Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information
The stochastic routing transportation network problem presents significant challenges due to uncertainty in travel times, real-time variability, and limited sensor data availability. Traditional adaptive routing strategies, which rely on real-time travel time updates, may lead to suboptimal decision...
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2262 |
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| author | Ahmed Almutairi Mahmoud Owais |
| author_facet | Ahmed Almutairi Mahmoud Owais |
| author_sort | Ahmed Almutairi |
| collection | DOAJ |
| description | The stochastic routing transportation network problem presents significant challenges due to uncertainty in travel times, real-time variability, and limited sensor data availability. Traditional adaptive routing strategies, which rely on real-time travel time updates, may lead to suboptimal decisions due to dynamic traffic fluctuations. This study introduces a novel routing framework that integrates traffic sensor data augmentation and deep learning techniques to improve the reliability of route selection and network observability. The proposed methodology consists of four components: stochastic traffic assignment, multi-objective route generation, optimal traffic sensor location selection, and deep learning-based traffic flow estimation. The framework employs a traffic sensor location problem formulation to determine the minimum required sensor deployment while ensuring an accurate network-wide traffic estimation. A Stacked Sparse Auto-Encoder (SAE) deep learning model is then used to infer unobserved link flows, enhancing the observability of stochastic traffic conditions. By addressing the gap between limited sensor availability and complete network observability, this study offers a scalable and cost-effective solution for real-time traffic management and vehicle routing optimization. The results confirm that the proposed data-driven approach significantly reduces the need for sensor deployment while maintaining high accuracy in traffic flow predictions. |
| format | Article |
| id | doaj-art-d1c4053f1b124893b3040e15dcb32d25 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d1c4053f1b124893b3040e15dcb32d252025-08-20T02:09:21ZengMDPI AGSensors1424-82202025-04-01257226210.3390/s25072262Reliable Vehicle Routing Problem Using Traffic Sensors Augmented InformationAhmed Almutairi0Mahmoud Owais1Department of Civil and Environmental Engineering, Majmaah University, Al-Majmaah 11952, Saudi ArabiaCivil Engineering Department, Faculty of Engineering, Assiut University, Assiut 71515, EgyptThe stochastic routing transportation network problem presents significant challenges due to uncertainty in travel times, real-time variability, and limited sensor data availability. Traditional adaptive routing strategies, which rely on real-time travel time updates, may lead to suboptimal decisions due to dynamic traffic fluctuations. This study introduces a novel routing framework that integrates traffic sensor data augmentation and deep learning techniques to improve the reliability of route selection and network observability. The proposed methodology consists of four components: stochastic traffic assignment, multi-objective route generation, optimal traffic sensor location selection, and deep learning-based traffic flow estimation. The framework employs a traffic sensor location problem formulation to determine the minimum required sensor deployment while ensuring an accurate network-wide traffic estimation. A Stacked Sparse Auto-Encoder (SAE) deep learning model is then used to infer unobserved link flows, enhancing the observability of stochastic traffic conditions. By addressing the gap between limited sensor availability and complete network observability, this study offers a scalable and cost-effective solution for real-time traffic management and vehicle routing optimization. The results confirm that the proposed data-driven approach significantly reduces the need for sensor deployment while maintaining high accuracy in traffic flow predictions.https://www.mdpi.com/1424-8220/25/7/2262stochastic routingdeep learningtraffic sensorstraffic flow estimationvehicle routing optimization |
| spellingShingle | Ahmed Almutairi Mahmoud Owais Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information Sensors stochastic routing deep learning traffic sensors traffic flow estimation vehicle routing optimization |
| title | Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information |
| title_full | Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information |
| title_fullStr | Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information |
| title_full_unstemmed | Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information |
| title_short | Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information |
| title_sort | reliable vehicle routing problem using traffic sensors augmented information |
| topic | stochastic routing deep learning traffic sensors traffic flow estimation vehicle routing optimization |
| url | https://www.mdpi.com/1424-8220/25/7/2262 |
| work_keys_str_mv | AT ahmedalmutairi reliablevehicleroutingproblemusingtrafficsensorsaugmentedinformation AT mahmoudowais reliablevehicleroutingproblemusingtrafficsensorsaugmentedinformation |