ST-Bikes: Predicting Travel-Behaviors of Sharing-Bikes Exploiting Urban Big Data

Jun Chai, Jun Song*, Hongwei Fan, Yibo Xu, Le Zhang, Bing Guo*, Yawen Xu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)


With the development of the modern smart city, sharing-bikes require behaviors prediction for grid-level areas which is essential for intelligent transportation systems. A model which can predict bike sharing demand behaviours accurately can allocate sharing-bikes in advance to satisfy travel demands alongside saving energy, reducing traffic, cutting down waste for those sharing-bikes companies putting excessive sharing-bikes in unsaturated demand areas. In this paper, we abandon the traditional time series prediction method and use a more efficient deep learning method to solve the traffic forecasting problem. Moreover, instead of considering spatial relation and temporal relation relatively, we produced a deep multi-view spatial-temporal network to combine them into one prediction model framework. In the experimental section, we investigate in the experiment on enormous amount of real sharing-bikes application use data in the core region of Beijing to test the performance of the model framework with a 1 km × 1 km grid-level scale and compare it with other existing machine learning approaches and prediction models. And the 4G/5G/6G communication technology facilitate the real-time control of the space-time locations of sharing bikes dynamically. Thus, it provides the basis for high-frequency analysis of space-time patterns, especially supported by the 6G large-scale application in the future.

Original languageEnglish
Pages (from-to)7676-7686
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
Early online date18 Aug 2022
Publication statusPublished - Jul 2023

Scopus Subject Areas

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications

User-Defined Keywords

  • Sharing-bikes prediction
  • ITS
  • deep learning
  • multi-view
  • spatial-temporal feature
  • travel-behaviors 4G/5G/6G communication


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