Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping (DSAA 2022)
Paper Slides
Bodong Zhou*, Jiahui Liu*, Songyi Cui, Yaping Zhao
(* denotes equal contribution)
- [Comming soon] The code will be released soon.
- [2022-08] The paper is accepted at DSAA 2022.
With the progress of the urbanisation process, the urban transportation system is extremely critical to the development of cities and the quality of life of the citizens. Among them, it is one of the most important tasks to judge traffic congestion by analysing the congestion factors. Recently, various traditional and machine-learning-based models have been introduced for predicting traffic congestion. However, these models are either poorly aggregated for massive congestion factors or fail to make accurate predictions for every precise location in large-scale space. To alleviate these problems, a novel end-to-end framework based on convolutional neural networks is proposed in this paper. With learning representations, the framework proposes a novel multimodal fusion module and a novel representation mapping module to achieve traffic congestion predictions on arbitrary query locations on a large-scale map, combined with various global reference information. The proposed framework achieves significant results and efficient inference on real-world large-scale datasets.
@article{zhou2022large,
title={Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping},
author={Zhou, Bodong and Liu, Jiahui and Cui, Songyi and Zhao, Yaping},
journal={arXiv preprint arXiv:2208.11061},
year={2022}
}