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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2303.15954 |
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| _version_ | 1866916298014851072 |
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| author | Xu, Ming Ai, Qiang Li, Ruimin Ma, Yunyi Qi, Geqi Meng, Xiangfu Jin, Haibo |
| author_facet | Xu, Ming Ai, Qiang Li, Ruimin Ma, Yunyi Qi, Geqi Meng, Xiangfu Jin, Haibo |
| contents | Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control. Although current deep learning methods demonstrate significant advantages in traffic prediction, they are powerless in what-if traffic prediction due to their nature of correla-tion-based. Here, we present a simple deep learning framework called TraffNet that learns the mechanisms of traffic generation for what-if pre-diction from vehicle trajectory data. First, we use a heterogeneous graph to represent the road network, allowing the model to incorporate causal features of traffic flows, such as Origin-Destination (OD) demands and routes. Next, we propose a method for learning segment representations, which models the process of assigning OD demands onto the road network. The learned segment represen-tations effectively encapsulate the intricate causes of traffic generation, facilitating downstream what-if traffic prediction. Finally, we conduct experiments on synthetic datasets to evaluate the effectiveness of TraffNet. The code and datasets of TraffNet is available at https://github.com/iCityLab/TraffNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_15954 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TraffNet: Learning Causality of Traffic Generation for What-if Prediction Xu, Ming Ai, Qiang Li, Ruimin Ma, Yunyi Qi, Geqi Meng, Xiangfu Jin, Haibo Machine Learning Artificial Intelligence Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control. Although current deep learning methods demonstrate significant advantages in traffic prediction, they are powerless in what-if traffic prediction due to their nature of correla-tion-based. Here, we present a simple deep learning framework called TraffNet that learns the mechanisms of traffic generation for what-if pre-diction from vehicle trajectory data. First, we use a heterogeneous graph to represent the road network, allowing the model to incorporate causal features of traffic flows, such as Origin-Destination (OD) demands and routes. Next, we propose a method for learning segment representations, which models the process of assigning OD demands onto the road network. The learned segment represen-tations effectively encapsulate the intricate causes of traffic generation, facilitating downstream what-if traffic prediction. Finally, we conduct experiments on synthetic datasets to evaluate the effectiveness of TraffNet. The code and datasets of TraffNet is available at https://github.com/iCityLab/TraffNet. |
| title | TraffNet: Learning Causality of Traffic Generation for What-if Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2303.15954 |