road speed. An analysis block, built on these matrices, examines the relationships and differences between car-hailing vehicles and urban traffic patterns, which have been overlooked previously. Experimental results demonstrate the effectiveness of the proposed framework in examining temporal-spatial patterns of car-hailing vehicles and urban traffic. For temporal analysis, urban road traffic displays a bimodal characteristic, whereas car-hailing flow exhibits a “multipeak” pattern, fluctuating significantly during holidays and thus creating a hierarchical structure. For spatial analysis, the heat maps generated from the matrices exhibit discrepancies; however, the spatial distribution of hotspots and vehicle aggregation areas remains similar.