Using iterated local alignment to aggregate trajectory data into a traffic flow map
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911305510682624 |
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| author | Duong, Tarn |
| author_facet | Duong, Tarn |
| contents | Vehicle trajectories are a promising GNSS (Global Navigation Satellite System) data source to compute multi-scale traffic flow maps ranging from the city/regional level to the road level. The main obstacle is that trajectory data are prone to measurement noise. While this is negligible for city level, large-scale flow aggregation, it poses substantial difficulties for road level, small-scale aggregation. To overcome these difficulties, we introduce innovative local alignment algorithms, where we infer road segments to serve as local reference segments, and proceed to align nearby road segments to them. We deploy these algorithms in an iterative workflow to compute locally aligned flow maps. By applying this workflow to synthetic and empirical trajectories, we verify that our locally aligned flow maps provide high levels of accuracy and spatial resolution of flow aggregation at multiple scales for static and interactive maps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17500 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using iterated local alignment to aggregate trajectory data into a traffic flow map Duong, Tarn Applications Computational Engineering, Finance, and Science 62P30, 62-08 Vehicle trajectories are a promising GNSS (Global Navigation Satellite System) data source to compute multi-scale traffic flow maps ranging from the city/regional level to the road level. The main obstacle is that trajectory data are prone to measurement noise. While this is negligible for city level, large-scale flow aggregation, it poses substantial difficulties for road level, small-scale aggregation. To overcome these difficulties, we introduce innovative local alignment algorithms, where we infer road segments to serve as local reference segments, and proceed to align nearby road segments to them. We deploy these algorithms in an iterative workflow to compute locally aligned flow maps. By applying this workflow to synthetic and empirical trajectories, we verify that our locally aligned flow maps provide high levels of accuracy and spatial resolution of flow aggregation at multiple scales for static and interactive maps. |
| title | Using iterated local alignment to aggregate trajectory data into a traffic flow map |
| topic | Applications Computational Engineering, Finance, and Science 62P30, 62-08 |
| url | https://arxiv.org/abs/2406.17500 |