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Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks

  • Lei BaiEmail author
  • Lina Yao
  • Salil S. Kanhere
  • Zheng Yang
  • Jing Chu
  • Xianzhi Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)

Abstract

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.

Keywords

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lei Bai
    • 1
    Email author
  • Lina Yao
    • 1
  • Salil S. Kanhere
    • 1
  • Zheng Yang
    • 2
  • Jing Chu
    • 2
  • Xianzhi Wang
    • 3
  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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