Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


Accurate prediction of passenger demands for taxis is vital for reducing the waiting time of passengers and drivers in large cities as we move towards smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence of external factors (e.g., weather, holidays) in a simplistic manner by directly mapping external features to demands through fully-connected layers and thus result in substantial bias as the influence of external factors is not unified. To tackle these problems, we propose an end-to-end multi-task deep learning model for passenger demand prediction. First, we select similar regions for each target region based on their Point-of-Interest (PoI) information or historical demand and utilize Convolutional Neural Networks (CNN) to extract their spatial correlations. Second, we map external factors to future demand levels as part of the multi-task learning framework to further boost prediction accuracy. We conduct experiments on a large-scale real-world dataset collected from a city in China with a population of 1.5 million. The results demonstrate that our model significantly outperforms the state-of-the-art and a set of baseline methods.


Demand prediction Muti-task learning Spatial-temporal correlations Convolutional recurrent neural networks 


  1. 1.
    Ke, J., et al.: Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp. Res. Part C: Emerg. Technol. 85, 591–608 (2017)CrossRefGoogle Scholar
  2. 2.
    Yao, H., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. In: AAAI (2018)Google Scholar
  3. 3.
    Zhang, J., et al.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI (2017)Google Scholar
  4. 4.
    Deng, D., et al.: Latent space model for road networks to predict time-varying traffic. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)Google Scholar
  5. 5.
    Wang, D., et al.: DeepSD: supply-demand prediction for online car-hailing services using deep neural networks. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE (2017)Google Scholar
  6. 6.
    Moreira-Matias, L., et al.: Predicting taxi-passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14(3), 1393–1402 (2013)CrossRefGoogle Scholar
  7. 7.
    Li, X., et al.: Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comput. Sci. 6(1), 111–121 (2012)MathSciNetGoogle Scholar
  8. 8.
    Li, Y., et al.: Taxi booking mobile app order demand prediction based on short-term traffic forecasting. Transp. Res. Rec.: J. Transp. Res. Board 2634, 57–68 (2017)CrossRefGoogle Scholar
  9. 9.
    Yu, R., et al.: Deep learning: a generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2017)Google Scholar
  10. 10.
    Zheng, Y., et al.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 5(3), 38 (2014)Google Scholar
  11. 11.
    Chu, J., et al.: Passenger demand prediction with cellular footprints. In: 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE (2018)Google Scholar
  12. 12.
    Zhang, J., et al.: DNN-based prediction model for spatio-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM (2016)Google Scholar
  13. 13.
    Xingjian, S.H.I., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems (2015)Google Scholar
  14. 14.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.School of SoftwareTsinghua UniversityBeijingChina
  3. 3.School of SoftwareUniversity of Technology SydneySydneyAustralia

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