The Intelligent Traffic Control Model of Multiphase Intersection with Variable Phase Sequence

2017 ◽  
Author(s):  
Liming Gao
Keyword(s):  
Traffic Control ◽  
Control Model ◽  
Variable Phase ◽  
Phase Sequence

scholarly journals Research on Urban Traffic Active Control in Cooperative Vehicle Infrastructure

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Li-li Zhang ◽  
Li Wang ◽  
Qi Zhao ◽  
Fang Wang ◽  
Yadongyang Zhu ◽  
...  
Keyword(s):  
Traffic Control ◽  
Optimization Method ◽  
Control Model ◽  
Urban Traffic ◽  
Advanced Technology ◽  
Traffic Demand ◽  
Traffic Efficiency ◽  
Dynamic Phase ◽  
Phase Sequence ◽  
Intersection Control

Urban intersection control mainly undertakes two tasks: traffic safety and traffic efficiency. Traditional intersection control models and methods have been insufficient in improving traffic efficiency, which is composed of the increase in traffic demand and the complexity of demand at present. In this paper, we propose a novel model and method called ATCM, which is based on the advanced technology of cooperative vehicle infrastructure. In this paper, a novel active traffic control model (ATCM) is proposed, which is based on the advanced technology of cooperative vehicle infrastructure. ATCM increases the intersection control model variables from the traditional two dimensions to five dimensions. It reshapes intersection control from the perspective of road designers and managers, so it can achieve more flexible and efficient traffic control. To this end, a multivariable active traffic control model is constructed, which includes road speed, lane control, sequence, phase, and green light time; a D-double layer optimization method is designed for this model. The first part of this optimization method combines speed control and dynamic phase sequence control. The second part is realized by the combination of lane control and dynamic phase sequence control. By conducting comprehensive experiments, the results demonstrate that the proposed approach is more flexible and efficient than traditional methods.

Translating the Air Traffic Control Model

2017 ◽  
pp. 39-57
Author(s):  
Leon Starr ◽  
Andrew Mangogna ◽  
Stephen Mellor
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Control Model ◽  
Air Traffic

scholarly journals Research on an intelligent traffic congestion control model

2021 ◽  
Vol 336 ◽  
pp. 07001
Author(s):  
Bo Xu ◽  
Jianbing Chen ◽  
Wei Tang
Keyword(s):  
Congestion Control ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Control Model ◽  
Status Quo ◽  
Key Technology ◽  
Related Research ◽  
The Status ◽  
Traffic Congestion Control

This paper summarizes the status quo of intelligent traffic congestion control and vehicle following on traffic road, puts forward the key technology model and its content of intelligent traffic control, elaborates the model and content in detail, and summarizes the research done, hoping to provide reference for the related research on intelligent traffic congestion control.

scholarly journals Developement of Dynamic On-Ramp Traffic Control Model for Urban Expressway Network

1995 ◽  
Vol 12 ◽  
pp. 757-768
Author(s):  
Yasunori IIDA ◽  
Ju-Hyun KIM ◽  
Nobuhiro UNO
Keyword(s):  
Traffic Control ◽  
Control Model ◽  
Urban Expressway

scholarly journals Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

2019 ◽  
Vol 8 (11) ◽  
pp. 2255-2262
Keyword(s):  
Traffic Control ◽  
Control Method ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Light Phase ◽  
Agent Based ◽  
Effective Manner ◽  
Multi Agent ◽  
On The Road

This paper presents a multi-agent based distributed traffic control model to optimize the traffic signal for multiple intersections. Previous works in the area of traffic signal control suffer from a number of inadequacies, including the use of fixed cycle length, centralized mode of operations and dependency on historical data. Considering these, the aim of this work is to control the traffic signal timings by adjusting the phase sequence in order to minimize the delay in traffic at the intersections. To model the traffic network, a three-tier multi-agent system has been adopted in distributed mode. In addition, a fully actuated signal control algorithm is designed and it utilizes state-space equations to formulate the queue length at the green light phase and red light phase. The proposed model is simulated with SUMO simulator and a comparative analysis has been made between adaptive control method, multi-agent method based on collective learning and multi-agent based fully actuated control method on a similar platform. The results spectacle the proposed traffic control model outperforms that of other existing control methods in all condition; hence it can be deployed to control the tremendous traffic on the road network and to optimize the traffic signal in more effective manner.

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