Collaborating Unmanned Aerial Vehicle and Ground Sensors for Urban Signalized Network Traffic Monitoring

TL;DR

This paper presents an integrated UAV and ground sensor system for urban traffic monitoring, introducing an optimized deployment method and an improved genetic algorithm to enhance traffic state estimation accuracy and efficiency.

Contribution

It develops a novel UAV deployment optimization framework combined with a new quantum genetic algorithm to improve urban traffic monitoring accuracy.

Findings

UAV fleet of 7 suffices for effective traffic monitoring

Over 60% reduction in network-wide observation uncertainty

IQGA outperforms classic QGA in convergence speed and solution quality

Abstract

Reliable estimation of network-wide traffic states is essential for urban traffic management. Unmanned Aerial Vehicles (UAVs), with their airborne full-sample continuous trajectory observation, bring new opportunities for traffic state estimation. In this study, we will explore the optimal UAV deployment problem in road networks in conjunction with ground sensors, including connected vehicle (CV) and loop detectors, to achieve more reliable estimation of vehicle path reconstruction as well as movement-based arrival rates and queue lengths. Oriented towards reliable estimation of traffic states, we propose an index, feasible domain size, as the uncertainty measurement, and transform the optimal UAV deployment problem into minimizing the observation uncertainty of network-wide traffic states. Given the large-scale and nonlinear nature of the problem, an improved quantum genetic algorithm (IQGA) that integrates two customized operators is proposed to enhance neighbor searching and solution refinement, thereby improving the observability of UAV pairs. Evaluation was conducted on an empirical network with 18 intersections. Results demonstrated that a UAV fleet size of 7 is sufficient for traffic monitoring, with more than 60\% of network-wide observation uncertainty reduced. Through horizontal comparison with three baselines, the optimal UAV location scheme obtained by the proposed method can reach an improvement of up to 7.23\% and 5.02\% in the estimation accuracy of arrival rate and queue length, respectively. The proposed IQGA is also shown to be faster in solution convergence than the classic QGA by about 9.22\% with better exploration ability in optimum searching.