Research on Public Bicycle Demand Forecasting Based on Historical Data and BP Neural Network

Station layout and scheduling optimization could improve public bicycle system efficiency, and it is important to analyze and forecast the spatial and temporal distribution of demand. Based on rental data of the Hohhot public bicycle system in 2017, this study establishes a public bicycle demand forecasting model using back propagation (BP) neural network. A station with large demand is selected to analyze the distribution of demand in different periods. By analyzing the similarity of data in different periods, the law of data variation is determined. Then the public bicycle demand forecasting model based on BP neural network (PBDF-BP model) is trained and validated by using peak hour rental data. By comparing the prediction accuracy with other forecasting models, it is found that the PBDF-BP model has less error in prediction results and higher stability. These findings may be help promote the sustainable development of public bicycle systems.

• Record URL:
  • https://doi.org/10.1061/9780784483053.259
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483053
• Supplemental Notes:
  • © 2020 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Jian, Meiying
  • Zhang, Jiayu
• Conference:
  • 20th COTA International Conference of Transportation Professionals
  • Location: Xi’an , China
  • Date: 2020-8-14 to 2020-8-16
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 3074-3086
• Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

• TRT Terms: Bicycles; Forecasting; Neural networks; Optimization; Scheduling; Travel demand; Validation; Vehicle sharing
• Subject Areas: Pedestrians and Bicyclists; Planning and Forecasting; Terminals and Facilities;

Filing Info

• Accession Number: 01766360
• Record Type: Publication
• ISBN: 9780784483053
• Files: TRIS, ASCE
• Created Date: Mar 1 2021 9:24AM