Multi-objective optimal predictive control of signals in urban traffic network

Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network model...

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Bibliographic Details
Published in:Journal of intelligent transportation systems Vol. 23; no. 4; pp. 370 - 388
Main Authors: Li, Xiang, Sun, Jian-Qiao
Format: Journal Article
Language:English
Published: Philadelphia Taylor & Francis 04.07.2019
Taylor & Francis Ltd
Subjects:
2010 Mathematics Subject Classification. Primary
cell transmission model
Communications traffic
Control algorithms
Control theory
Dynamic control
dynamic predictive control
genetic algorithm
Genetic algorithms
Multi-objective optimization
Multiple objective analysis
Optimization
Performance enhancement
Predictive control
Traffic congestion
Traffic models
traffic network
ISSN:1547-2450, 1547-2442
Online Access:Get full text
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Summary:Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network models are considered, i.e., simple network which captures basic macroscopic traffic characteristics and advanced network that further considers vehicle turning and different traveling routes between origins and destinations. A network signal predictive control algorithm is developed for online multi-objective optimization. A variety of objectives are considered such as system throughput, vehicle delay, intersection crossing volume, and spillbacks. The genetic algorithm (GA) is applied to solve the optimization problem. Three example networks with different complexities are studied. It is observed that the optimal traffic performance can be achieved by the dynamic control in different situations. The influence of the objective selection on short-term and long-term network benefits is studied. With the help of parallel computing, the proposed method can be implemented in real time and is promising to improve the performance of real traffic network.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1547-2450
1547-2442
DOI:10.1080/15472450.2018.1504294