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

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...

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Main Authors: Ken-ichi Tanaka, Takehiro Furuta, Shigeki Toriumi
Format: Article
Language:English
Published: Elsevier 2019-01-01
Series:Operations Research Perspectives
Online Access:http://www.sciencedirect.com/science/article/pii/S2214716018301714
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author Ken-ichi Tanaka
Takehiro Furuta
Shigeki Toriumi
author_facet Ken-ichi Tanaka
Takehiro Furuta
Shigeki Toriumi
author_sort Ken-ichi Tanaka
collection DOAJ
description 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
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spelling doaj.art-f9664d38b8a24710a2566a5311caf0fe2022-12-22T00:01:09ZengElsevierOperations Research Perspectives2214-71602019-01-016Railway flow interception location model: Model development and case study of Tokyo metropolitan railway networkKen-ichi Tanaka0Takehiro Furuta1Shigeki Toriumi2Corresponding Author.; Keio University, Japan; Department of Administration Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, JapanNara University of Education, JapanChuo University, JapanThe 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 networkhttp://www.sciencedirect.com/science/article/pii/S2214716018301714
spellingShingle Ken-ichi Tanaka
Takehiro Furuta
Shigeki Toriumi
Railway flow interception location model: Model development and case study of Tokyo metropolitan railway network
Operations Research Perspectives
title Railway flow interception location model: Model development and case study of Tokyo metropolitan railway network
title_full Railway flow interception location model: Model development and case study of Tokyo metropolitan railway network
title_fullStr Railway flow interception location model: Model development and case study of Tokyo metropolitan railway network
title_full_unstemmed Railway flow interception location model: Model development and case study of Tokyo metropolitan railway network
title_short Railway flow interception location model: Model development and case study of Tokyo metropolitan railway network
title_sort railway flow interception location model model development and case study of tokyo metropolitan railway network
url http://www.sciencedirect.com/science/article/pii/S2214716018301714
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