scholarly journals Network Fundamental Diagram (NFD) and traffic signal control: first empirical evidences from the city of Santander

2017 ◽  
Vol 27 ◽  
pp. 27-34 ◽  
Author(s):  
Borja Alonso ◽  
Ángel Ibeas Pòrtilla ◽  
Giuseppe Musolino ◽  
Corrado Rindone ◽  
Antonino Vitetta
Keyword(s):  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fundamental Diagram ◽  
Network Fundamental Diagram ◽  
The City

scholarly journals Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 274
Author(s):  
Maha Elouni ◽  
Hossam M. Abdelghaffar ◽  
Hesham A. Rakha
Keyword(s):  
Traffic Congestion ◽  
State Of The Art ◽  
Simulation Software ◽  
Traffic Signal ◽  
Nash Bargaining ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fundamental Diagram ◽  
Total Delay ◽  
Urban Network

This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between 21% and 41%, total delay ranging between 40% and 55%, and emission levels/fuel consumption ranging between 12% and 20%. The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers.

scholarly journals IoT- Based Traffic Signal Control for Ambulance

2020 ◽  
Vol 9 (3) ◽  
pp. 3250-3254
Keyword(s):  
Traffic Management ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Emergency Vehicle ◽  
Current Time ◽  
The Road ◽  
Traffic Management System ◽  
The City ◽  
Modern Era

In modern era, due to increase in traffic in the city, emergency vehicles take more time to reach the destination. The current, time-based traffic management system is not suitable and also not flexible for present day traffic, especially at the intersection where the traffic needs to be controlled for vehicles from all four directions. To solve this problem, we bring users a sound detector with automatic recording of various vehicle sounds and distinguishing the presence of ambulance in a particular lane by detecting the siren sound. The captured ambulance sound is processed using IOT and sent to the traffic pole to enhance the traffic clearance. This is carried out by placing the sensors in each lane and a sensor near the traffic pole to indicate that the ambulance has crossed the lane. In this method the traffic signal controller decides when the vehicle has to cross the road and also provide importance to the emergency vehicle.

OVERCOMING THE PIGOU–DOWNS PARADOX USING ADVANCED TRAFFIC SIGNAL CONTROL

2013 ◽  
Vol 24 (06) ◽  
pp. 1350039 ◽  
Author(s):  
S. C. FOWDUR ◽  
S. D. D. V. RUGHOOPUTH
Keyword(s):  
Cellular Automata ◽  
Travel Time ◽  
Road Network ◽  
Traffic Signal ◽  
Capital City ◽  
Signal Control ◽  
Traffic Signal Control ◽  
City Center ◽  
Traffic Lights ◽  
The City

Expansion of a road network has often been observed to cause more congestion and has led researchers to the formulation of traffic paradoxes such as the Pigou–Downs and the Braess paradoxes. In this paper, we present an application of advanced traffic signal control (ATSC) to overcome the Pigou–Downs paradox. Port Louis, the capital city of Mauritius is used to investigate the effect of using a harbor bridge to by-pass the city center. Using traffic cellular automata (TCA) simulations it has been shown how, if traffic is only gradually deviated along the by-pass, an overall longer travel time and decreased flux would result. By making use of ATSC, which involves traffic lights that sense the number of vehicles accumulated in the queue, better travel times and fluxes are achieved.

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

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