Map‐matching for cycling travel data in urban area
Abstract To promote urban sustainability, many cities are adopting bicycle‐friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map‐matching. Due to GPS device malfunction, road network ambi...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | IET Intelligent Transport Systems |
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| Online Access: | https://doi.org/10.1049/itr2.12567 |
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| author | Ting Gao Winnie Daamen Panchamy Krishnakumari Serge Hoogendoorn |
| author_facet | Ting Gao Winnie Daamen Panchamy Krishnakumari Serge Hoogendoorn |
| author_sort | Ting Gao |
| collection | DOAJ |
| description | Abstract To promote urban sustainability, many cities are adopting bicycle‐friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map‐matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map‐matching methods face challenges in accurately selecting the best‐mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map‐matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi‐objective map‐matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second‐largest city in the Netherlands, is selected as the case study city, and real‐world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high‐traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times. |
| format | Article |
| id | doaj-art-df0d5ebf8b8f4c62a7a2254850131199 |
| institution | OA Journals |
| issn | 1751-956X 1751-9578 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj-art-df0d5ebf8b8f4c62a7a22548501311992025-08-20T02:13:06ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-11-0118112178220310.1049/itr2.12567Map‐matching for cycling travel data in urban areaTing Gao0Winnie Daamen1Panchamy Krishnakumari2Serge Hoogendoorn3Department of Transport & PlanningDelft University of TechnologyDelftThe NetherlandsDepartment of Transport & PlanningDelft University of TechnologyDelftThe NetherlandsDepartment of Transport & PlanningDelft University of TechnologyDelftThe NetherlandsDepartment of Transport & PlanningDelft University of TechnologyDelftThe NetherlandsAbstract To promote urban sustainability, many cities are adopting bicycle‐friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map‐matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map‐matching methods face challenges in accurately selecting the best‐mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map‐matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi‐objective map‐matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second‐largest city in the Netherlands, is selected as the case study city, and real‐world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high‐traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times.https://doi.org/10.1049/itr2.12567bicyclesdata analysismap‐matching |
| spellingShingle | Ting Gao Winnie Daamen Panchamy Krishnakumari Serge Hoogendoorn Map‐matching for cycling travel data in urban area IET Intelligent Transport Systems bicycles data analysis map‐matching |
| title | Map‐matching for cycling travel data in urban area |
| title_full | Map‐matching for cycling travel data in urban area |
| title_fullStr | Map‐matching for cycling travel data in urban area |
| title_full_unstemmed | Map‐matching for cycling travel data in urban area |
| title_short | Map‐matching for cycling travel data in urban area |
| title_sort | map matching for cycling travel data in urban area |
| topic | bicycles data analysis map‐matching |
| url | https://doi.org/10.1049/itr2.12567 |
| work_keys_str_mv | AT tinggao mapmatchingforcyclingtraveldatainurbanarea AT winniedaamen mapmatchingforcyclingtraveldatainurbanarea AT panchamykrishnakumari mapmatchingforcyclingtraveldatainurbanarea AT sergehoogendoorn mapmatchingforcyclingtraveldatainurbanarea |