Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles
This study introduces an approach for route optimization of many-to-many Demand-Responsive Transport (DRT) services. In contrast to conventional fixed-route transit systems, DRT provides dynamic, flexible, and cost-effective alternatives. We present an algorithm that integrates DRT with the autonomo...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10438424/ |
| _version_ | 1797296356672405504 |
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| author | Sunhyung Yoo Hyun Kim Jinwoo Lee |
| author_facet | Sunhyung Yoo Hyun Kim Jinwoo Lee |
| author_sort | Sunhyung Yoo |
| collection | DOAJ |
| description | This study introduces an approach for route optimization of many-to-many Demand-Responsive Transport (DRT) services. In contrast to conventional fixed-route transit systems, DRT provides dynamic, flexible, and cost-effective alternatives. We present an algorithm that integrates DRT with the autonomous shuttles at Korea National University of Transportation (KNUT), allowing dynamic route modifications in real-time to accommodate incoming service calls. The algorithm is designed to take into account the shuttle’s current position, the destinations of passengers already on board, the current locations and destinations of individuals who have requested shuttle services, and the remaining capacity of the shuttle. The algorithm has been developed to combine genetic algorithms and reinforcement learning. The performance evaluation was conducted using a simulation model that emulates KNUT’s campus and the adjoining local community area. The simulation results show significant improvements in two key metrics, specifically the ‘Request to Pick-up Time’ and ‘Request to Drop-off Time’ during high-demand periods over the single-shuttle operation. Additional simulation test with random service requests and stochastic passenger walking distances showed the potential adaptability across different settings. |
| first_indexed | 2024-03-07T22:03:28Z |
| format | Article |
| id | doaj.art-a78332cac134453c831a01eb783ee10e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T22:03:28Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a78332cac134453c831a01eb783ee10e2024-02-24T00:01:15ZengIEEEIEEE Access2169-35362024-01-0112269312694210.1109/ACCESS.2024.336651710438424Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous VehiclesSunhyung Yoo0Hyun Kim1Jinwoo Lee2https://orcid.org/0000-0002-4148-4115School of Built Environment, The University of New South Wales, Sydney, NSW, AustraliaDepartment of Computer Science and Information Engineering, Korea National University of Transportation, Chungju, South KoreaSchool of Built Environment, The University of New South Wales, Sydney, NSW, AustraliaThis study introduces an approach for route optimization of many-to-many Demand-Responsive Transport (DRT) services. In contrast to conventional fixed-route transit systems, DRT provides dynamic, flexible, and cost-effective alternatives. We present an algorithm that integrates DRT with the autonomous shuttles at Korea National University of Transportation (KNUT), allowing dynamic route modifications in real-time to accommodate incoming service calls. The algorithm is designed to take into account the shuttle’s current position, the destinations of passengers already on board, the current locations and destinations of individuals who have requested shuttle services, and the remaining capacity of the shuttle. The algorithm has been developed to combine genetic algorithms and reinforcement learning. The performance evaluation was conducted using a simulation model that emulates KNUT’s campus and the adjoining local community area. The simulation results show significant improvements in two key metrics, specifically the ‘Request to Pick-up Time’ and ‘Request to Drop-off Time’ during high-demand periods over the single-shuttle operation. Additional simulation test with random service requests and stochastic passenger walking distances showed the potential adaptability across different settings.https://ieeexplore.ieee.org/document/10438424/Autonomous vehiclesintelligent transportation systemsmachine learningmachine learning algorithmspublic transportationtransportation |
| spellingShingle | Sunhyung Yoo Hyun Kim Jinwoo Lee Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles IEEE Access Autonomous vehicles intelligent transportation systems machine learning machine learning algorithms public transportation transportation |
| title | Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles |
| title_full | Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles |
| title_fullStr | Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles |
| title_full_unstemmed | Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles |
| title_short | Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles |
| title_sort | combining reinforcement learning with genetic algorithm for many to many route optimization of autonomous vehicles |
| topic | Autonomous vehicles intelligent transportation systems machine learning machine learning algorithms public transportation transportation |
| url | https://ieeexplore.ieee.org/document/10438424/ |
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