This is a PyTorch implementation of Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction.
Due to the widespread applications in real-world scenarios, metro ridership prediction is a crucial but challenging task in intelligent transportation systems. However, conventional methods either ignore the topological information of metro systems or directly learn on physical topology, and cannot fully explore the patterns of ridership evolution. To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system, while a similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation. These complementary graphs are incorporated into a Graph Convolution Gated Recurrent Unit (GC-GRU) for spatial-temporal representation learning. Further, a Fully-Connected Gated Recurrent Unit (FC-GRU) is also applied to capture the global evolution tendency. Finally, we develop a Seq2Seq model with GC-GRU and FC-GRU to forecast the future metro ridership sequentially. Extensive experiments on two large-scale benchmarks (e.g., Shanghai Metro and Hangzhou Metro) well demonstrate the superiority of our PVCGN for station-level metro ridership prediction. \Revise{Moreover, we apply the proposed PVCGN to address the online origin-destination (OD) ridership prediction and the experiment results show the universality of our method.
If you use this code for your research, please cite our paper https://ieeexplore.ieee.org/abstract/document/9269513 :
@article{liu2020physical,
title={Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction},
author={Liu, Lingbo and Chen, Jingwen and Wu, Hefeng and Zhen, Jiajie and Li, Guanbin and Lin, Liang},
journal={IEEE Transactions on Intelligent Transportation Systems},
year={2020},
publisher={IEEE}
}
- python3
- numpy
- yaml
- pytorch
- torch_geometric
cd data && tar xvf data.tar.gz
- SHMetro
python ggnn_train.py --config
data/model/ggnn_sh_multigraph_rnn256_global_local_fusion_input.yaml
- HZMetro
python ggnn_train.py --config
data/model/ggnn_hz_multigraph_rnn256_global_local_fusion_input.yaml
First of all, download the trained model and extract it to trained folder.
mkdir trained
mv trained.zip trained
cd trained && unzip trained.zip
- SHMetro
python ggnn_evaluation.py --config trained/sh.yaml
- HZMetro
python ggnn_evaluation.py --config trained/hz.yaml