Optimal Unmanned Aerial Vehicle Deployment for Macro-Micro Traffic Monitoring Fused with Connected Vehicles
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| Format: | Preprint |
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2025
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| _version_ | 1866912674039726080 |
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| author | Tan, Chaopeng Yao, Jiarong Wang, Meng |
| author_facet | Tan, Chaopeng Yao, Jiarong Wang, Meng |
| contents | Reliable estimation of macro and micro traffic states is essential for urban traffic management. Unmanned Aerial Vehicles, with their airborne full-sample continuous trajectory observation, bring new opportunities for macro- and micro-traffic state estimation. In this study, we will explore the optimal UAV deployment problem in road networks in conjunction with sampled connected vehicle data to achieve more reliable estimation of macroscopic path flow as well as microscopic arrival rates and queue lengths. Oriented towards macro-micro traffic states, we propose entropy-based and area-based uncertainty measures, respectively, and transform the optimal UAV deployment problem into minimizing the uncertainty of macro-micro traffic states. A quantum genetic algorithm that integrates the thoughts of metaheuristic algorithms and quantum computation is then proposed to solve the large-scale nonlinear problem efficiently. Evaluation results on a network with 18 intersections have demonstrated that by deploying UAV detection at specific locations, the uncertainty reduction of macro-micro traffic state estimation ranges from 15.28\% to 75.69\%. A total of 5 UAVs with optimal location schemes would be sufficient to detect over 95\% of the paths in the network considering both microscopic uncertainty regarding the intersection operation efficiency and the macroscopic uncertainty regarding the route choice of road users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Unmanned Aerial Vehicle Deployment for Macro-Micro Traffic Monitoring Fused with Connected Vehicles Tan, Chaopeng Yao, Jiarong Wang, Meng Optimization and Control Reliable estimation of macro and micro traffic states is essential for urban traffic management. Unmanned Aerial Vehicles, with their airborne full-sample continuous trajectory observation, bring new opportunities for macro- and micro-traffic state estimation. In this study, we will explore the optimal UAV deployment problem in road networks in conjunction with sampled connected vehicle data to achieve more reliable estimation of macroscopic path flow as well as microscopic arrival rates and queue lengths. Oriented towards macro-micro traffic states, we propose entropy-based and area-based uncertainty measures, respectively, and transform the optimal UAV deployment problem into minimizing the uncertainty of macro-micro traffic states. A quantum genetic algorithm that integrates the thoughts of metaheuristic algorithms and quantum computation is then proposed to solve the large-scale nonlinear problem efficiently. Evaluation results on a network with 18 intersections have demonstrated that by deploying UAV detection at specific locations, the uncertainty reduction of macro-micro traffic state estimation ranges from 15.28\% to 75.69\%. A total of 5 UAVs with optimal location schemes would be sufficient to detect over 95\% of the paths in the network considering both microscopic uncertainty regarding the intersection operation efficiency and the macroscopic uncertainty regarding the route choice of road users. |
| title | Optimal Unmanned Aerial Vehicle Deployment for Macro-Micro Traffic Monitoring Fused with Connected Vehicles |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.24384 |