Short-term Forecast on Individual Accessibility in Bus System Based on Neural Network

This study proposes a three-stage method for short-term forecast on individual accessibility in bus system based on neural network (NN). In the first stage, a NN is designed to determine whether the passengers will have bus trips in the predicted period. An extra layer is added into the NN considering the bus travel generation rate in the region. The inputs of the designed NN are composed of the appearance of the bus trips of the passengers in the historical periods. Then, the probability of the passenger’s destination choice is calculated based on his/her historical bus trip data. In the third stage, land use information combined with the results of the second stage are used to obtain the individual accessibility in bus system in the predicted period. The land use information is represented by spatial distribution of the number of points of interest in the studied area. Results show that the proposed method can give precise short-term forecast on individual accessibility in bus system both in weekdays and weekends. The results also demonstrate the capabilities of combining deep learning method, traffic data and land use information to get the future spatial distribution of individual accessibility in traffic system.


  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 13p

Subject/Index Terms

Filing Info

  • Accession Number: 01763587
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02488
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:06AM