Citation

Makridis MA, Kouvelas A. Transp. Res. C Emerg. Technol. 2023; 149: e104066.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trc.2023.104066

PMID

unavailable

Abstract

Advancements in sensor technologies, vehicle automation, communication, and intelligent transportation systems create unforeseen possibilities for the development of novel traffic management approaches in road transport systems. Furthermore, data observations with different accuracy and noise levels are fused towards advanced traffic state estimators. This work builds on the existing family of data assimilation techniques in the literature and proposes an online adaptive framework, fusing observations from static and moving sensors, along with estimations inferred from a traffic flow model and performing real-time traffic estimation in the presence of Connected and Automated Vehicles (CAVs). A real-world case study was used for validation and assessment of the proposed framework against well-known methodologies in the literature. The benefits and downsides of each approach for different scenarios are discussed, as well as the performance of each framework for different traffic models and penetration rates of CAVs.

Language: en

Keywords

Adaptive unscented Kalman Filter; Connected and automated vehicles; Data assimilation; Data fusion; Intelligent transportation systems; Online freeway traffic estimation