scholarly journals Traffic Efficiency Evaluation of Elliptical Roundabout Compared with Modern and Turbo Roundabouts Considering Traffic Signal Control

2017 ◽  
Vol 29 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Hadi Hatami ◽  
Iman Aghayan
Keyword(s):  
Unsaturated Flow ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Speed Limit ◽  
Efficiency Evaluation ◽  
Flow Conditions ◽  
Negative Effects ◽  
Traffic Efficiency ◽  
Central Island ◽  
Positive Effect

This paper compared the performance of elliptical roundabout with turbo and modern roundabouts. It considers the effects of increasing the central island radius and speed limit on delay and capacity. Three types of roundabouts (modern, turbo and elliptical roundabouts) with different numbers of lanes (single lane, two-lane and three-lane) were designed. Unsignalized and signalized controls were applied for these roundabouts. The robustness of the designed roundabouts was investigated for saturated and unsaturated flow conditions. Based on the obtained results, increasing the central island radius had both positive and negative effects on delay and capacity. However, a positive effect on these variables was observed in all roundabouts when increasing the speed limit. In unsignalized and signalized control under unsaturated flow conditions, a modern roundabout had lower delay time than an elliptical roundabout. Moreover, in saturated flow, the elliptical roundabout had the best performance in terms of delay. Overall, in comparison with the turbo roundabouts, modern and elliptical roundabouts had the highest capacities in unsignalized and signalized controls. This study can provide useful information for engineers who decide to design a roundabout.

scholarly journals Development of a Neural Network Based Q Learning Algorithm for Traffic Signal Control

2011 ◽  
Vol 2-3 ◽  
pp. 91-95
Author(s):  
Li Bi Fu ◽  
Kil To Chong
Keyword(s):  
Neural Network ◽  
Memory Load ◽  
Learning Algorithm ◽  
Control Area ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Q Learning ◽  
Traffic Efficiency ◽  
Continuous State

As one kind of reinforcement learning method, Q learning algorithm has already been proved to achieve many significant results in traffic signal control area. However, when the state of Markov Decision Process is very big or continuous, the computation load and the memory load will become very big and can not be solved then. Therefore, this paper proposed a neural network based Q learning algorithm to solve this problem known as “Curse of Dimensionality”. This new method realized generalization of conventional Q learnig algorithm in huge and continuous state space as neural network is a very effective value function approximator. Experiment has been implemented upon an isolated intersection and simulation results show that the proposed method can improve the traffic efficiency significantly than the conventional Q learning algorithm.

DESIGN OF TRAFFIC SIGNAL CONTROL SYSTEMS BASED ON FUZZY CONTROL

2020 ◽  
Vol 4 (1) ◽  
pp. 24-27
Author(s):  
Ahmad Fadzli Abd Aziz
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fuzzy Control System ◽  
Urban Road ◽  
Traffic Efficiency

With a gradual increase in urban road traffic volume and traffic congestion degree, how to improve and solve the current traffic pressures has been a problem requiring urgent solution. To improve the traffic congestion status in urban road intersections and heighten the road traffic efficiency, the principle of fuzzy control is employed in this paper. Moreover, the multi-phase signal traffic control of intersections is performed together with vehicle queue lengths in lanes corresponding to the key traffic flow, and a traffic signal fuzzy control system is designed. Finally, this paper compares the simulation results between the fuzzy control system designed herein and the existing fixed traffic signal control methods. The comparative test results have shown that the fuzzy control method can well better actual congestion and traffic efficiency at intersections to a greater degree than the fixed timing method does.

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

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

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

scholarly journals Determinants of Corporate Anti-Corruption Disclosure: The Case of the Emerging Economics

2021 ◽  
Vol 13 (6) ◽  
pp. 3462
Author(s):  
Maider Aldaz Odriozola ◽  
Igor Álvarez Etxeberria
Keyword(s):  
Empirical Studies ◽  
Geographical Area ◽  
Control Variable ◽  
Emerging Countries ◽  
Point Of View ◽  
Press Freedom ◽  
Negative Effects ◽  
Key Factor ◽  
Using Data ◽  
Positive Effect

Corruption is a key factor that affects countries’ development, with emerging countries being a geographical area in which it tends to generate greater negative effects. However, few empirical studies analyze corruption from the point of view of disclosure by companies in this relevant geographical area. Based on a regression analysis using data from the 96 large companies from 15 emerging countries included in the 2016 International Transparency Report, this paper seeks to understand what determinants affect such disclosure. In that context, this paper provides empirical evidence to understand the factors that influence reporting on anti-corruption mechanisms in an area of high economic importance that has been little studied to date, pointing to the positive effect of press freedom in a country where the company is located and with the industry being the unique control variable that strengthens this relationship.

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