This paper discusses vehicle traffic congestion which leads to air pollution, driver frustration, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a considerable challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Recent advances in sensing, communication and computing technologies enables us to gather real-time data about traffic condition of the roads and mitigate the traffic congestion via various ways such as Vehicle Traffic Routing Systems (VTRSs), electronic toll collection system (ETCS), and intelligent traffic light signals (TLSs). Regarding this issue, an innovative technology, called Intelligent Guardrails (IGs), is presented in this paper. IGs takes advantages of Internet of Things (IoT) and vehicular networks to provide a solution for vehicle traffic congestion in large cities. IGs senses the roads' traffic condition and uses this information to set the capacity of the roads dynamically.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Intelligent Guardrails: An IoT Application for Vehicle Traffic Congestion Reduction in Smart City


    Contributors:


    Publication date :

    2016-12-01


    Size :

    1279848 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Intelligent traffic light control for congestion management for smart city development

    Gupta, Vishu / Kumar, Rajesh / Reddy, K. Srikanth et al. | IEEE | 2017


    Motorcycle accidents with guardrails

    Koch,H. / Brendicke,R. / Inst.f.Zweiradsicherheit,Bochum-Wattenscheid,DE | Automotive engineering | 1989


    Adoption of Smart Traffic System to Reduce Traffic Congestion in a Smart City

    Aroba, Oluwasegun Julius / Mabuza, Phumla / Mabaso, Andile et al. | Springer Verlag | 2023



    Machine learning Smart Traffic Prediction and Congestion Reduction

    Lakshna, A. / Ramesh, K. / Prabha, B. et al. | IEEE | 2021