Abstract: The driving behavior in area-based (i.e., non-lane based) traffic is induced by the presence of other individuals in the choice space from the driver’s visual perception area. The driving behavior of a subject vehicle is constrained by the potential leaders and leaders are frequently changed over time. This paper is to determine a stochastic model for a parameter of modified intelligent driver model (MIDM) in area-based traffic (as in developing countries). The parametric and non-parametric distributions are presented to fit the parameters of MIDM. The goodness of fit for each parameter is measured in two different ways such as graphically and statistically. The quantile-quantile (Q-Q) plot is used for a graphical representation of a theoretical distribution to model a parameter and the Kolmogorov-Smirnov (K-S) test is used for a statistical measure of fitness for a parameter with a theoretical distribution. The distributions are performed on a set of estimated parameters of MIDM. The parameters are estimated on the real vehicle trajectory data from India. The fitness of each parameter with a stochastic model is well represented. The results support the applicability of the proposed modeling for parameters of MIDM in area-based traffic flow simulation.
Abstract: This paper develops driver reaction-time models for
car-following analysis based on human factors. The reaction time
was classified as brake-reaction time (BRT) and
acceleration/deceleration reaction time (ADRT). The BRT occurs
when the lead vehicle is barking and its brake light is on, while the
ADRT occurs when the driver reacts to adjust his/her speed using the
gas pedal only. The study evaluates the effect of driver
characteristics and traffic kinematic conditions on the driver reaction
time in a car-following environment. The kinematic conditions
introduced urgency and expectancy based on the braking behaviour
of the lead vehicle at different speeds and spacing. The kinematic
conditions were used for evaluating the BRT and are classified as
normal, surprised, and stationary. Data were collected on a driving
simulator integrated into a real car and included the BRT and ADRT
(as dependent variables) and driver-s age, gender, driving experience,
driving intensity (driving hours per week), vehicle speed, and
spacing (as independent variables). The results showed that there was
a significant difference in the BRT at normal, surprised, and
stationary scenarios and supported the hypothesis that both urgency
and expectancy had significant effects on BRT. Driver-s age, gender,
speed, and spacing were found to be significant variables for the
BRT in all scenarios. The results also showed that driver-s age and
gender were significant variables for the ADRT. The research
presented in this paper is part of a larger project to develop a driversensitive
in-vehicle rear-end collision warning system.