Railway flow interception location model: Model development and case study of Tokyo metropolitan railway network

Ken-ichi Tanaka, Takehiro Furuta and Shigeki Toriumi

Operations Research Perspectives, 2019, vol. 6, issue C

Abstract: The flow interception location model (FILM) focuses on vehicular traffic in a road network and locates a fixed number of facilities so as to maximize the total flow that can be serviced at facilities along preplanned routes, such as a daily commute to work. This paper develops a version of FILM, the railway flow interception location model (R-FILM), that explicitly focuses on railway passenger flows. For railway users, accessing a facility placed at an origin, destination, or transfer station (ODT station) is easier than visiting a facility at other stations included in the travel path. Accessing a facility at a non-ODT station involves the additional cost of disembarking the train to obtain a service and then boarding another train after consuming the service. R-FILM introduces this railway-specific structure to FILM by introducing two different coverages according to the types of station intercepted for each flow. Concretely, a given flow is called strongly covered when at least one facility is located at an ODT station. Similarly, a given flow is called weakly covered when no facility is located at an ODT station, but at least one facility is located among stations included in the travel path. We present an integer programming formulation of the proposed R-FILM. Using it, we conduct a large-scale case study of the Tokyo metropolitan railway network, which includes about 1500 railway stations. Input flow is constructed using census data for commuter traffic, with about 100,000 distinct flow paths. Optimal solutions of both models for single- and multi-facility problems are analyzed. In R-FILM solutions, large terminal stations tend to be selected more often than with FILM.

Keywords: Discrete location problem; Flow interception location model; Integer programming; Railway passenger flow; Tokyo metropolitan railway network (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2214716018301714
Full text for ScienceDirect subscribers only

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:eee:oprepe:v:6:y:2019:i:c:s2214716018301714

DOI: 10.1016/j.orp.2018.11.001

Access Statistics for this article

Operations Research Perspectives is currently edited by Rubén Ruiz Garcia

More articles in Operations Research Perspectives from Elsevier
Bibliographic data for series maintained by Catherine Liu ().