scholarly journals TSDCN: Traffic safety state deep clustering network for real‐time traffic crash‐prediction

2020 ◽  
Author(s):  
Haitao Li ◽  
Qiaowen Bai ◽  
Yonghua Zhao ◽  
Zhaowei Qu ◽  
Wang Xin
Keyword(s):  
Real Time ◽  
Traffic Safety ◽  
Crash Prediction ◽  
Traffic Crash ◽  
Real Time Traffic

Computing Traffic Information in the Cloud

2015 ◽  
pp. 1062-1079
Author(s):  
Po-Ting Wei ◽  
Tai-Chi Wang ◽  
Shih-Yu Chang ◽  
Yeh-Ching Chung
Keyword(s):  
Ad Hoc Networks ◽  
Real Time ◽  
Traffic Safety ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Hoc Networks ◽  
Commercial Applications

Vehicular ad hoc networks have been envisioned to be useful in road safety and commercial applications. In addition, in-vehicle capabilities could be used as a service to provide a variety of applications, for example, to provide real-time junction view of road intersections or to address traffic status for advanced traffic light control. In this work, the authors construct a cloud service over vehicular ad hoc networks to provide event data including capturing videos or Global Positioning System (GPS) data. Moreover, the authors integrate the GPS receiver and the navigation software equipped over On Board Unit to create a Geographic Information System digital map and to offer a traffic safety application. The hardware is implemented by Eeepad for integrating camera and GPS. Furthermore, the cyclic recording scheme has been addressed for data transmission and query. With the design, people can get real-time traffic information including traffic videos or geographical data in the cloud.

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.

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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