A Car-following Model Based on Discrete Choice Theory

In the car-following model proposed in this paper, the acceleration and deceleration are incorporated into seven alternatives, and alternative-specific parameters are adopted to capture different attractiveness of each alternative. To avoid interferences from lane-changing behavior, the model is estimated and validated by trajectory data in the high occupancy vehicle (HOV) lane. From the estimation results, most estimated parameters are significantly different from zero at 95% confidence level, which indicates that the chosen variables and the classification of alternatives are effective and reasonable. In addition, according to the proposed model, the changing tendencies of choosing different alternatives at different speeds and gaps are completely consistent with common driving behaviors. The validation results show that, for the chosen alternatives, the percentages of predicted probabilities that are larger than 1/7 (the average probability of choosing each alternative) are more than 87% in the used data sets. From the shares of observations point of view, there are only minor differences between the observed and predicted results. Finally, this model is applied to simulate the 30-min single lane traffic conditions. Through simulation results, it can be found that the proposed model can accurately represent the real traffic conditions on the macroscopic level, while, on the microscopic level, there are some defects needed to be further improved.