UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA
User equilibrium (UE) has long been regarded as the cornerstone of transport planning studies. Despite its fundamental importance, our understanding of the actual UE state of road networks has remained surprisingly incomplete. Using big datasets of taxi trajectories, this study investigates the UE s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-12-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/331/2023/isprs-archives-XLVIII-1-W2-2023-331-2023.pdf |
| _version_ | 1827585408132710400 |
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| author | B. Y. Chen X.-Y. Chen H.-P. Chen |
| author_facet | B. Y. Chen X.-Y. Chen H.-P. Chen |
| author_sort | B. Y. Chen |
| collection | DOAJ |
| description | User equilibrium (UE) has long been regarded as the cornerstone of transport planning studies. Despite its fundamental importance, our understanding of the actual UE state of road networks has remained surprisingly incomplete. Using big datasets of taxi trajectories, this study investigates the UE states of road networks in Wuhan. Effective indicators, namely relative gaps, are introduced to quantify how actual traffic states deviate from theoretical UE states. Advanced machine learning techniques, including XGBoost and SHAP values, are employed to analyze nonlinear relationships between network disequilibrium states and seven influencing factors extracted from trajectory data. The results reveal significant gaps between actual traffic states and the theoretical UE states at various times of the day during both weekdays and weekends. The XGBoost analysis shows that differences in travel distances, travel speeds, and signalized intersection numbers among alternative routes are the primary causes of road network disequilibrium. The results of this study could have several important methodological and policy implications for using the UE models in transport applications. |
| first_indexed | 2024-03-08T23:45:07Z |
| format | Article |
| id | doaj.art-d8ebe4d226fe427f81af2d95c01db552 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-03-08T23:45:07Z |
| publishDate | 2023-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-d8ebe4d226fe427f81af2d95c01db5522023-12-14T00:18:19ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-12-01XLVIII-1-W2-202333133710.5194/isprs-archives-XLVIII-1-W2-2023-331-2023UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATAB. Y. Chen0X.-Y. Chen1H.-P. Chen2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaUser equilibrium (UE) has long been regarded as the cornerstone of transport planning studies. Despite its fundamental importance, our understanding of the actual UE state of road networks has remained surprisingly incomplete. Using big datasets of taxi trajectories, this study investigates the UE states of road networks in Wuhan. Effective indicators, namely relative gaps, are introduced to quantify how actual traffic states deviate from theoretical UE states. Advanced machine learning techniques, including XGBoost and SHAP values, are employed to analyze nonlinear relationships between network disequilibrium states and seven influencing factors extracted from trajectory data. The results reveal significant gaps between actual traffic states and the theoretical UE states at various times of the day during both weekdays and weekends. The XGBoost analysis shows that differences in travel distances, travel speeds, and signalized intersection numbers among alternative routes are the primary causes of road network disequilibrium. The results of this study could have several important methodological and policy implications for using the UE models in transport applications.https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/331/2023/isprs-archives-XLVIII-1-W2-2023-331-2023.pdf |
| spellingShingle | B. Y. Chen X.-Y. Chen H.-P. Chen UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA |
| title_full | UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA |
| title_fullStr | UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA |
| title_full_unstemmed | UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA |
| title_short | UNDERSTANDING USER EQUILIBRIUM STATES OF ROAD NETWORKS USING BIG TRAJECTORY DATA |
| title_sort | understanding user equilibrium states of road networks using big trajectory data |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/331/2023/isprs-archives-XLVIII-1-W2-2023-331-2023.pdf |
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