IDEAS home Printed from https://ideas.repec.org/p/cdl/itsdav/qt8858n2wn.html
   My bibliography  Save this paper

Eco-Friendly Intelligent Transportation System Technology for Freight Vehicles

Author

Listed:
  • Kari, David
  • Wu, Guoyuan
  • Barth, Matthew

Abstract

Heavy-duty freight vehicles contribute a disproportionate amount of emissions relative to the national fleet percentage and the relative vehicle miles traveled by heavy-duty freight vehicles. Accordingly, an environmentally-friendly Intelligent Transportation System (ITS) application for improving arterial roadway performance is presented in this report. For arterial roadways, most Active Traffic and Demand Management (ATDM) strategies focus on traffic signal timing optimization at signalized intersections. A critical drawback of conventional traffic signal control strategies is that they rely on measurements from point detection, and estimate traffic states such as queue length based on very limited information. The introduction of Connected Vehicle (CV) technology can potentially address the limitations of point detection via wireless communications to assist signal phase and timing optimization. In this project report, the authors present an agent-based online adaptive signal control (ASC) strategy based on real-time traffic information available from vehicles equipped with CV technology. They then evaluate the proposed strategy in terms of travel delay and energy consumption, relative to a Highway Capacity Manual (HCM) based method in which hourly traffic demand is assumed to be known accurately a priori. This Connected Vehicle Adaptive Signal Control (CV-ASC) strategy has been applied to an isolated traffic intersection as well as to a corridor of traffic intersections. The baseline signalization strategy for the corridor of traffic intersections is coordinated signal control. Study results indicate that for both the isolated intersection and corridor contexts, the proposed strategy outperforms the HCM based method and is very robust to traffic demand variations. The proposed system also provides a framework to flexibly modify signal timing in order to serve evolving localities freight needs. View the NCST Project Webpage

Suggested Citation

  • Kari, David & Wu, Guoyuan & Barth, Matthew, 2017. "Eco-Friendly Intelligent Transportation System Technology for Freight Vehicles," Institute of Transportation Studies, Working Paper Series qt8858n2wn, Institute of Transportation Studies, UC Davis.
  • Handle: RePEc:cdl:itsdav:qt8858n2wn
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/8858n2wn.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:itsdav:qt8858n2wn. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucdus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.