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Zhang, W.S., Yao, R.H., Du, X., Liu, Y., Wang, R. and Wang, L. (2023) Traffic Flow Prediction under Multiple Adverse Weather Based on Self-Attention Mechanism and Deep Learning Models. Physica A: Statistical Mechanics and Its Applications, 625, Article 128988.
https://doi.org/10.1016/j.physa.2023.128988

has been cited by the following article:

• TITLE: Research on Traffic Flow Characteristics Based on 2-4-6 Second Car-Following Criteria

  AUTHORS: Liuhua Zhu

  KEYWORDS: Traffic Flow, Cellular Automaton, Car-Following Model, Metastable State

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.14 No.9, September 1, 2026

  ABSTRACT: In order to clarify the impact of weather conditions such as sunny, rainy, and snowy days on traffic, a one-dimensional single lane cellular automaton traffic flow model is established. These three typical weather conditions are characterized in the model via minimum safe time headways of 2 seconds, 4 seconds, and 6 seconds, respectively. Through numerical simulations, two curves are obtained: one describing the relationship between traffic flow and vehicle density under different weather conditions, and the other depicting the relationship between mean velocity and vehicle density. The research results indicate that under moderate vehicle density, weather conditions exert a considerable impact on traffic flow and mean velocity. However, at extremely low and higher vehicle densities, traffic flow and mean velocity are immune to weather conditions. In addition, the model proposed in this article reproduces nonlinear phenomena such as non-equilibrium phase transitions and metastable states that are consistent with actual traffic.