Multiobjective route finding in a multimode transportation network by NSGA-II
Abstract Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimiza...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-03-01
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| Series: | Journal of Engineering and Applied Science |
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| Online Access: | https://doi.org/10.1186/s44147-024-00417-7 |
| _version_ | 1797233515508531200 |
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| author | Hamed Faroqi |
| author_facet | Hamed Faroqi |
| author_sort | Hamed Faroqi |
| collection | DOAJ |
| description | Abstract Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm. |
| first_indexed | 2024-04-24T16:17:24Z |
| format | Article |
| id | doaj.art-3c8d06dccfc54b589538c0aeb39e96f9 |
| institution | Directory Open Access Journal |
| issn | 1110-1903 2536-9512 |
| language | English |
| last_indexed | 2024-04-24T16:17:24Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj.art-3c8d06dccfc54b589538c0aeb39e96f92024-03-31T11:22:06ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122024-03-0171111610.1186/s44147-024-00417-7Multiobjective route finding in a multimode transportation network by NSGA-IIHamed Faroqi0School of Civil Engineering, University of KurdistanAbstract Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm.https://doi.org/10.1186/s44147-024-00417-7HeuristicsGenetic algorithmsMultiple objective programmingDecision support systems |
| spellingShingle | Hamed Faroqi Multiobjective route finding in a multimode transportation network by NSGA-II Journal of Engineering and Applied Science Heuristics Genetic algorithms Multiple objective programming Decision support systems |
| title | Multiobjective route finding in a multimode transportation network by NSGA-II |
| title_full | Multiobjective route finding in a multimode transportation network by NSGA-II |
| title_fullStr | Multiobjective route finding in a multimode transportation network by NSGA-II |
| title_full_unstemmed | Multiobjective route finding in a multimode transportation network by NSGA-II |
| title_short | Multiobjective route finding in a multimode transportation network by NSGA-II |
| title_sort | multiobjective route finding in a multimode transportation network by nsga ii |
| topic | Heuristics Genetic algorithms Multiple objective programming Decision support systems |
| url | https://doi.org/10.1186/s44147-024-00417-7 |
| work_keys_str_mv | AT hamedfaroqi multiobjectiveroutefindinginamultimodetransportationnetworkbynsgaii |