A UAV-Based Real-Time Traffic State Estimation System for Urban Road Networks

EasyChair Preprint 15023

Abstract

Unmanned Aerial Vehicles (UAVs) offer advantages as traffic monitoring sensors compared to traditional sensors, such as enhanced quality of measurements. However, the scalability of such systems for large urban traffic road networks has been questioned. To circumvent this issue, we propose a UAV-based Traffic Estimation System (UAV-TES), which leverages real-time traffic measurements from UAVs to estimate traffic densities across observed and unobserved areas of an urban traffic road network. This is achieved through Gaussian Process modelling to handle data sparsity, as well as moving horizon estimation that incorporates non-linear, a-priori knowledge of traffic dynamics. The proposed solution is validated through macroscopic simulations. The results show that accurate traffic density estimates are achieved in real-time even under challenging conditions such as noisy measurements and sparsity of data due to a limited number of UAVs.

Keyphrases: Gaussian process model, Succesive Convexification, Urban Traffic Modelling, convex optimization, moving horizon estimation

@booklet{EasyChair:15023,
  author    = {Kyriacos Theocharides and Yiolanda Englezou and Charalambos Menelaou and Stelios Timotheou},
  title     = {A UAV-Based Real-Time Traffic State Estimation System for Urban Road Networks},
  howpublished = {EasyChair Preprint 15023},
  year      = {EasyChair, 2024}}