Using a Simulated Annealing Algorithm to Solve the Transit Route Network Design Problem

Fan, Wei; Machemehl, Randy B.

doi:10.1061/(asce)0733-947x(2006)132:2(122)

Cited by 166 publications

(88 citation statements)

References 15 publications

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“…Five different algorithms are used: Tabu search, Simulated annealing, Genetic algorithms, Random search and Local search. The Simulated annealing algorithm is particularly detailed by Fan and Machemehl (2006b).…”

Section: Neighborhood Search Approachesmentioning

confidence: 99%

Transit network design and scheduling: A global review

Guihaire

Hao

2008

Transportation Research Part A: Policy and Practice

423

313

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This paper presents a global review of the crucial strategic and tactical steps of transit planning: the design and scheduling of the network. These steps influence directly the quality of service through coverage and directness concerns but also the economic profitability of the system since operational costs are highly dependent on the network structure. We first exhibit the context and the goals of strategic and tactical transit planning. We then establish a terminology proposal in order to name sub-problems and thereby structure the review. Then, we propose a classification of 69 approaches dealing with the design, frequencies setting, timetabling of transit lines and their combinations. We provide a descriptive analysis of each work so as to highlight their main characteristics in the frame of a two-fold classification referencing both the problem tackled and the resolution method used. Finally, we expose recent context evolutions and identify some trends for future research. This paper aims to contribute to unification of the field and constitutes a useful complement to the few existing reviews.

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Section: Neighborhood Search Approachesmentioning

confidence: 99%

Transit network design and scheduling: A global review

Guihaire

Hao

2008

Transportation Research Part A: Policy and Practice

423

313

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“…Kermansshahi et al (2010) develop a simulated annealing methodology to solve networks of medium-scale cities. Fan et al (2006) use Simulated Annealing to solve a transit network design problem. Ngamchai and Lovell (2003) and Tom and Mohan (2003) use Genetic Algorithms to solve the same type of problems.…”

Section: State Of the Artmentioning

confidence: 99%

Improved rapid transit network design model: considering transfer effects

Cadarso

Marı́n

2015

Ann Oper Res

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The rail rapid transit network design problem aims at locating train alignments and stations, maximizing demand coverage while competing with the current existing networks. We present a model formulation for computing tight bounds of the linear relaxation of the problem where transfers are also introduced. The number of transfers within a trip is a decisive attribute for attracting passengers: transferring is annoying and undesirable for passengers. We conduct computational experiments on different networks and show how we are able to solve more efficiently problems that have been already solved; sensitivity analysis on several model parameters are also performed so as to demonstrate the robustness of the new formulation.

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“…Applies a genetic algorithm. Fan and Machemehl (2006) Includes computer aided network design and assignment. Applies a simulated annealing procedure to select an optimal set of routes.…”

Section: Saka (2001)mentioning

confidence: 99%

“…Finally, other metaheuristics such as taboo search or simulated annealing have been used to search the optimal set of routes in the solution domain (Fan and Machemehl 2006). Table 1 provides a summary of past research on this topic, including both the continuous and discrete approaches.…”

Section: Introductionmentioning

confidence: 99%