Urban Bus Transit Route Network Design Using Genetic Algorithm

Pattnaik, S. B.; Mohan, S.; Tom, V. M.

doi:10.1061/(asce)0733-947x(1998)124:4(368)

Cited by 297 publications

(130 citation statements)

References 9 publications

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“…Their computational tool is effectively tested both on literature benchmarks and realistic data from a transit operator. Pattnaik et al (1998) propose a Genetic algorithm based method to simultaneously determine the transit routes network and frequencies. The objective is to minimize both operator costs and passengers travel time, given headway constraints.…”

Section: Neighborhood Search Approachesmentioning

confidence: 99%

Transit network design and scheduling: A global review

Guihaire

Hao

2008

Transportation Research Part A: Policy and Practice

422

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

422

313

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“…Numerous scholars, including Newell (1979) and Baaj and Mahmassani (1991), have pointed out that traditional mathematical programming has difficulties in generating an optimal transit network due to nonlinearity and nonconvexity of the model, combinatorial explosion, multiobjective nature, and spatial layout of routes. With the improvement of search algorithms and computer technology, important heuristic research has been done (Hasselström 1981;Baaj and Mahmassani 1991;Shih, Mahmassani, and Baaj 1998;Ceder and Israeli 1998;Pattnaik, Mohan, and Tom 1998;Chien, Yang, and Hou 2001). All of these studies are based on the combinatorial search approach.…”

Section: The Model For the Transit Network Designmentioning

confidence: 99%

Transit Network Sensitivity Analysis

Lee¹

2006

JPT

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“…The transit network-planning problem [44] can be defined as follows: for known estimated annual numbers of passengers between individual pairs of nodes, the shape of a network of public transportation lines and their corresponding service frequencies should be determined, taking into account operator revenues and costs as well as the level of service offered to passengers. The problem of determining the shape of public transportation routes and corresponding service frequencies is combinatorial by its nature.…”

Section: Some Potential Applications Of Swarm Intelligence In Trafficmentioning

confidence: 99%

Transport modeling by multi-agent systems: a swarm intelligence approach

Teodorovic

2003

Transportation Planning and Technology

129

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There are a number of emergent traffic and transportation phenomena that cannot be analyzed successfully and explained using analytical models. The only way to analyze such phenomena is through the development of models that can simulate behavior of every agent. Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual 'agents' and that each agent interacts with other agents according to localized knowledge. The agent-based approach is a 'bottom-up' approach to modeling where special kinds of artificial agents are created by analogy with social insects. Social insects (including bees, wasps, ants and termites) have lived on Earth for millions of years. Their behavior in nature is primarily characterized by autonomy, distributed functioning and self-organizing capacities. Social insect colonies teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. On the other hand, a large number of traditional engineering models and algorithms are based on control and centralization. In this article, we try to obtain the answer to the following question: Can we use some principles of natural swarm intelligence in the development of artificial systems aimed at solving complex problems in traffic and transportation?

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Urban Bus Transit Route Network Design Using Genetic Algorithm

Cited by 297 publications

References 9 publications

Transit network design and scheduling: A global review

Transit network design and scheduling: A global review

Transit Network Sensitivity Analysis

Transport modeling by multi-agent systems: a swarm intelligence approach

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