TY - JOUR
T1 - Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory
AU - Sakiyama, Tomoko
AU - Arizono, Ikuo
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP 18K04611.
Publisher Copyright:
© 2019 Tomoko Sakiyama and Ikuo Arizono.
PY - 2019
Y1 - 2019
N2 - Here, we develop a new cellular automata-based traffic model. In this model, individual vehicles cannot estimate global traffic flows but can only detect the vehicle ahead. Each vehicle occasionally adjusts its velocity based on the distance to the vehicle in front. Our model generates reversible phase transitions in the vehicle flux over a wide range of vehicle densities, and the traffic system undergoes scale-free evolution with respect to the flux. We thus believe that our model reveals the relationship between the macro-level flows and micro-level mechanisms of multi-agent systems for handling traffic congestion, and illustrates how drivers' decisions impact free and congested flows.
AB - Here, we develop a new cellular automata-based traffic model. In this model, individual vehicles cannot estimate global traffic flows but can only detect the vehicle ahead. Each vehicle occasionally adjusts its velocity based on the distance to the vehicle in front. Our model generates reversible phase transitions in the vehicle flux over a wide range of vehicle densities, and the traffic system undergoes scale-free evolution with respect to the flux. We thus believe that our model reveals the relationship between the macro-level flows and micro-level mechanisms of multi-agent systems for handling traffic congestion, and illustrates how drivers' decisions impact free and congested flows.
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U2 - 10.1155/2019/1956521
DO - 10.1155/2019/1956521
M3 - Article
AN - SCOPUS:85077945794
VL - 2019
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 1956521
ER -