Floating car data (FCD), characterized by wide spatiotemporal coverage, low collection cost, and immunity to adverse weather conditions, are one of the key approaches for intelligent transportation systems to obtain real‐time urban road network traffic information. The research aims to utilize GPS data from taxis in Shanghai and vector geographic information data of the road network, with urban expressways as the research focus. Based on the different driving characteristics of expressways and the vehicles on the ramps below, a clustering analysis is employed to determine all floating vehicles traveling on the target road. Furthermore, an adaptive buffer zone consistent with the road orientation is established based on road vector geographic data. This allows for the extraction of FCD within segmented areas, and the average vehicle speed for that road segment is obtained through weighted calculations. This method fully exploits the natural characteristics of taxis in urban areas with a wide spatiotemporal distribution. The data effectiveness and coverage reach 90.2% and 85.7%, respectively, significantly surpassing the traditional grid‐based extraction method for FCD. Additionally, to capture the long‐term spatiotemporal dependencies of road network traffic states, a spatial–temporal autoformer (STAF) network based on spatial–temporal sequence autocorrelation is employed for traffic state prediction. The results indicate that the STAF method demonstrates good performance in medium‐ and long‐term prediction. We believe that the proposed FCD mining method in this paper provides a new approach for efficiently extracting large‐scale road network traffic states and conducting medium‐ to long‐term predictions.

中文翻译：

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基于Autoformer架构的浮动车数据时空数据挖掘的交通状态预测方法

浮动车数据（FCD）具有时空覆盖广、采集成本低、不受恶劣天气影响等特点，是智能交通系统获取实时城市路网交通信息的关键手段之一。本研究旨在利用上海出租车GPS数据和路网矢量地理信息数据，以城市快速路为研究重点。根据高速公路及下方匝道车辆的不同行驶特性，通过聚类分析确定目标道路上行驶的所有浮动车辆。进一步，根据道路矢量地理数据建立与道路方位一致的自适应缓冲区。这样就可以提取分段区域内的FCD，并通过加权计算获得该路段的平均车速。该方法充分利用了城市地区出租车时空分布广泛的自然特征。数据有效性和覆盖率分别达到90.2%和85.7%，明显超越传统的基于网格的FCD提取方法。此外，为了捕获道路网络交通状态的长期时空依赖性，采用基于时空序列自相关的时空自形成器（STAF）网络进行交通状态预测。结果表明，STAF 方法在中长期预测中表现出良好的性能。我们相信本文提出的FCD挖掘方法为有效提取大规模路网交通状态并进行中长期预测提供了一种新方法。