Network-wide Impacts of Eco-routes and Route Choice Behavior/Evaluation of AERIS Applications

The objective of the Applications for the Environment: Real-Time Information Synthesis (AERIS) research program is to generate and acquire environmentally-relevant real-time transportation data, and use these data to create actionable information that support and facilitate "green" transportation choices by transportation system users and operators. The specific goals of this project include: (1) identifying AERIS applications that have potential network-wide benefits; (2) creating simulation testbeds for the evaluation of various forms of integration of AERIS applications; and (3) evaluating the various AERIS applications on the different testbeds and make regional and national recommendations for the implementation of these applications. In achieving these goals the following research is proposed. Virginia Tech;s (VT's) proposed research team will develop predictive Eco-Routing algorithms under the CV environment. A key component of the predictive Eco-Routing system entails predicting the onset of congestion before it occurs so that Eco-Routing operation can be provided to approaching vehicles in order to reduce the fuel consumption and congestion. In previous research efforts, the research team developed Eco-Routing algorithms and evaluated the system-wide impacts of the Eco-Rouging system. Traffic state estimation and prediction are critical components of traffic management and advanced traveler information systems. The team developed a particle filter approach which can accurately predict freeway traffic conditions using measured traffic speed data. Currently the team is developing a real-time monitoring system that can continuously evaluate energy and environmental impacts on transportation facilities using real-time traffic data. Utilizing the results of the previous research efforts, the proposed system will provides optimum vehicle routing information using a multi-step traffic state prediction algorithm that a driver may follow in order to minimize his/her vehicle's fuel consumption level. Further, the research will develop a micro traffic simulation model to assess the potential network-wide impacts of the predictive Eco-Routing implementation. The simulation model will build on the CVI-UTC test-bed in Northern Virginia. The study will quantify the impacts of the overall Eco-Routing benefits, the levels of market penetration (LMPs) of the Eco-Routing system on network-wide performance, the levels of traffic congestion on the system performance, and vehicle types on Eco-Routing system performance. VT's proposed research develops algorithms that can characterize the optimum Eco-Lanes operational conditions. The Eco-Lanes was introduced as one of six AERIS Transformative Concepts. Major innovative research efforts related to Connected Vehicle Environmental Applications has been performed in recent years in the United States, Europe, and various Asian countries. However, few studies have been conducted focusing on Eco-Lanes applications. The Eco-Lanes concept integrates dedicated highway lanes that are optimized to reduce vehicle fuel consumption levels and improve air quality. In eco-lanes, drivers are required to operate the vehicle at recommended or variable speeds to reduce transportation energy consumption and improve vehicle mobility. The research team recently investigated the feasibility of Eco-Lanes applications that attempt to reduce system-wide fuel consumption and greenhouse gas (GHG) emission levels through lane management strategies. The study focused its efforts on evaluating various Eco-Lanes and SPD-HARM applications using microscopic traffic simulation software. The proposed study will develop a framework that can identify the optimum Eco-Lanes specifications such as the spatial and temporal Eco-Lanes boundaries under various traffic operational conditions. Further the study will develop the optimum eco-speed limit algorithms in Eco-Lanes. Old Dominion University's (ODU's) proposed research will develop robust models and data mining for predicting network conditions from probe vehicle data at recurrent bottlenecks to support eco-route guidance. One of the key aspects of providing eco-routes to drivers entails predicting network conditions in real-time. The ability to estimate downstream conditions accurately is important for determining the true eco-routes in a network. In particular, due to incidents or variation in demand, traffic conditions at recurrent-bottleneck locations are typically more volatile, making reliable prediction a challenge. In this research, probe data from known bottleneck locations (e.g., bridges and tunnels) will be used as the basis to develop robust models that provide reliable travel-time or delay prediction under varying conditions. Spatiotemporal correlation of travel times will be analyzed to build models that capitalize on the predictable patterns. The prediction models will be evaluated for their performance under different prediction horizons and events. The impacts of variation in bottleneck conditions on eco-route selection will be assessed. University of Virginia's (UVA's) proposed research shows how it is expected that route guidance system under AERIS would take full advantage from opted-in drivers equipped with connected vehicle technology. One of key challenges is properly modeling drivers' compliances on the guided routes, in which would vary depending on the time of day, trip purpose, quality of existing and alterative routes, etc. This research will develop a simulation testbed that can evaluate these factors and assess the impact of eco-route guidance at a network level under various AERIS applications.


  • English


  • Status: Completed
  • Contract Numbers:


  • Sponsor Organizations:

    Virginia Center for Transportation Innovation and Research

    530 Edgemont Road
    Charlottesville, VA  United States  22903

    Old Dominion University

    Norfolk, VA  United States  23529

    Virginia Polytechnic Institute and State University, Blacksburg

    Blacksburg, VA  United States  24061

    Research and Innovative Technology Administration

    University Transportation Centers Program
    1200 New Jersey Avenue
    Washington, DC  United States  20590
  • Project Managers:

    Parkany, Emily

  • Performing Organizations:

    University of Virginia, Charlottesville

    Charlottesville, VA  United States 

    Old Dominion University

    Norfolk, VA  United States  23529

    Virginia Polytechnic Institute and State University, Blacksburg

    Blacksburg, VA  United States  24061
  • Principal Investigators:

    Park, B

    Cetin, Mecit

    Ahn, Kyoungho

    Rakha, Hesham

  • Start Date: 20141101
  • Expected Completion Date: 0
  • Actual Completion Date: 20160430
  • Source Data: RiP Project 37261

Subject/Index Terms

Filing Info

  • Accession Number: 01539894
  • Record Type: Research project
  • Source Agency: Mid-Atlantic Transportation Sustainability Center
  • Contract Numbers: DTRT13-G-UTC33
  • Files: UTC, RiP
  • Created Date: Oct 7 2014 1:00AM