Cellular automaton model with the multi-anticipative effect to reproduce the empirical findings of Kerner's three-phase traffic theory

As the important spatiotemporal state of traffic flow discovered by Kerner's three-phase traffic theory, the synchronized traffic flow describes a new traffic phase in congested traffic. However, until now most models within the standard traffic theories cannot reproduce it. The average space gap model (ASGM) is a simple cellular automaton model aimed to reproduce various empirical findings discovered by Kerner's three-phase traffic theory by incorporating the influence of the multi-anticipative effect on vehicle's deceleration. However, this paper shows that the simulated synchronized flow by ASGM is not consistent with the reality. To this end, the Multi-Anticipative Model (MAM) based on ASGM is proposed, which describes the influence of the multi-anticipative effect on both the accelerations and the decelerations. Simulations indicate that the empirical consistent synchronized flow and related congested patterns can be well reproduced by MAM. Moreover, MAM can reproduce the speed drop in the car following vehicle platoons reported by the empirical observations. Generally, MAM indicates that the multi-anticipative effect can shed light on the understanding and capturing the complex characteristics of traffic flow especially reported by Kerner's three-phase traffic theory. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper investigates the importance of synchronized traffic flow in traffic flow theory and the limitations of existing models in reproducing it. A multi-anticipative model based on the average space gap model is proposed, and simulation experiments demonstrate that this model can effectively reproduce synchronized traffic flow and related congestion patterns.