Neuro-Adaptive Traffic Congestion Control for Urban Road Networks

2018 ◽  
Author(s):  
Charalampos P. Bechlioulis ◽  
Kostas J. Kyriakopoulos
Keyword(s):  
Congestion Control ◽  
Traffic Congestion ◽  
Road Networks ◽  
Urban Road ◽  
Traffic Congestion Control

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 HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS

2016 ◽  
Vol XLI-B2 ◽  
pp. 159-164 ◽  
Author(s):  
B. Anbaroglu ◽  
B. Heydecker ◽  
T. Cheng
Keyword(s):  
Traffic Congestion ◽  
Travel Demand ◽  
Evaluation Criteria ◽  
Road Networks ◽  
Daily Basis ◽  
Detection Methods ◽  
Just In Time ◽  
Urban Road ◽  
Time Estimates ◽  
Congestion Detection

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

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