Urban Taxi Demand Forecast Based on Graph Convolutional Network

Author:

Wang Yaguan,Qin Yong,Guo Jianyuan

Publisher

Springer Singapore

Reference13 articles.

1. Hai, Y., Yan, W.L., Lo, W.: A macroscopic taxi model for passenger demand, taxi utilization and level of services. Transportation 27, 317–340 (2000)

2. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);L Moreira-Matias,2013

3. Lu, M.C., Li, J.B., Pang, J.J., Li, Y., Dong, X.S.: Multi-factor spatio-temporal graph convolution network for taxi demand pre-diction. Comput. Eng. Appl. 56(24), 266–273 (2020). (in Chinese)

4. Duan, Z.T., Zhang, K., Yang, Y., Ni, Y.Y., Saurab, B.: Taxi demand prediction based on CNN-LSTM-ResNetHybrid depth learning model. J. Transp. Syst. Eng. Inf. Technol. 18(4), 215–223 (2018). (in Chinese)

5. Lin, Y.J., Chu, N.: Short-term prediction model of taxi passenger demand based on operation systems. J. Northeastern Univ. (Nat. Sci.) 37(9), 1235–1240 (2016). (in Chinese)

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