  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
attracts significant attention lately. Calibration of car-following model
parameters is the prevalent way to simulate different driving behaviors through
randomly injected variation around average parameter values. An issue is that,
as shown in the literature, most car-following model do not realistically
reproduce free-flow acceleration, that is in turn highly correlated with
heterogeneity in driving styles. Furthermore, often, model parameters lose
their physical interpretation upon calibration. The present study proposes a
novel framework to analyse observed vehicle trajectories from various drivers,
identify individual driver fingerprints based on their acceleration behavior,
cluster drivers in categorically-meaningful driving styles (e.g. mild, normal
and aggressive) and reproduce observed individual driver acceleration behaviors
in microsimulation.
