A novel passenger flow prediction model using deep learning methods
place - asia, place - urban, mode - bus rapid transit, technology - ticketing systems, technology - passenger information, ridership - modelling, ridership - forecasting
Passenger flow, Prediction, Deep learning, Autoencoder, BRT
Currently, deep learning has been successfully applied in many fields and achieved amazing results. Meanwhile, big data has revolutionized the transportation industry over the past several years. These two hot topics have inspired us to reconsider the traditional issue of passenger flow prediction. As a special structure of deep neural network (DNN), an autoencoder can deeply and abstractly extract the nonlinear features embedded in the input without any labels. By exploiting its remarkable capabilities, a novel hourly passenger flow prediction model using deep learning methods is proposed in this paper. Temporal features including the day of a week, the hour of a day, and holidays, the scenario features including inbound and outbound, and tickets and cards, and the passenger flow features including the previous average passenger flow and real-time passenger flow, are defined as the input features. These features are combined and trained as different stacked autoencoders (SAE) in the first stage. Then, the pre-trained SAE are further used to initialize the supervised DNN with the real-time passenger flow as the label data in the second stage. The hybrid model (SAE-DNN) is applied and evaluated with a case study of passenger flow prediction for four bus rapid transit (BRT) stations of Xiamen in the third stage. The experimental results show that the proposed method has the capability to provide a more accurate and universal passenger flow prediction model for different BRT stations with different passenger flow profiles.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Liu, L., & Chen, R. (2017). A novel passenger flow prediction model using deep learning methods. Transportation Research Part C: Emerging Technologies, Vol. 84, pp.74-91.