Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
Transportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dynamics of urban mobility, strategically selecting a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/806 |
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| author | Mehmet Bilge Han Taş Kemal Özkan İnci Sarıçiçek Ahmet Yazici |
| author_facet | Mehmet Bilge Han Taş Kemal Özkan İnci Sarıçiçek Ahmet Yazici |
| author_sort | Mehmet Bilge Han Taş |
| collection | DOAJ |
| description | Transportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dynamics of urban mobility, strategically selecting a transportation mode can significantly mitigate delays and enhance overall productivity in densely populated areas. The objective of this study is to find the most efficient result among various transportation modes to make deliveries from different points on a university campus. To solve this problem, reinforcement learning was used and tested on the simulation environment SUMO. Traffic density was increased by using an equal number of different transportation modes, such as driving, cycling, motorbiking, and walking. Various traffic densities were generated, and different reward models were applied to select the best means of transportation. Various probability distributions were used as reward models to avoid the unfair distribution caused by how near or how far the road is when moving from random points to the destination region. As a result of the models created using the applied reward–penalty functions, it was determined that the best means of transportation in areas with a low traffic density is cycling, and in areas with high traffic density, the optimal mode of transportation is motorbiking. |
| format | Article |
| id | doaj-art-a28c42fc6f6f424fb5041a4ceb7a6e43 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a28c42fc6f6f424fb5041a4ceb7a6e432025-01-24T13:20:53ZengMDPI AGApplied Sciences2076-34172025-01-0115280610.3390/app15020806Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban MobilityMehmet Bilge Han Taş0Kemal Özkan1İnci Sarıçiçek2Ahmet Yazici3Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, TürkiyeDepartment of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, TürkiyeThe Center for Intelligent Systems Applications Research (CISAR), Eskisehir 26040, TürkiyeDepartment of Computer Engineering, Eskisehir Osmangazi University, Eskisehir 26040, TürkiyeTransportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dynamics of urban mobility, strategically selecting a transportation mode can significantly mitigate delays and enhance overall productivity in densely populated areas. The objective of this study is to find the most efficient result among various transportation modes to make deliveries from different points on a university campus. To solve this problem, reinforcement learning was used and tested on the simulation environment SUMO. Traffic density was increased by using an equal number of different transportation modes, such as driving, cycling, motorbiking, and walking. Various traffic densities were generated, and different reward models were applied to select the best means of transportation. Various probability distributions were used as reward models to avoid the unfair distribution caused by how near or how far the road is when moving from random points to the destination region. As a result of the models created using the applied reward–penalty functions, it was determined that the best means of transportation in areas with a low traffic density is cycling, and in areas with high traffic density, the optimal mode of transportation is motorbiking.https://www.mdpi.com/2076-3417/15/2/806transportation mode selectionreinforcement learningGaussian distributionPoisson distributionsimulation of urban mobility (SUMO) |
| spellingShingle | Mehmet Bilge Han Taş Kemal Özkan İnci Sarıçiçek Ahmet Yazici Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility Applied Sciences transportation mode selection reinforcement learning Gaussian distribution Poisson distribution simulation of urban mobility (SUMO) |
| title | Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility |
| title_full | Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility |
| title_fullStr | Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility |
| title_full_unstemmed | Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility |
| title_short | Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility |
| title_sort | transportation mode selection using reinforcement learning in simulation of urban mobility |
| topic | transportation mode selection reinforcement learning Gaussian distribution Poisson distribution simulation of urban mobility (SUMO) |
| url | https://www.mdpi.com/2076-3417/15/2/806 |
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