Congestion Prediction on Rapid Transit System Based on Weighted Resample Deep Neural Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


Investigating congestion in train rapid transit system (RTS) in today’s urban is demanded by both the operators and the public. Increase traffic data availability can be obtained from travel smart card and allowed to investigate the congestion of RTS. Artificial neural network are employed to do prediction on traffic. However the imbalance of data is a challenge to make an efficient prediction on congestion of RTS. This work proposes a Weighted Resample Deep Neural Network (WRDNN) model to predict the congestion level of RTS. The case study of RTS of one city of US indicate that the model introduced in this work can effectively predicting the congestion level of RTS with the 90% accuracy..


Congestion prediction Deep neural networks Data imbalance Rapid transit system 



This research is funded by Fujian Provincial Department of Science and Technology (Granted No. 2017J01729) and the China Scholarship Council.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fujian Province Key Laboratory of Automotive Electronics and Electric DriveFujian University of TechnologyFuzhouChina
  2. 2.Department of Civil Engineering and Engineering MechanicsUniversity of ArizonaTucsonUSA

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