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arXiv · 2306.07456

On the Temporal-spatial Analysis of Estimating Urban Traffic Patterns Via GPS Trace Data of Car-hailing Vehicles

Jiannan Mao·Lan Liu·Hao Huang·Weike Lu·Kaiyu Yang·Tianli Tang·Haotian Shi
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Abstract

Car-hailing services have become a prominent data source for urban traffic studies. Extracting useful information from car-hailing trace data is essential for effective traffic management, while 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 pre-processing car-hailing GPS trace data with operations such as data cutting, map matching, and trace correction, the framework generates tensor matrices that estimate traffic patterns for car-hailing vehicle flow and average road speed. An analysis block based on these matrices examines the relationships and differences between car-hailing vehicles and urban traffic patterns, which have been overlooked in previous research. 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 while car-hailing flow exhibits a 'multi-peak' pattern, fluctuating significantly during holidays and thus generating a hierarchical structure. For spatial analysis, the heat maps generated from the matrices exhibit certain discrepancies, but the spatial distribution of hotspots and vehicle aggregation areas remains similar.

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Jiannan Mao, Lan Liu, Hao Huang, Weike Lu, Kaiyu Yang, Tianli Tang, Haotian Shi. 2023-06-12. On the Temporal-spatial Analysis of Estimating Urban Traffic Patterns Via GPS Trace Data of Car-hailing Vehicles. https://arxiv.org/abs/2306.07456

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