Modeling cars and trucks in the heterogeneous traffic based on car–truck combination effect using cellular automata

An improved cellular automata model based on car–truck combination effect oriented to faithfully reproducing the characteristics of heterogeneous traffic flow is presented in this paper. The model focuses on reflecting complex car-following and lane-changing behavior of cars and trucks by incorporating the difference of physical performance and driving behavior between cars and trucks and the behavioral change caused by car–truck interaction. The update rules of cars and trucks were constructed separately based on the analysis results of NGSIM and SP survey. The impact of the truck in front was introduced into the model, which can affect the lane-changing probability, safety expectation and randomization probability. As revealed by the simulation results, the driving of vehicles in the heterogeneous traffic (i.e. car–truck, truck–car, car–car and truck–truck) is reproduced properly through the model. In addition, the model is capable of reproducing the hysteresis phenomenon and identifying three typical traffic states, namely free flow, synchronized flow and jam. Under certain conditions, trucks can help to eliminate the propagation of congestion waves. Moreover, the percentage of trucks and truck impact have a significant influence on the fundamental diagram, congestion rate, lane-changing rate and traffic stability. The findings of this study can provide a basis for further research on the heterogeneous traffic.