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Predicting Passenger’s Public Transportation Travel Route Using Smart Card Data

  • Chen Yang
  • Wei Chen
  • Bolong Zheng
  • Tieke He
  • Kai Zheng
  • Han Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)

Abstract

Transit prediction is a important task for public transport institutions and urban planners to provide better transit scheduling and urban planning. In recent years, there are a lot of research on traffic prediction, but the existing works focus predicting the monolithic traffic trend, and few works focus on passenger’s public transportation travel route. In this paper, we study the passenger’s travel route and duration prediction. We propose a prediction model based on LSTM neural network to predict passenger’s travel route and duration. Specifically, we leverage multimodal embedding to extract passenger’s features which are highly related to passenger’s travel route and then use a LSTM-based model to improve the prediction accuracy. To verify the effectiveness of our model, we conduct extensive experiments using a real dataset which is collected from Brisbane in Australia for four months. The experimental results show that the accuracy of our model is better than baseline models.

Keywords

Transit prediction Multimodal embedding Smart card 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Chen Yang
    • 1
  • Wei Chen
    • 1
  • Bolong Zheng
    • 2
    • 3
  • Tieke He
    • 4
  • Kai Zheng
    • 1
  • Han Su
    • 1
  1. 1.Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Computer ScienceAalborg UniversityAalborgDenmark
  4. 4.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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