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Traffic Signal Self-organizing Control Based on Phase Random Traffic Demand

Published: 14 March 2022 Publication History

Abstract

In order to solve the problem of traffic signal control under fluctuating and unsteady traffic conditions, an urban traffic signal self-organizing control model based on phase random traffic demand is proposed. The traditional traffic signal control model based on cycle and green ratio is discretized into a real-time online control model with the phase duration as the control parameter. According to the probability density distribution model of traffic flow and the queuing state of vehicles at local intersection, the prediction model of phase random traffic demand at local intersection is established. The self-organizing control rule of phase green light duration is established with the control objective that the current phase queuing vehicles are released exactly. According to the self-organizing control rules, the phase duration of traffic signal matches the traffic demand of the current phase in real time, so as to adapt to the fluctuation of traffic flow and reduce the delay time. Simulation results show that the self-organizing control model has a significant advantage over traditional cycle-based traffic signal control under the condition of high traffic saturation.

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AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
October 2021
3136 pages
ISBN:9781450385046
DOI:10.1145/3495018
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 March 2022

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