An advanced GA–VNS combination for multicriteria route planning in public transit networks

Dib, Omar; Moalic, Laurent; Manier, Marié-Ange; Caminada, Alexandre

doi:10.1016/j.eswa.2016.12.009

Cited by 53 publications

(20 citation statements)

References 32 publications

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“…This array is scanned once per query, which is very efficient in practice. Dib et al proposed a heuristic approach whereby a Genetic Algorithm (GA) was combined with a Variable Neighborhood Search (VNS) to solve the multicriteria shortest-path problem in multimodal networks [25]. Experimental results show that the computational time is not prohibitive to integrate it within an online journey planning system.…”

Section: Related Workmentioning

confidence: 99%

A Gradual Approach for Multimodel Journey Planning: A Case Study in Izmir, Turkey

Dalkılıç

Doğan

Birant

et al. 2017

Journal of Advanced Transportation

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Planning a journey by integrating route and timetable information from diverse sources of transportation agencies such as bus, ferry, and train can be complicated. A user-friendly, informative journey planning system may simplify a plan by providing assistance in making better use of public transportation. In this study, we presented the service-oriented, multimodel Intelligent Journey Planning System, which we developed to assist travelers in journey planning. We selected Izmir, Turkey, as the pilot city for this system. The multicriteria problem is one of the well-known problems in transportation networks. Our study proposes a gradual path-finding algorithm to solve this problem by considering transfer count and travel time. The algorithm utilizes the techniques of efficient algorithms including round based public transit optimized router, transit node routing, and contraction hierarchies on transportation graph. We employed Dijkstra's algorithm after the first stage of the path-finding algorithm by applying stage specific rules to reduce search space and runtime. The experimental results show that our path-finding algorithm takes 0.63 seconds of processing time on average, which is acceptable for the user experience.

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Section: Related Workmentioning

confidence: 99%

A Gradual Approach for Multimodel Journey Planning: A Case Study in Izmir, Turkey

Dalkılıç

Doğan

Birant

et al. 2017

Journal of Advanced Transportation

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show abstract

“…In particular, it is of great significance for the improvement of the travel environment of vulnerable groups. However, the traditional public transport network optimization schemes are mostly optimized with the minimum passenger travel time, the highest passenger flow rate, the lowest line overlap factor, and the largest bus economic benefit [13][14][15][16][17][18][19][20], excluding the transportation equity. Therefore, in view of uneven urban transportation resources and differentiated spatial accessibility, this paper intends to use the Gini coefficient of road area and relative deprivation coefficient of travel cost to evaluate the utility of public transit network optimization scheme.…”

Section: Introductionmentioning

confidence: 99%

Bilevel Programming Model of Urban Public Transport Network under Fairness Constraints

Hao

Liu

Shen

et al. 2019

Discrete Dynamics in Nature and Society

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In this paper, the bilevel programming model of the public transport network considering factors such as the per capita occupancy area and travel cost of different groups was established, to alleviate the urban transportation equity and optimize the urban public transport network under fairness constraints. The upper layer minimized the travel cost deprivation coefficient and the road area Gini coefficient as the objective function, to solve the optimization scheme of public transport network considering fairness constraints; the lower layer was a stochastic equilibrium traffic assignment model of multimode and multiuser, used to describe the complex selection behavior of different groups for different traffic modes in the bus optimization scheme given by the upper layer. The model in addition utilised the noninferior sorting genetic algorithm II to validate the model via a simple network. The results showed that (1) the travel cost deprivation coefficient of the three groups declined from 33.42 to 26.51, with a decrease of 20.68%; the Gini coefficient of the road area declined from 0.248 to 0.030, with a decrease of 87.76%; it could be seen that the transportation equity feeling of low-income groups and objective resource allocation improved significantly; (2) before the optimization of public transport network, the sharing rate of cars, buses, and bicycles was 42%, 47%, and 11%, respectively; after the optimization, the sharing rate of each mode was 7%, 82%, and 11%, respectively. Some of the high and middle income users who owned the car were transferred to the public transportation. It could be seen that the overall travel time of the optimized public transport network reduced, enhancing the attraction of the public transport network to various travel groups. The model improves the fairness of the urban public transport system effectively while ensuring the travel demand of the residents. It provides theoretical basis and model foundation for the optimization of public transit network, and it is a new attempt to improve the fairness of the traffic planning scheme.

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“…Nowadays, passengers in urban public transport systems do not only seek a shorter travelling time but they also ask for optimizing other criteria such as cost and effort (O. Dib, 2016). Time being one of the most valuable assets, people want to use their time in optimal way.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is a real need to develop a seamless routing application that provides passengers with efficient itineraries according to their needs and preferences (O. Dib, 2016).…”

Section: Introductionmentioning

confidence: 99%

Optimal Route Computation for Public Transport with Minimum Travelling Time & Travel Cost: A Case Study of Pokhara City

Thapa

Shrestha

2019

Tech J

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In road networks, it is imperative to discover a shortest way to reach the final destination. When an individual is new to a place, lots of time is wasted in finding the destination. With the advancement of technology, various navigation applications have been developed for guiding private vehicles, but few are designed for public transportation. This study is solely concentrated on finding the possible shortest path in terms of minimum time and cost to reach specific destination for an individual. It requires an appropriate algorithm to search the shortest path. With the implementation of Dijkstra’s algorithm, the shortest path with respect to minimum travel time and travel cost was computed. Public transportation network of Pokhara city was taken for the case study of this research. The results of this analysis indicated that when the “time” impedance was used by the algorithm, it generated the shortest path between the origin and destination along with the path to be followed. This study formulates a framework for generating itinerary for passengers in a transit network that allows the user to find the optimal path with minimum travel time and cost.

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An advanced GA–VNS combination for multicriteria route planning in public transit networks

Cited by 53 publications

References 32 publications

A Gradual Approach for Multimodel Journey Planning: A Case Study in Izmir, Turkey

A Gradual Approach for Multimodel Journey Planning: A Case Study in Izmir, Turkey

Bilevel Programming Model of Urban Public Transport Network under Fairness Constraints

Optimal Route Computation for Public Transport with Minimum Travelling Time & Travel Cost: A Case Study of Pokhara City

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