Combined Traffic Signal Control and Route Guidance: Multiple User Class Traffic Assignment Model versus Discrete Choice Model

2006 ◽  
Author(s):  
Zhaosheng Yang ◽  
Shoufeng Lu ◽  
Ximin Liu
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Route Guidance ◽  
Multiple User ◽  
Assignment Model ◽  
Model Versus

scholarly journals Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7330
Author(s):  
Le Zhang ◽  
Mohamed Khalgui ◽  
Zhiwu Li
Keyword(s):  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Integrated Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Route Guidance ◽  
Internet Of Vehicles ◽  
Vehicle Route ◽  
Dimension Control

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.

Stochastic Model of Traffic Jam and Traffic Signal Control

2011 ◽  
Vol 131 (2) ◽  
pp. 303-310
Author(s):  
Ji-Sun Shin ◽  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Hee-hyol Lee
Keyword(s):  
Stochastic Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam
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