Analysis of car–following behaviors based on data–driven and theory–driven car–following models: Heterogeneity and asymmetry

The data–driven car–following model can realistically depict driving behavior, and is widely used in simulating the longitudinal movements of vehicles. However, it has some unexpected results such as uncontrollable output and potential security risks, making it challenging to apply practically. To address this issue, this paper proposes a combination model that integrates the theory–driven model (IDM) with the data–driven model (PSO–Bi–LSTM). This novel integration is referred to as the IDM–Bi–LSTM combination car–following model. This combination model offers two key features. First, to accurately predict vehicle trajectories, rather than manually configuring the number of neurons in the hidden layer and iterations, we employ the Particle Swarm Optimization (PSO) algorithm to optimize these parameters in the data–driven model. Consequently, we establish a PSO–Bi–LSTM data–driven model framework. Second, our combination model integrates the prediction outcomes of the IDM model with those of the PSO–Bi–LSTM neural network. This amalgamation retains the controllability and security offered by the theory–driven model, while also leveraging the prediction accuracy of the data–driven model. To evaluate the effectiveness of our model, we conduct numerical simulations of heterogeneous driving behaviors. The results reveal a significant reduction in error for the combination car–following model, exhibiting reductions of 60.2% and 54.4% compared to the individual IDM theory–driven model and PSO–Bi–LSTM data–driven model, respectively. Additionally, the simulation outcomes for asymmetric driving behavior further confirm the superior performance of the combination model.