Integrated optimization of collection point selection, UAV routing, and upload scheduling for UAV-aided data collection

Unmanned aerial vehicles (UAVs) provide a flexible means of collecting data from spatially distributed sensor networks in intelligent transportation systems. This study investigates a UAV-assisted data collection problem that jointly optimizes collection point (CP) selection, UAV routing, and data uploading scheduling with the objective of reducing the Age of Information (AoI). The problem captures the interdependence among spatial collection decisions, UAV movement, and temporal uploading schedules, which is often simplified in existing studies. A mixed-integer programming model is formulated to represent the integrated decision process. To solve the problem efficiently, two metaheuristic algorithms are developed: a biased random-key genetic algorithm (BRKGA) and a differential evolution (DE) algorithm. Computational experiments are conducted to evaluate the proposed model and solution methods. The results show that the integrated optimization framework can effectively improve data freshness, and that BRKGA provides the best overall performance among the tested approaches, outperforming both CPLEX and DE in solution quality and computational efficiency as the problem size increases. These findings provide practical implications for the design and deployment of UAV-assisted data collection systems in large-scale transportation sensing environments.