How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915399357956096 |
|---|---|
| author | Qian, Kangan Miao, Jinyu Jiao, Xinyu Luo, Ziang Fu, Zheng Shi, Yining Wang, Yunlong Jiang, Kun Yang, Diange |
| author_facet | Qian, Kangan Miao, Jinyu Jiao, Xinyu Luo, Ziang Fu, Zheng Shi, Yining Wang, Yunlong Jiang, Kun Yang, Diange |
| contents | Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analyzing traffic patterns have gained traction due to their applicability to city-scale policy development. However, conventional methodologies often rely on fragmented grid-based techniques, which may overlook critical interdependencies among spatial elements and temporal continuity. These limitations can compromise analytical effectiveness in complex urban environments. To address these challenges, we propose PriorMotion, a data integration framework designed to systematically uncover movement patterns through driving dynamics analysis. Our approach combines multi-scale empirical observations with customized analytical tools to capture evolving spatial-temporal trends in urban traffic. Comprehensive evaluations demonstrate that PriorMotion significantly enhances analytical outcomes, including increased accuracy in traffic pattern analysis, improved adaptability to heterogeneous data environments, and reduced long-term projection errors. Validation confirms its effectiveness for urban infrastructure management applications requiring precise characterization of complex spatial-temporal interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Qian, Kangan Miao, Jinyu Jiao, Xinyu Luo, Ziang Fu, Zheng Shi, Yining Wang, Yunlong Jiang, Kun Yang, Diange Computer Vision and Pattern Recognition Performance Robotics Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analyzing traffic patterns have gained traction due to their applicability to city-scale policy development. However, conventional methodologies often rely on fragmented grid-based techniques, which may overlook critical interdependencies among spatial elements and temporal continuity. These limitations can compromise analytical effectiveness in complex urban environments. To address these challenges, we propose PriorMotion, a data integration framework designed to systematically uncover movement patterns through driving dynamics analysis. Our approach combines multi-scale empirical observations with customized analytical tools to capture evolving spatial-temporal trends in urban traffic. Comprehensive evaluations demonstrate that PriorMotion significantly enhances analytical outcomes, including increased accuracy in traffic pattern analysis, improved adaptability to heterogeneous data environments, and reduced long-term projection errors. Validation confirms its effectiveness for urban infrastructure management applications requiring precise characterization of complex spatial-temporal interactions. |
| title | How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic |
| topic | Computer Vision and Pattern Recognition Performance Robotics |
| url | https://arxiv.org/abs/2412.04020 |