Modeling the cordon pricing policy for a multi-modal transportation system. Issue 3 (September 2019)

• Record Type: Journal Article
• Authors: Johari, Mansour; Haghshenas, Hossein
• Is Part Of: Case studies on transport policy. Volume 7:Issue 3(2019)
• Page Start: 531
• Page End: 539
• Publication Date: 2019-09
• Subjects: Travel demand management -- Multi-modal urban networks -- Congestion pricing -- Cordon pricing; Transportation and state -- Case studies -- Periodicals; Transportation -- Planning -- Case studies -- Periodicals; Transportation -- Research -- Case studies -- Periodicals; 388.05
• DOI: 10.1016/j.cstp.2019.07.012
• Languages: English
• ISSNs: 2213-624X

Abstract

Highlights: A simple methodology is presented to facilitate the complexity of previous modeling approaches. In comparison with relative studies, it is shown that the presented method results contain an acceptable accuracy level. Abstract: Cordon pricing is a promising policy through which the travel demand is managed in order to alleviate the negative impacts of traffic congestion. The models proposed in the literature are usually based on optimization procedures, which make them particularly complex for practitioners. To facilitate the modeling of this policy, this paper presents a simple method to model the cordon pricing policy in a multi-modal urban network. To this end, the origin-destination matrices subjected to cordon pricing conditions are obtained by the current origin-destination matrices as well as a car utility function. These current origin-destination matrices are built upon revealed preference data while the car utility function is based on stated preference data. Then, matrices subjected to cordon pricing condition are assigned to the transportation network of Isfahan, Iran. The simultaneous use of revealed preference and stated preference data in the presented modeling improves accuracy and allows prediction of people's reactions to the cordon pricing policy. Consistent with previous studies, the results show a 49% fall in car users and a 70% rise in public transit users within the pricing area, which leads to 2.5% decrease in both the travel time and air pollution costs in the entire network.