Exact algorithms for multi-criteria multi-modal shortest path with transfer delaying and arriving time-window in urban transit network

Liu, Linzhong; Yang, Juhua; Mu, Haibo; Li, Xiaojing; Wu, Fang

doi:10.1016/j.apm.2013.10.059

Cited by 21 publications

(18 citation statements)

References 27 publications

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“…Finally, the results of the numerical experiments deriving from the LINGO software demonstrated the performance of the proposed approaches. Actually, multi-modal shortest path problems have been widely solved by the label-setting algorithm [35], label-correcting algorithm [25], and genetic algorithm [36,37], which can also be considered in our further studies.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, investigating the multi-modal shortest path problem in the indeterministic environment becomes a significant and challenging issue for the practical applications. We here note that finding the shortest path in a multi-modal network has been studied in various forms such as static [22], dynamic [26,28], stochastic [29,30], fuzzy [27], constrained [23], and multi-criteria [24,25,31,32]. However, to the best of our knowledge, few studies have been considered in the uncertain environment within the framework of uncertain programming.…”

Section: Introductionmentioning

confidence: 99%

“…Ambrosino and Sciomachen [24] proposed an approach for computing shortest routes in multi-modal networks with objectives of minimizing the overall time, cost, and users' discomfort. Liu et al [25] designed an improved exact algorithm for a multi-criteria multi-modal shortest path problem with both arriving time window and transfer delaying. Galvez-Fernandez et al [26] introduced a transfer graph approach, which was believed to better abstract the distributed nature of real transport information sources, to calculate the best paths in multi-modal networks.…”

Section: Introductionmentioning

confidence: 99%

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A bi-objective model for uncertain multi-modal shortest path problems

Zhang

Liu

Yang

et al. 2015

J. Uncertain. Anal. Appl.

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This paper employs uncertain programming to investigate the uncertain multi-modal shortest path problem, in which the arc weights (arc travel time, arc travel costs) associated with different transport modes are characterized by uncertain variables. By using the chance-constrained programming approach, we firstly formulate a bi-objective optimization model to minimize the total travel time and travel costs simultaneously with the given confidence levels. Moreover, using the basic concepts and properties in the uncertainty theory, we transform the proposed model into its deterministic crisp equivalent with an explicit proof. Finally, some numerical experiments are implemented to show the performance of the proposed approaches on a multi-modal transportation network with three specific modes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A bi-objective model for uncertain multi-modal shortest path problems

Zhang

Liu

Yang

et al. 2015

J. Uncertain. Anal. Appl.

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“…A number of papers discussed various ways of finding the shortest path in a multi-modal network, but most articles often refer to the use of the label-setting algorithm [26], label-correcting algorithm [27], and genetic algorithms [28]. It should be noted that, in the basic effective solutions for journey planning in public transit networks, the timetable is formulated as a graph, hence, travel corresponds to a path in the graph.…”

Section: Introductionmentioning

confidence: 99%

Cockroach Swarm Optimization Algorithm for Travel Planning

Kwiecień

Pasieka

2017

Entropy

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Abstract:In transport planning, one should allow passengers to travel through the complicated transportation scheme with efficient use of different modes of transport. In this paper, we propose the use of a cockroach swarm optimization algorithm for determining paths with the shortest travel time. In our approach, this algorithm has been modified to work with the time-expanded model. Therefore, we present how the algorithm has to be adapted to this model, including correctly creating solutions and defining steps and movement in the search space. By introducing the proposed modifications, we are able to solve journey planning. The results have shown that the performance of our approach, in terms of converging to the best solutions, is satisfactory. Moreover, we have compared our results with Dijkstra's algorithm and a particle swarm optimization algorithm.

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“…The main capability of their proposed algorithm was predicting users' preferences for give a context to help them with facing a new situation. (Liu, Yang, Mu, Li, & Wu, 2014) used a label correcting approach for finding the best multimodal multi-criteria route by considering transfer delaying and arriving time window.…”

Section: Introductionmentioning

confidence: 99%

A New Multimodal Multi-Criteria Route Planning Model by Integrating a Fuzzy-Ahp Weighting Method and a Simulated Annealing Algorithm

Ghaderi

Pahlavani

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A multimodal multi-criteria route planning (MMRP) system provides an optimal multimodal route from an origin point to a destination point considering two or more criteria in a way this route can be a combination of public and private transportation modes. In this paper, the simulate annealing (SA) and the fuzzy analytical hierarchy process (fuzzy AHP) were combined in order to find this route. In this regard, firstly, the effective criteria that are significant for users in their trip were determined. Then the weight of each criterion was calculated using the fuzzy AHP weighting method. The most important characteristic of this weighting method is the use of fuzzy numbers that aids the users to consider their uncertainty in pairwise comparison of criteria. After determining the criteria weights, the proposed SA algorithm were used for determining an optimal route from an origin to a destination. One of the most important problems in a meta-heuristic algorithm is trapping in local minima. In this study, five transportation modes, including subway, bus rapid transit (BRT), taxi, walking, and bus were considered for moving between nodes. Also, the fare, the time, the user's bother, and the length of the path were considered as effective criteria for solving the problem. The proposed model was implemented in an area in centre of Tehran in a GUI MATLAB programming language. The results showed a high efficiency and speed of the proposed algorithm that support our analyses.

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Exact algorithms for multi-criteria multi-modal shortest path with transfer delaying and arriving time-window in urban transit network

Cited by 21 publications

References 27 publications

A bi-objective model for uncertain multi-modal shortest path problems

A bi-objective model for uncertain multi-modal shortest path problems

Cockroach Swarm Optimization Algorithm for Travel Planning

A New Multimodal Multi-Criteria Route Planning Model by Integrating a Fuzzy-Ahp Weighting Method and a Simulated Annealing Algorithm

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