research-article

An advanced GAVNS combination for multicriteria route planning in public transit networks

Authors:

A. CaminadaAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 72, Issue C

Pages 67 - 82

https://doi.org/10.1016/j.eswa.2016.12.009

Published: 15 April 2017 Publication History

Abstract

A modeling approach for representing a public transit network is proposed.The multicriteria shortest path problem in public transit networks is addressed.A new hybrid GAVNS approach is introduced to solve the multicriteria shortest path problem.The proposed GAVNS is more efficient than the algorithm of Dijkstra, a pure GA and a pure VNS.An efficient real world routing system has been developed using the proposed GAVNS algorithm. Nowadays, passengers in urban public transport systems do not only seek a short-time travel, but they also ask for optimizing other criteria such as cost and effort. Therefore, an efficient routing system should incorporate a multiobjective analysis into its search process. Several algorithms have been proposed to optimally compute the set of nondominated journeys while going from one place to another such as the generalization of the algorithm of Dijkstra. However, such approaches become less performant or even inapplicable when the size of the network becomes very large or when the number of criteria considered is very important. Therefore, we propose in this paper an advanced heuristic approach whereby a Genetic Algorithm (GA) is combined with a Variable Neighborhood Search (VNS) to solve the Multicriteria Shortest Path Problem (MSPP) in multimodal networks. As transportation modes, we focus on railway, bus, tram and pedestrian. As optimization criteria, we consider travel time, monetary cost, number of transfers and the total walking time. The proposed approach is compared with the exact algorithm of Dijkstra, as well as, with a standard GA and a pure VNS. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that the proposed combination GAVNS represents the best approach in terms of computational time and solutions quality for a real world routing system.

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Cited By

Li XChen NMa HNie FWang X(2024)A Parallel Genetic Algorithm With Variable Neighborhood Search for the Vehicle Routing Problem in Forest Fire-FightingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339593025:10(14359-14375)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3395930
Chu KGuo W(2024)Multi-Agent Reinforcement Learning-Based Passenger Spoofing Attack on Mobility-as-a-ServiceIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.337928321:6(5565-5581)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TDSC.2024.3379283
Pradhan RAgarwal ADe T(2022)A Multi-Criteria Route Selection Method for Vehicles Using Genetic Algorithms Based on Driver’s PreferenceWireless Personal Communications: An International Journal10.1007/s11277-022-09670-6125:3(2477-2496)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11277-022-09670-6
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An advanced GAVNS combination for multicriteria route planning in public transit networks
1. Computing methodologies
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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 72, Issue C

April 2017

455 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 April 2017

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Cited By

Li XChen NMa HNie FWang X(2024)A Parallel Genetic Algorithm With Variable Neighborhood Search for the Vehicle Routing Problem in Forest Fire-FightingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339593025:10(14359-14375)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3395930
Chu KGuo W(2024)Multi-Agent Reinforcement Learning-Based Passenger Spoofing Attack on Mobility-as-a-ServiceIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.337928321:6(5565-5581)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TDSC.2024.3379283
Pradhan RAgarwal ADe T(2022)A Multi-Criteria Route Selection Method for Vehicles Using Genetic Algorithms Based on Driver’s PreferenceWireless Personal Communications: An International Journal10.1007/s11277-022-09670-6125:3(2477-2496)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11277-022-09670-6
Jin ZDib OLuo YHu B(2021)A Non-Dominated Sorting Memetic Algorithm for the Multi-Objective Travelling Salesman ProblemProceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence10.1145/3508546.3508565(1-6)Online publication date: 22-Dec-2021
https://dl.acm.org/doi/10.1145/3508546.3508565
Mousavi MHajiaghaei–Keshteli MTavakkoli–Moghaddam R(2020)Two calibrated meta-heuristics to solve an integrated scheduling problem of production and air transportation with the interval due dateSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04948-y24:21(16383-16411)Online publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1007/s00500-020-04948-y
Dib OCaminada AManier MMoalic L(2017)A memetic algorithm for computing multicriteria shortest paths in stochastic multimodal networksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3076064(103-104)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3076064

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