2014
DOI: 10.1007/978-3-662-44320-0_5
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An Improved Multi-objective Algorithm for the Urban Transit Routing Problem

Abstract: Abstract. The determination of efficient routes and schedules in public transport systems is complex due to the vast search space and multiple constraints involved. In this paper we focus on the Urban Transit Routing Problem concerned with the physical network design of public transport systems. Historically, route planners have used their local knowledge coupled with simple guidelines to produce network designs. Several major studies have identified the need for automated tools to aid in the design and evalua… Show more

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Cited by 23 publications
(35 citation statements)
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References 20 publications
(45 reference statements)
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“…Referring to Tables 7 and 8 our method found the best average travel times in all Mandl instances as well as the best d 0 , d 1 , d 2 in all cases except in Mandl4 instance. In Mumford's instances, our approach outperformed the methods in (Mumford, 2013;John et al, 2014;Kılıç and Gök, 2014) in terms of the average travel time, and d 0 , d 1 , d 2 values scoring zero percentage for unsatisfied demand in all cases. From the operator perspective, we succeeded in finding the lower bound in Mandl's four problems and in Mumford0 instance.…”
Section: Comparison With Other Approachesmentioning
confidence: 89%
See 1 more Smart Citation
“…Referring to Tables 7 and 8 our method found the best average travel times in all Mandl instances as well as the best d 0 , d 1 , d 2 in all cases except in Mandl4 instance. In Mumford's instances, our approach outperformed the methods in (Mumford, 2013;John et al, 2014;Kılıç and Gök, 2014) in terms of the average travel time, and d 0 , d 1 , d 2 values scoring zero percentage for unsatisfied demand in all cases. From the operator perspective, we succeeded in finding the lower bound in Mandl's four problems and in Mumford0 instance.…”
Section: Comparison With Other Approachesmentioning
confidence: 89%
“…Their experiments were tested on Mandl's instance and compared with the work of Mumford (2013) and Fan and Mumford (2010), where they reported improved results. The work in (John et al, 2014) is built upon the work of Mumford (2013) using an NSGA-II bi-objective framework. They developed a new powerful heuristic construction method for candidate route sets generation and implemented eight new operators to perform replace, exchange, and merge operations.…”
Section: Urban Transit Routing Problemmentioning
confidence: 99%
“…The determination of efficient routes and schedules in public transport systems is complex due to the vast search space and the multiple constraints involved [27][28][29][30][31]. It can be seen from the above statement that the established model consists of the passenger waiting time and the wasted capacity, where minimizing the passenger waiting time is a nonlinear integer programming problem and the minimization of wasted capacity is a general linear mixed integer programming problem.…”
Section: Solution Proceduresmentioning
confidence: 99%
“…On the other hand, the operator costs depend on many factors including the fleet size required to maintain the needed service level, the daily distance covered by the vehicles, vehicle operating hours and the cost of employing enough drivers. The UTRP can be formally defined as (see, [15]): Given a road network represented as a graph = ( , ), where = { 1 , … } is a set of vertices representing demand points (bus stops) and = { 1 , … , } a set of edges representing street segments. The weight for each edge, , defines the time it takes to traverse edge , and matrix × such that ,…”
Section: Urban Transit Routing Problemmentioning
confidence: 99%