Dynamic Spatio-Temporal Multi-Scale Representation for Bus Ridership Prediction

Author:

Peng Lilan1,Wang Xiu2,Lu Hongchun1,Guo Xiangyu1,Li Tianrui1,Ji Shenggong3

Affiliation:

1. School of Computing and Artificial Intelligence, Southwest Jiaotong University,Chengdu,China

2. School of Economics and Management, Sichuan Technology and Business University,Meishan,China

3. Tencent Inc.,Shenzhen,China

Publisher

IEEE

Reference35 articles.

1. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution;li;ACM Transactions on Knowledge Discovery from Data (TKDD),2021

2. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

3. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

4. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

5. Spatiotemporal multi-graph convolution network for ride-hailing de-mand forecasting;geng;Proceedings of the AAAI Conference on Artificial Intelligence,2019

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