Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory

Tomoko Sakiyama, Ikuo Arizono

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number1956521
JournalComplexity
Volume2019
DOIs
Publication statusPublished - Jan 1 2019

ASJC Scopus subject areas

  • Computer Science(all)
  • General

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