Self-organization models of urban traffic lights based on digital infochemicals

Self-organization models of urban traffic lights based on digital infochemicals This paper presents a self-organizing model to design effective traffic signaling strategies in order to reduce traffic congestion in urban areas. The proposed traffic signaling system is based on a pattern model of self-organization, i.e., digital infochemicals (DIs), which are analogous to chemical substances that convey information between interactive elements mediated via the environment. In the context of traffic systems, the DIs refer to information generated by vehicles and dissipated by the urban transportation infrastructure. Based on the exploratory analysis with one single intersection, we demonstrate that the DI-based strategy performs significantly better than both the fixed and trigger-based scheduling strategies in terms of queue length and waiting time under both fixed and dynamic traffic demands. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png SIMULATION: Transactions of The Society for Modeling and Simulation International SAGE

Self-organization models of urban traffic lights based on digital infochemicals

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Publisher
SAGE Publications
Copyright
© The Author(s) 2018
ISSN
0037-5497
eISSN
1741-3133
D.O.I.
10.1177/0037549718777615
Publisher site
See Article on Publisher Site

Abstract

This paper presents a self-organizing model to design effective traffic signaling strategies in order to reduce traffic congestion in urban areas. The proposed traffic signaling system is based on a pattern model of self-organization, i.e., digital infochemicals (DIs), which are analogous to chemical substances that convey information between interactive elements mediated via the environment. In the context of traffic systems, the DIs refer to information generated by vehicles and dissipated by the urban transportation infrastructure. Based on the exploratory analysis with one single intersection, we demonstrate that the DI-based strategy performs significantly better than both the fixed and trigger-based scheduling strategies in terms of queue length and waiting time under both fixed and dynamic traffic demands.

Journal

SIMULATION: Transactions of The Society for Modeling and Simulation InternationalSAGE

Published: Jun 1, 2018

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