Review and prospect of floating car data research in transportation
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 litera...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-08-01
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| Series: | Journal of Traffic and Transportation Engineering (English ed. Online) |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095756425001102 |
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| _version_ | 1849397962197696512 |
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| author | Chi Zhang Yuming Zhou Min Zhang Bo Wang Yuhan Nie |
| author_facet | Chi Zhang Yuming Zhou Min Zhang Bo Wang Yuhan Nie |
| author_sort | Chi Zhang |
| collection | DOAJ |
| description | 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 positional parameters. With the trend of large-scale Internet-connected vehicle deployment in the future, a massive amount of FCD will be generated, prompting in-depth research on the fusion of heterogeneous data sources, including FCD. Future research could focus on leveraging transformer and graph neural networks to explore spatiotemporal features of data, developing lightweight real-time FCD processing algorithms, and constructing multimodal refined models tailored to specific traffic scenarios. |
| format | Article |
| id | doaj-art-059c44a63b93464dbacc0144df7beed9 |
| institution | Kabale University |
| issn | 2095-7564 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Traffic and Transportation Engineering (English ed. Online) |
| spelling | doaj-art-059c44a63b93464dbacc0144df7beed92025-08-20T03:38:48ZengKeAi Communications Co., Ltd.Journal of Traffic and Transportation Engineering (English ed. Online)2095-75642025-08-0112475277110.1016/j.jtte.2024.09.005Review and prospect of floating car data research in transportationChi Zhang0Yuming Zhou1Min Zhang2Bo Wang3Yuhan Nie4School of Highway, Chang’an University, Xi’an 710064, China; Corresponding author.School of Highway, Chang’an University, Xi’an 710064, ChinaSchool of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaSchool of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaWith 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 positional parameters. With the trend of large-scale Internet-connected vehicle deployment in the future, a massive amount of FCD will be generated, prompting in-depth research on the fusion of heterogeneous data sources, including FCD. Future research could focus on leveraging transformer and graph neural networks to explore spatiotemporal features of data, developing lightweight real-time FCD processing algorithms, and constructing multimodal refined models tailored to specific traffic scenarios.http://www.sciencedirect.com/science/article/pii/S2095756425001102Transportation engineeringFloating car data applicationBibliometric analysisFreewayFloating car proportionSampling frequency |
| spellingShingle | Chi Zhang Yuming Zhou Min Zhang Bo Wang Yuhan Nie Review and prospect of floating car data research in transportation Journal of Traffic and Transportation Engineering (English ed. Online) Transportation engineering Floating car data application Bibliometric analysis Freeway Floating car proportion Sampling frequency |
| title | Review and prospect of floating car data research in transportation |
| title_full | Review and prospect of floating car data research in transportation |
| title_fullStr | Review and prospect of floating car data research in transportation |
| title_full_unstemmed | Review and prospect of floating car data research in transportation |
| title_short | Review and prospect of floating car data research in transportation |
| title_sort | review and prospect of floating car data research in transportation |
| topic | Transportation engineering Floating car data application Bibliometric analysis Freeway Floating car proportion Sampling frequency |
| url | http://www.sciencedirect.com/science/article/pii/S2095756425001102 |
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