Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach
The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmente...
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MDPI AG
2025-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/6/234 |
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| author | Fengwei Jiao Longgang Xiang Yuanyuan Deng |
| author_facet | Fengwei Jiao Longgang Xiang Yuanyuan Deng |
| author_sort | Fengwei Jiao |
| collection | DOAJ |
| description | The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of overlapping roads as intersections. To address these limitations, we propose a forward and reverse tracking method for high-accuracy road interchange network generation. First, raw crowdsourced trajectory data is preprocessed by filtering out non-interchange trajectories and removing abnormal data based on both static and dynamic characteristics of the trajectories. Next, road subgraphs are extracted by identifying potential transition nodes, which are verified using directional and distribution information. Trajectory bifurcation is then performed at these nodes. Finally, a two-stage fusion process combines forward and reverse tracking results to produce a geometrically complete and topologically accurate road interchange network. Experiments using crowdsourced trajectory data from Shenzhen demonstrated highly accurate results, with 95.26% precision in geometric road network alignment and 90.06% accuracy in representing the connectivity of road interchange structures. Compared to existing methods, our approach enhanced accuracy in spatial alignment by 13.3% and improved the correctness of structural connections by 12.1%. The approach demonstrates strong performance across different types of interchanges, including cloverleaf, turbo, and trumpet interchanges. |
| format | Article |
| id | doaj-art-ac76fa2f1edc4c8eaa02fcf83b62e4fc |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-ac76fa2f1edc4c8eaa02fcf83b62e4fc2025-08-20T03:27:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-06-0114623410.3390/ijgi14060234Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking ApproachFengwei Jiao0Longgang Xiang1Yuanyuan Deng2State Key Laboratory of LIESMARS, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory of LIESMARS, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory of LIESMARS, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaThe generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of overlapping roads as intersections. To address these limitations, we propose a forward and reverse tracking method for high-accuracy road interchange network generation. First, raw crowdsourced trajectory data is preprocessed by filtering out non-interchange trajectories and removing abnormal data based on both static and dynamic characteristics of the trajectories. Next, road subgraphs are extracted by identifying potential transition nodes, which are verified using directional and distribution information. Trajectory bifurcation is then performed at these nodes. Finally, a two-stage fusion process combines forward and reverse tracking results to produce a geometrically complete and topologically accurate road interchange network. Experiments using crowdsourced trajectory data from Shenzhen demonstrated highly accurate results, with 95.26% precision in geometric road network alignment and 90.06% accuracy in representing the connectivity of road interchange structures. Compared to existing methods, our approach enhanced accuracy in spatial alignment by 13.3% and improved the correctness of structural connections by 12.1%. The approach demonstrates strong performance across different types of interchanges, including cloverleaf, turbo, and trumpet interchanges.https://www.mdpi.com/2220-9964/14/6/234road interchangeforward and reverse trackingtransition nodetrajectory continuitycrowdsourced trajectory data |
| spellingShingle | Fengwei Jiao Longgang Xiang Yuanyuan Deng Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach ISPRS International Journal of Geo-Information road interchange forward and reverse tracking transition node trajectory continuity crowdsourced trajectory data |
| title | Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach |
| title_full | Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach |
| title_fullStr | Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach |
| title_full_unstemmed | Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach |
| title_short | Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach |
| title_sort | automatic extraction of road interchange networks from crowdsourced trajectory data a forward and reverse tracking approach |
| topic | road interchange forward and reverse tracking transition node trajectory continuity crowdsourced trajectory data |
| url | https://www.mdpi.com/2220-9964/14/6/234 |
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