Review and prospect of floating car data research in transportation

作者：Chi Zhang, Yuming Zhou, Min Zhang, Bo Wang, Yuhan Nie · 发表于：Journal of Traffic and Transportation Engineering (English Edition) · 年份：2025 · DOI：10.1016/j.jtte.2024.09.005 · 被引用次数：4 · 研究领域：Traffic Prediction and Management Techniques、Traffic control and management、Transportation Planning and Optimization

With the advancement of intelligent transportation systems, floating car data (FCD), as a crucial source of transportation information, has garnered increasing attention for its applications and development directions within the context of massive traffic data. This study conducts an in-depth literature review analysis of FCD in the transportation field based on the Web of Science (WOS) database from 2000 to 2023, employing bibliometric methods and knowledge graph technologies. The current research status was visually analyzed through the literature distribution by year, research regions and institutions, research hotspots, and literature clustering using the bibliometric tool CiteSpace. Three major research topics were identified based on the literature clustering analysis. A systematic review of key literature was conducted to address research challenges related to floating car sampling proportions and frequencies, and future research challenges and opportunities were proposed. The results show an overall parabolic increase in publication volume, with research hotspots mainly focusing on mountainous cities, cluster analysis, machine learning, and deep learning. The three major research clusters include traffic flow state, traffic safety, and route planning. The optimal investment proportion for floating cars is determined to be 3%–8%, and the sampling frequency significantly affects the accuracy of vehicle speed and heading angle information, while having a weaker impact on...