|Ph.D Student||Zatmeh Kanj Sunbola|
|Subject||Traffic Simulation Models for Road Safety Studies|
|Department||Department of Civil and Environmental Engineering||Supervisor||Professor Tomer Toledo|
|Full Thesis text|
Engagement in distracting activities is among the leading causes of car crashes. In recent years, there has been a rapid increase in the availability of smartphones and other connected and infotainment devices, also within the vehicle, and in their widespread use even when driving. This trend exacerbates their potential negative effects on driving.
Microscopic simulation models have been widely used as tools to investigate the operation of traffic systems and different Intelligent Transportation Systems (ITS) applications. The fidelity of microscopic simulation tools depends on the driving behavior models that they implement. However, current models commonly do not take into account human-related factors, such as distraction. The potential for distraction while driving has increased rapidly with the availability of smartphones and other connected and infotainment devices. Thus, an understanding of the impact of distraction on driving behavior is essential in order to improve the realism of microscopic traffic tools, and to support the development of effective technology and policy solutions to mitigate its potential risk.
This study focuses on the car-following behavior in the context of distracting activities. The parameters of the well-known GM, IDM and Helly models are estimated under various distraction scenarios using data collected with an experiment conducted with a driving simulator. The estimation results show that drivers are less sensitive to their leaders while talking on the phone and especially while texting. The estimated models are implemented in a microscopic traffic simulation model. The average speed, coefficient of variation of speed, acceleration noise and acceleration and deceleration time fractions were used as measures of performance indicating on traffic flow and safety implications. The simulation results show deterioration of traffic flow with texting and to some extent talking on the phone: average speeds are lower and the coefficient of variation of speeds are higher. Further experimentation with varying fractions of texting drivers showed similar trends.