scholarly journals A Scientometric-Based Review of Traffic Signal Control Methods and Experiments Based on Connected Vehicles and Floating Car Data (FCD)

2021 ◽  
Vol 11 (12) ◽  
pp. 5547
Author(s):  
Vittorio Astarita ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
New Technologies ◽  
State Of The Art ◽  
Traffic Signal ◽  
The State ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicles ◽  
City Management ◽  
Control Methods ◽  
Floating Car Data

This paper reviews the state of the art in traffic signal control methods that are based on data coming from onboard smartphones or connected vehicles. The review of the state of the art is carried out by applying analytical scientometric tools (topic visualization, co-citation analysis to establish influential journals and references, country analysis based on coauthorship, trending-topics analysis carried out by overlay visualization). The introduction of autonomous and connected vehicles will allow city management organizations to introduce new intersection management systems that rely on real-time positional data coming from instrumented vehicles. Traditional vehicles also could benefit from these new technologies by profiting from better-regulated intersections. This paper using a scientometric approach frames all the scientific contributions aimed at the field of traffic signal methods and experiments based on connected vehicles and floating car data. The applied scientometric approach reveals trending ideas and concepts and identifies the relevant documents that can be consulted in order for scientists and professionals to develop further this field with the implementation of new traffic signal control systems that can “give the green light” to drivers.

scholarly journals A review of traffic signal control methods and experiments based on Floating Car Data (FCD)

2020 ◽  
Vol 175 ◽  
pp. 745-751 ◽  
Author(s):  
Vittorio Astarita ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Floating Car Data

scholarly journals Floating Car Data Adaptive Traffic Signals: A Description of the First Real-Time Experiment with “Connected” Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 114 ◽  
Author(s):  
Vittorio Astarita ◽  
Vincenzo Giofré ◽  
Demetrio Festa ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicles ◽  
Test Bed ◽  
Floating Car Data ◽  
Better Regulation

The future of traffic management will be based on “connected” and “autonomous” vehicles. With connected vehicles it is possible to gather real-time information. The main potential application of this information is in real-time adaptive traffic signal control. Despite the feasibility of using Floating Car Data (FCD), for signal control, there have been practically no real experiments with all “connected” vehicles to regulate traffic signals in real-time. Most of the research in this field has been carried out with simulations. The purpose of this study is to present a dedicated system that was implemented in the first experiment of an FCD-based adaptive traffic signal. For the first time in the history of traffic management, a traffic signal has been regulated in real time with real “connected” vehicles. This paper describes the entire path of software and system development that has allowed us to make the steps from just simulation test to a real on-field implementation. Results of the experiments carried out with the presented system prove the feasibility of FCD adaptive traffic signals with commonly-used technologies and also establishes a test-bed that may help others to develop better regulation algorithms for these kinds of new “connected” intersections.

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 Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

2020 ◽  
pp. 1-11
Author(s):  
Felipe de Souza ◽  
Rodrigo Castelan Carlson ◽  
Eduardo Rauh Muller ◽  
Konstantinos Ampountolas
Keyword(s):  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicles

scholarly journals A Single Intersection Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 409 ◽  
Author(s):  
Vittorio Astarita ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Traffic Safety ◽  
New Technologies ◽  
Traffic Signal ◽  
Pollutant Emissions ◽  
Connected Vehicles ◽  
Floating Car Data ◽  
Real Time Traffic ◽  
Signal Systems

New technologies such as "connected" and "autonomous" vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate traffic signal systems, in real time, has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not "connected" vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate traffic signal systems in real-time in a single intersection and in terms of cooperative-competitive paradigm between "connected" vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that "invisible vehicles" for the system (which are not "connected") in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of a single intersection signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.

A survey of model predictive control methods for traffic signal control

2019 ◽  
Vol 6 (3) ◽  
pp. 623-640 ◽  
Author(s):  
Bao-Lin Ye ◽  
Weimin Wu ◽  
Keyu Ruan ◽  
Lingxi Li ◽  
Tehuan Chen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods

scholarly journals Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data

2018 ◽  
Author(s):  
Vittorio Astarita ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
Alessandro Vitale
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Traffic Safety ◽  
New Technologies ◽  
Traffic Signal ◽  
Pollutant Emissions ◽  
Connected Vehicles ◽  
Floating Car Data ◽  
Real Time Traffic ◽  
Signal Systems

New technologies such as "connected" and "autonomous" vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate, in real-time, traffic signal systems has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not "connected" vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate in real-time traffic signal systems in terms of cooperative-competitive paradigm between "connected" vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that "invisible vehicles" for the system (which are not "connected") in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.

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