skip to main content
10.1145/3131944.3133936acmconferencesArticle/Chapter ViewAbstractPublication PagesmobicomConference Proceedingsconference-collections
research-article

New Dimensions of Intersection Control with Connected and Automated Vehicles

Published:20 October 2017Publication History

ABSTRACT

Connected and automated vehicle (CAV) technologies are believed to offer tremendous benefits to the urban transportation system. Intersection control will also experience a transition process from the state-of-the-practice paradigm to a fully CAV deployed environment, and new dimensions will be added to the control framework. In this paper, we envision that the transition process mainly contains three major stages, namely, the detector-free signal operation, the generalized spatiotemporal intersection control, and the infrastructure adaptation. Several key challenges are discussed.

References

  1. Barbaresso, J., et al., USDOT's Intelligent Transportation Systems (ITS) ITS Strategic Plan 2015--2019. 2014Google ScholarGoogle Scholar
  2. NHTSA, U.S. DOT advances deployment of Connected Vehicle Technology to prevent hundreds of thousands of crashes. 2016.Google ScholarGoogle Scholar
  3. Zheng, J. and H.X. Liu, Estimating traffic volumes for signalized intersections using connected vehicle data. Transportation Research Part C: Emerging Technologies, 2017. 79: p. 347--362.Google ScholarGoogle Scholar
  4. Feng, Y., KL. Head, Khoshmagham, and S., Zamanipour, M., A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C: Emerging Technologies, 2015. 55: p. 460--473.Google ScholarGoogle Scholar
  5. Li, Z., L. Elefteriadou and S. Ranka, Signal control optimization for automated vehicles at isolated signalized intersections. Transportation Research Part C: Emerging Technologies, 2014. 49: p. 1--18.Google ScholarGoogle Scholar
  6. Dresner, K. and P. Stone, A Multiagent Approach to Autonomous Intersection Management. Journal of Articial Intelligence Research, 2008. 31: p. 591--656. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Sun, W., J. Zheng and H.X. Liu, A capacity maximization scheme for intersection management with automated vehicles. Transportation Research Procedia, 2017. 23: p. 121--136.Google ScholarGoogle Scholar
  8. Day, C.M. and D.M. Bullock, Opportunities for Detector-Free Signal Offset Optimization with Limited Connected Vehicle Market Penetration: A Proof-of-Concept Study. Transportation Research Record, 2016. 2558: p. 54--65.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    CarSys '17: Proceedings of the 2nd ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and Services
    October 2017
    94 pages
    ISBN:9781450351461
    DOI:10.1145/3131944

    Copyright © 2017 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 20 October 2017

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    Overall Acceptance Rate8of20submissions,40%
  • Article Metrics

    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)0

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader