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.

翻译：网约车服务已成为城市交通研究的重要数据来源。从网约车轨迹数据中提取有效信息对于交通管理至关重要，同时需考虑网约车与城市交通之间的差异。本文提出一个通用框架，用于基于网约车轨迹数据估计和分析城市交通模式。该框架包含三个层次：数据层、交互软件层和处理方法层。通过对网约车GPS轨迹数据进行数据切割、地图匹配和轨迹校正等预处理操作，该框架生成张量矩阵，以估计网约车流量和平均道路速度的交通模式。基于这些矩阵的分析模块用于研究网约车与城市交通模式之间的关系和差异——这些在以往研究中被忽视。实验结果表明，该框架在分析网约车与城市交通的时空模式方面具有有效性。在时间分析中，城市道路交通呈现双峰特征，而网约车流量呈现"多峰"模式，在节假日期间波动显著并形成层次结构。在空间分析中，由矩阵生成的热力图呈现一定差异，但热点区域和车辆聚集区的空间分布仍保持相似性。