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

• Published in: Promet Vol. 31; no. 6; pp. 621 - 632
• Main Authors: Zhang, Siyuan, Yu, Shijun, Deng, Shejun, Nie, Qinghui, Zhang, Pengpeng, Chen, Chen
• Format: Journal Article Paper
• Language: English
• Published: Fakultet prometnih znanosti Sveučilišta u Zagrebu 11.12.2019; University of Zagreb, Faculty of Transport and Traffic Sciences
• Subjects: bike-and-ride; dynamic demand; frank-wolfe algorithm; generalised cost; user equilibrium model
• ISSN: 0353-5320; 1848-4069
• DOI: 10.7307/ptt.v31i6.3197

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.