Passenger Flow Prediction of Urban Public Transport Hubs Based on a Deep Learning Approach: A Case Study in Changzhou

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Te Xu ◽  
Min Yang ◽  
Jingxian Wu ◽  
Da Lei ◽  
Yunteng Wu
Author(s):  
Sonnam Jo ◽  
Liang Gao ◽  
Feng Liu ◽  
Menghui Li ◽  
Zhesi Shen ◽  
...  

Robustness studies on integrated urban public transport networks have attracted growing attention in recent years due to the significant influence on the overall performance of urban transport system. In this paper, topological properties and robustness of a bus–subway coupled network in Beijing, composed of both bus and subway networks as well as their interactions, are analyzed. Three new models depicting cascading failure processes on the coupled network are proposed based on an existing binary influence modeling approach. Simulation results show that the proposed models are more accurate than the existing method in reflecting actual passenger flow redistribution in the cascading failure process. Moreover, the traffic load influence between nodes also plays a vital role in the robustness of the network. The proposed models and derived results can be utilized to improve the robustness of integrated urban public transport systems in traffic planning.


2018 ◽  
Vol 43 ◽  
pp. 357-365 ◽  
Author(s):  
Xiuxia Zhang ◽  
Qingnian Zhang ◽  
Tingting Sun ◽  
Yongchao Zou ◽  
Huanwan Chen

2021 ◽  
Vol 279 ◽  
pp. 123807 ◽  
Author(s):  
Marcin Wołek ◽  
Michał Wolański ◽  
Mikołaj Bartłomiejczyk ◽  
Olgierd Wyszomirski ◽  
Krzysztof Grzelec ◽  
...  

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