Driving Heterogeneity in Traffic Flow Theory
An Action-based Framework for Identification, Modelling, and Simulation
X. Yao (TU Delft - Traffic Systems Engineering)
S.P. Hoogendoorn – Promotor (TU Delft - Traffic Systems Engineering)
S.C. Calvert – Copromotor (TU Delft - Traffic Systems Engineering)
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Abstract
Human driving behaviour is inherently heterogeneous, shaping traffic dynamics and affecting traffic safety, efficiency and sustainbility. This dissertation develops an interpretable, AI-driven framework to identify, model, and simulate heterogeneous driving behaviour using naturalistic data. By analysing action phases, patterns, and behavioural sequences, it reveals how behavioural variability influences traffic performance and supports improved traffic management, personalised driver assistance, and human-aware autonomous vehicle design.