Optimizing location and capacity of rail-based Park-and-Ride sites to increase public transport usage

Xinyuan Chen, Zhiyuan Liu and Graham Currie

Transportation Planning and Technology, 2016, vol. 39, issue 5, 507-526

Abstract: This paper presents a new methodology to identify optimal locations and capacity for rail-based Park-and-Ride (P&R) sites to increase public transport mode share. P&R is usually taken as an important component of policies for the sustainable development of urban transport systems. However, previous studies reveal that arbitrarily determined P&R sites may act to reduce public transport commuting. This paper proposes a methodology for the optimal location and capacity design of P&R sites, with the aim of enhancing public transport usage. A Combined Mode Split and Traffic Assignment (CMSTA) model is proposed for the P&R scheme. Taking the CMSTA model as the lower level, a bi-level mathematical programming model is then built to establish the optimal location and capacity of P&R sites. A heuristic genetic algorithm is adopted to solve this model. Finally, a network example is adopted to test numerically the proposed models and algorithms.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2016.1174366 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:39:y:2016:i:5:p:507-526

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060.2016.1174366

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().