Characterising driver heterogeneity within stochastic traffic simulation. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Makridis, Michail A.; Anesiadou, Aikaterini; Mattas, Konstantinos; Fontaras, Georgios; Ciuffo, Biagio
• Is Part Of: Transportmetrica. Volume 11:Issue 1(2023)
• Page Start: 725
• Page End: 743
• Publication Date: 2023-12-31
• Subjects: Drivers' heterogeneity -- driving behaviour -- stochastic traffic simulation -- vehicles' dynamics -- microscopic free-flow acceleration model (MFC); Transportation -- Mathematical models -- Periodicals; 388.015118
• DOI: 10.1080/21680566.2022.2125458
• Languages: English
• ISSNs: 2168-0566

Abstract

ABSTRACT: Drivers' heterogeneity and the broad range of vehicle characteristics on public roads are primarily responsible for the stochasticity observed in road traffic dynamics. Understanding the behavioural differences in drivers (human or automated systems) and reproducing observed behaviours in microsimulation has lately attracted significant attention. Calibration of car-following model parameters is the prevalent way to chracterize different driving behaviours. However, most car-following models do not realistically reproduce free-flow accelerations and therefore, model parameters are usually mainly the result of over-fitting with limited possibility to reproduce realistic drivers' heterogeneity in simulation. To solve this problem, the present study proposes a novel framework to identify individual driver fingerprints based on their acceleration behaviours and reproduce them in microsimulation. The paper also discusses the unsuitability of vehicle acceleration to properly characterise alone the aggressiveness of a driver. A large experimental campaign and simulation results demonstrate the robustness of the proposed method.