Urban transportation inefficiency at signalized intersections has been a complicate problem for decades and speed guidance is a classic and widely used method for traffic optimization at signalized intersections. With the emergence of vehicular networks and intelligent vehicular infrastructure cooperative systems, frequent information communication can be established between all the traffic units and precise speed guidance notification will be available to help traffic participants make better decisions. In this paper, we proposed a speed guidance model based on vehicular networks from most simplicity to generality: from single-vehicle situation to pair situation, then to cluster situation. We focus on the comfortability of driving (minimization of acceleration rate) in single-vehicle situation, the safety distance in pair situation, and communication quality in cluster situation. An information propagation algorithm is proposed in cluster situation to ensure most car receive speed guidance information successfully.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicular Networks Communication Analysis and Speed Guidance at Signalized Intersections


    Contributors:
    Hou, Kaizhe (author) / Hu, Jianming (author)

    Conference:

    International Conference on Transportation and Development 2018 ; 2018 ; Pittsburgh, Pennsylvania



    Publication date :

    2018-07-12




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Priority for Personalized Vehicular Mobility at Signalized Intersections

    Sun, Xiaoxue / Guo, Zishen / Liu, Sijin et al. | IEEE | 2024


    Signalized Intersections and Networks

    Elefteriadou, Lily | Springer Verlag | 2013


    Signalized intersections

    Miller, A.J. | Engineering Index Backfile | 1968


    What Motivates Drivers to Comply with Speed Guidance Information at Signalized Intersections?

    Xiaomei Zhang / Xumei Chen / Aihua Fan et al. | DOAJ | 2020

    Free access