Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

  • Siyuan Zhang Yangzhou University
  • Shijun Yu Yangzhou University
  • Shejun Deng Yangzhou University
  • Qinghui Nie Yangzhou University
  • Pengpeng Zhang Yangzhou University
  • Chen Chen Oregon State University
Keywords: Bike-and-Ride, dynamic demand, generalised cost, user equilibrium model, Frank-Wolfe algorithm

Abstract

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.

Author Biographies

Siyuan Zhang, Yangzhou University

Master, College of Civil Science and Engineering

Shijun Yu, Yangzhou University

Ph.D., College of Civil Science and Engineering

Shejun Deng, Yangzhou University

Ph.D., College of Civil Science and Engineering

Qinghui Nie, Yangzhou University

Ph.D., College of Civil Science and Engineering

Pengpeng Zhang, Yangzhou University

Master, College of Civil Science and Engineering

Chen Chen, Oregon State University
Ph.D. Candidate, School of Civil and Construction Engineering

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Published
2019-12-11
How to Cite
1.
Zhang S, Yu S, Deng S, Nie Q, Zhang P, Chen C. Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System. PROMET [Internet]. 2019Dec.11 [cited 2020Jan.19];31(6):621-32. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/3197
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Articles