A cellular automata model for car–truck heterogeneous traffic flow considering drivers’ risky driving behaviors

Abstract

Driving behavior is the core of traffic simulation modeling. Despite the sizable efforts to investigate the influence of risky driving behavior on traffic flow, very little is known about its influence under car–truck heterogeneous traffic conditions. Therefore, this study aims to analyze the influence of several common risky driving behaviors on car–truck heterogeneous traffic flow. A two-lane cellular automata (CA) model for heterogeneous traffic flow consisting of cars and trucks was proposed, the conditions for the occurrence of dangerous situations (DSs) and traffic congestion were defined. Then the vehicle performance under the influence of risky driving behaviors prevalent in car–truck heterogeneous traffic was obtained and incorporated into the proposed CA model to measure their influence on crash probabilities and congestion rates. The simulation results indicate that risky driving behaviors have a significant negative influence on traffic safety and efficiency, increasing the frequencies of DSs, reducing the traffic flow and aggravating the traffic congestion. In addition, the combinations of risky driving behaviors in traffic flow further deteriorate the traffic safety and efficiency. For example, when two risky driving behaviors, such as distracted driving and aggressive lane-changing, affect the traffic flow simultaneously, the frequencies of DSs increase dramatically and in some density ranges, they are even higher than the sum of the frequencies caused by two single risky driving behaviors. These findings may have notable practical implications in developing policies and regulations that enable truck operations without compromising safety and efficiency.