An oriented spanning tree based genetic algorithm for multi-criteria shortest path problems

Liu, Linzhong; Mu, Haibo; Yang, Xinfeng; He, Ruichun; Li, Yinzhen

doi:10.1016/j.asoc.2011.08.015

Cited by 32 publications

(22 citation statements)

References 15 publications

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“…An oriented spanning tree based genetic algorithm to find pareto optimal solutions for multi‐criteria shortest path problems was developed by Liu et al. Siddiqi et al developed an evolutionary algorithm that identified single most dominant solution as well as achieved a pre‐defined number of non‐dominant solutions for multi‐objective shortest path problem. An exact label‐setting algorithm that identifies a set of pareto optimal paths based on lexicographic goals for multi‐objective shortest path problem was developed by Pulido et al .…”

Section: Related Literaturementioning

confidence: 99%

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Godwin

Sajeev

George

2016

J of Advced Transportation

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Summary Intra‐city commuting is being revolutionized by call‐taxi services in many developing countries such as India. A customer requests a taxi via phone, and it arrives at the right time and at the right location for the pick‐up. This mode of intra‐city travel has become one of the most reliable and convenient modes of transportation for customers traveling for business and non‐business purposes. The increased number of vehicles on city roads and raising fuel costs has prompted a new type of transportation logistics problem of finding a fuel‐efficient and quickest path for a call‐taxi through a city road network, where the travel times are stochastic. The stochastic travel time of the road network is induced by obstacles such as the traffic signals and intersections. The delay and additional fuel consumption at each of these obstacles are calculated that are later imputed to the total travel time and fuel consumption of a path. A Monte‐Carlo simulation‐based approach is proposed to identify unique fuel‐efficient paths between two locations in a city road network where each obstacle has a delay distribution. A multi‐criteria score is then assigned to each unique path based on the probability that the path is fuel efficient, the average travel time of the path and the coefficient of variation of the travel times of the path. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Related Literaturementioning

confidence: 99%

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Godwin

Sajeev

George

2016

J of Advced Transportation

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“…There are exact and polynomial time solution algorithms for the shortest path problem in the literature. SPP has been extensively applied in real life application such as traffic system, the transit path of passenger, car navigation system and transport path of hazardous material and so on [2]. There is extensive research related shortest path problems in the literature.…”

Section: Introductionmentioning

confidence: 99%

Public Transportation Graph: A Graph Theoretical Model of Public Transportation Network for Efficient Trip Planning

Seri̇n¹,

Mete²

2019

Pamukkale J Eng Sci

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The presentation and usage of traditional graphs is very important for effective and fast solution of routing in public transportation. However, the traditional graph approach is unable to consider the passenger requests such as total travel time, minimum number of transfer and total distance of travel without pre-processing and/or post-processing. Moreover, the vehicles are not represented on traditional graph. In this paper, after analyzing the different kind of graphs, we propose a novel graph named as public transportation graph. The proposed graph models the public transportation system and considers distance, waiting time, travel time, self-transportation and number of transfers simultaneously for efficient trip planning. In this way, passenger requests can be met without pre-processing and post-processing. In addition, the vehicles are also considered and demonstrated in the proposed graph. Geleneksel çizgelerin sunumu ve kullanımı, toplu taşımada etkin ve hızlı bir yönlendirme çözümü için oldukça önemlidir. Ancak, geleneksel çizge yaklaşımı, ön ve/veya son işlem olmaksızın yolcuların toplam seyahat süresi, asgari transfer sayısı, toplam seyahat mesafesi gibi isteklerini dikkate alamamaktadır. Dahası, geleneksel çizgelerde araçlar temsil edilememektedir. Bu çalışmada, farklı çizge türleri incelendikten sonra, toplu taşıma çizgesi olarak isimlendirilen yeni bir çizge önerilmiştir. Önerilen çizge etkin bir seyahat planlaması için toplu taşıma sistemini modellemekte ve mesafe, bekleme süresi, seyahat süresi, kendi kendine ulaşım ile transfer sayısını aynı anda göz önünde bulundurabilmektedir. Bu sayede ön ve son işlem olmaksızın yolcu istekleri karşılanabilmektedir. Ayrıca, önerilen çizgede araçlar da göz önünde bulundurulmuş ve gösterilmiştir.

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“…Due to the complexity of constraints, both the MCOP and DMCOP problems constitute NP-hard problems [13,14]. into a unified framework should be constructed to reduce the transformation complexity of different constraints; (2) Due to the dynamically added constraints, the route generation strategy, which can dynamically integrate constraints during feasible path generation and optimal path searching, should be constructed [32]; (3) As the actual network problems are often large in scale, an efficient solution strategy should be carefully designed. Therefore, combing through the GA properties and analysis above, the specific ideas regarding the three steps are as follows.…”

Section: Introductionmentioning

confidence: 99%

Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

Luo

et al. 2018

IJGI

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The process of searching for a dynamic constrained optimal path has received increasing attention in traffic planning, evacuation, and personalized or collaborative traffic service. As most existing multiple constrained optimal path (MCOP) methods cannot search for a path given various types of constraints that dynamically change during the search, few approaches for dynamic multiple constrained optimal path (DMCOP) with type II dynamics are available for practical use. In this study, we develop a method to solve the DMCOP problem with type II dynamics based on the unification of various types of constraints under a geometric algebra (GA) framework. In our method, the network topology and three different types of constraints are represented by using algebraic base coding. With a parameterized optimization of the MCOP algorithm based on a greedy search strategy under the generation-refinement paradigm, this algorithm is found to accurately support the discovery of optimal paths as the constraints of numerical values, nodes, and route structure types are dynamically added to the network. The algorithm was tested with simulated cases of optimal tourism route searches in China's road networks with various combinations of constraints. The case study indicates that our algorithm can not only solve the DMCOP with different types of constraints but also use constraints to speed up the route filtering.Two different types of dynamics can increase the complexity of the DMCOP problem. With type I dynamics, the network condition, such as the weight or topology, changes during optimal path searching; the constraints are not altered. For example, in adaptive dynamic traffic navigation, the optimal path should be dynamically generated with the traffic conditions, where the weights are dynamically changed according to the current traffic status. With type II dynamics, the network conditions are not changed; however, the constraints used to restrict the searching of the optimal path change with time. One example is event-forced constraints, which are not determined unless a certain kind of event (e.g., emergency or user interactions) occurs. In this way, the constraints should be changed according to the specific schedule of the event dynamically occurring. Since these different constraints cannot be known in advance, these constraints should be added dynamically during route planning [15]. For instance, in a typical scenario of a long-distance self-driving tour, the navigation may first start from route planning with a start point and an end point. During the travel, users may add location constraints [16], reliability constraints [17], turn constraints [18] for intercity traffic and cycle routes [19], or satisfaction constraints [20]. Certain emergency cases may also exist that require the planned route to be dynamically adjusted in a short amount of time according to a series of constraints.Previous initial studies have addressed the solution of DMCOP with type I dynamics (i.e., network status changes) [21]. Schott and Staples ...

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An oriented spanning tree based genetic algorithm for multi-criteria shortest path problems

Cited by 32 publications

References 15 publications

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Public Transportation Graph: A Graph Theoretical Model of Public Transportation Network for Efficient Trip Planning

Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

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