Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916122210598912 |
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| author | Carneiro, Sarah Almeida Chierchia, Giovanni Pirayre, Aurelie Najman, Laurent |
| author_facet | Carneiro, Sarah Almeida Chierchia, Giovanni Pirayre, Aurelie Najman, Laurent |
| contents | A persistent challenge in the field of Intelligent Transportation Systems is to extract accurate traffic insights from geographic regions with scarce or no data coverage. To this end, we propose solutions for speed prediction using sparse GPS data points and their associated topographical and road design features. Our goal is to investigate whether we can use similarities in the terrain and infrastructure to train a machine learning model that can predict speed in regions where we lack transportation data. For this we create a Temporally Orientated Speed Dictionary Centered on Topographically Clustered Roads, which helps us to provide speed correlations to selected feature configurations. Our results show qualitative and quantitative improvement over new and standard regression methods. The presented framework provides a fresh perspective on devising strategies for missing data traffic analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations Carneiro, Sarah Almeida Chierchia, Giovanni Pirayre, Aurelie Najman, Laurent Artificial Intelligence A persistent challenge in the field of Intelligent Transportation Systems is to extract accurate traffic insights from geographic regions with scarce or no data coverage. To this end, we propose solutions for speed prediction using sparse GPS data points and their associated topographical and road design features. Our goal is to investigate whether we can use similarities in the terrain and infrastructure to train a machine learning model that can predict speed in regions where we lack transportation data. For this we create a Temporally Orientated Speed Dictionary Centered on Topographically Clustered Roads, which helps us to provide speed correlations to selected feature configurations. Our results show qualitative and quantitative improvement over new and standard regression methods. The presented framework provides a fresh perspective on devising strategies for missing data traffic analysis. |
| title | Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2402.07507 |