Research on Optimization Scheme of Auxiliary Signal Control in Elevated Exit Area Based on VISSIM Simulation

2021 ◽  
Author(s):  
Jinrong Hu ◽  
Jie He ◽  
Ziyang Liu ◽  
Hao Zhang ◽  
Changjian Zhang ◽  
...  
Author(s):  
Chronis Stamatiadis ◽  
Nathan H. Gartner

Progression schemes are widely used for traffic signal control in urban arterial streets. Commonly available programs such as the MAXBAND or PASSER programs use the traditional approach, which consists of a uniform bandwidth design for each arterial. The multiband criterion, on the other hand, has the ability to adapt the progressions to the specific characteristics of each link in the network and thus obtain improved performance. The development and application of the multiband signal optimization scheme in multiarterial grid networks are described. The MULTIBAND-96 model optimizes all the signal control variables, including phase lengths, offsets, cycle time, and phase sequences, and generates variable bandwidth progressions on each arterial in the network. It uses the MINOS mathematical programming package for the optimization and offers considerable advantages compared with existing models. Simulation results using TRAF-NETSIM are given.


2011 ◽  
Vol 131 (2) ◽  
pp. 303-310
Author(s):  
Ji-Sun Shin ◽  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Hee-hyol Lee

2017 ◽  
Vol E100.B (3) ◽  
pp. 417-425 ◽  
Author(s):  
Stephane KAPTCHOUANG ◽  
Hiroki TAHARA ◽  
Eiji OKI

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems


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