Large-scale road network partitioning: a deep learning method based on convolutional autoencoder model
<|reference_start|>Large-scale road network partitioning: a deep learning method based on convolutional autoencoder model: With the development of urbanization, the scale of urban road network continues to expand, especially in some Asian countries. Short-term traffic state prediction is one of the bases of traffic management and control. Constrained by the space-time cost of computation, the short-term traffic state prediction of large-scale urban road network is difficult. One way to solve this problem is to partition the whole network into multiple sub-networks to predict traffic state separately. In this study, a deep learning method is proposed for road network partitioning. The method mainly includes three steps. First, the daily speed series for roads are encoded into the matrix. Second, a convolutional autoencoder (AE) is built to extract series features and compress data. Third, the spatial hierarchical clustering method with adjacency relationship is applied in the road network. The proposed method was verified by the road network of Shenzhen which contains more than 5000 links. The results show that AE-hierarchical clustering distinguishes the tidal traffic characteristics and reflects the process of congestion propagation. Furthermore, two indicators are designed to make a quantitative comparison with spectral clustering: intra homogeneity increases by about 9% on average while inter heterogeneity about 9.5%. Compared with past methods, the time cost also decreases. The results may suggest ways to improve the management and control of urban road network in other metropolitan cities. The proposed method is expected to be extended to other problems related to large-scale networks.<|reference_end|>
@article{xu2024large-scale,
    title={Large-scale road network partitioning: a deep learning method based on
  convolutional autoencoder model},
    author={Pengfei Xu, Weifeng Li, Chenjie Xu and Jian Li},
    journal={arXiv preprint arXiv:2409.05132},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05132},
    primaryClass={eess.SY cs.SY}
}