Estimating Crash Risk Using a Microscopic Traffic Model

In this research, a microscopic model is developed that combines car following and lane changing models, describing driver behavior as a utility maximization process of drivers for reducing crash risk and increasing speed. this model is simulated by a cellular automata simulator and compared with the real data. it is shown that there is no reason to consider the model invalid for driver behavior in basic segments of the freeways in iran, under non-congested conditions. considering that the uncertain position of vehicles is caused by their acceleration or deceleration, a probability function is calibrated for calculating the presence probability of vehicles in their feasible cells. by multiplying the presence probability and impact of a crash, the crash risk of cells is calculated. an idea for estimating the crash risk of vehicles is introduced, named total risk. total risk is the sum of risks on the path of the considered vehicles. it is shown that, when the difference between vehicle characteristics such as brake deceleration increases, crash risk also increases, and vice versa.