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Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sakiyama, Tomoko& Arizono, Ikuo. 2019. Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory. Complexity،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1131152
Modern Language Association (MLA)
Sakiyama, Tomoko& Arizono, Ikuo. Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory. Complexity No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1131152
American Medical Association (AMA)
Sakiyama, Tomoko& Arizono, Ikuo. Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory. Complexity. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1131152
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131152