A large number of Connected and Automated Vehicle (CAV) applications have been emerging that benefit transportation systems in terms of safety, mobility and the environment. These benefits can be quantified by a variety of performance indices (PIs) as described in recent literature. However, there has been very little research in analyzing the potential co-benefits and tradeoffs among all these PIs for the various CAV applications. In this paper, we examine a number of CAV applications whose system effectiveness focus is targeted on the three key areas of safety, mobility and environment, and then examine whether some PIs are synergistic or antagonistic. Using the Lane Speed Monitoring application as a specific example, we explore the in-depth relationship between different types of measures of effectiveness (MOEs) under different penetration rates of the technology, in order to show the association between the application focus and tradeoffs to be made among different performance measures. As part of the analysis, several future research directions are discussed, including the identification of key influential factors on system performance to obtain co-benefits in terms of different types of MOEs.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A co-benefit and tradeoff evaluation framework for connected and automated vehicle applications


    Contributors:


    Publication date :

    2017-06-01


    Size :

    361728 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Co-Benefit and Tradeoff Evaluation Framework for Connected and Automated Vehicle Applications

    Tian, Danyang / Wu, Guoyuan / Boriboonsomsin, Kanok et al. | British Library Conference Proceedings | 2017


    CAVELIER: Automated Security Evaluation for Connected Autonomous Vehicle Applications

    Boddupalli, Srivalli / Chamarthi, Venkata Sai Gireesh / Lin, Chung-Wei et al. | IEEE | 2022