Cellular automaton model simulating spatiotemporal patterns, phase transitions and evolution concavity in traffic flow

TL;DR

This paper improves a cellular automaton traffic flow model to better replicate real-world traffic patterns, phase transitions, and evolution concavity, validated against empirical Floating Car Data.

Contribution

The paper introduces modifications to an existing model, including safe speed and probabilistic deceleration, enhancing its ability to simulate complex traffic phenomena.

Findings

Successfully reproduces metastable states and spatiotemporal patterns.

Accurately simulates phase transition behaviors in traffic flow.

Matches empirical traffic speed data from Floating Car Data.

Abstract

This paper firstly show that a recent model (Tian et al., Transpn. Res. B 71, 138-157, 2015) is not able to well replicate the evolution concavity in traffic flow, i.e. the standard deviation of vehicles increases in a concave/linear way along the platoon. Then we propose an improved model by introducing the safe speed, the logistic function of the randomization probability, and small randomization deceleration for low-speed vehicles into the model. Simulations show that the improved model can well reproduce the metastable states, the spatiotemporal patterns, the phase transition behaviors of traffic flow, and the evolution concavity of traffic oscillations. Validating results show that the empirical time series of traffic speed obtained from Floating Car Data can be well simulated as well.