1 Old Dominion University
Norfolk, VA 23529
University of Virginia
351 McCormick Dr.
P.O. Box 400742
Charlottesville, VA 22904-4742
1424 S Main St.
Blacksburg, VA 24061
Kyoungho Ahn (VT) - Email: email@example.com
Mecit Cetin (ODU) - Email: firstname.lastname@example.org
Brian Park (UVA) - Email: email@example.com
Funding Source(s) and Amounts Provided (by each agency or organization)
VT: $16,500 (Match)
VDOT: $10,000 (Match)
ODU: $40,000 (Match)
Total Project Costs
Agency ID or Contract Number
The objective of the 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. – See more at: http://www.its.dot.gov/aeris/#sthash.bgIJbY7s.dpuf.
The specific goals of this project include:
- Identify AERIS applications that have potential network-wide benefits.
- Create simulation testbeds for the evaluation of various forms of integration of AERIS applications.
- Evaluate 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.
VT’s proposed research (1): The 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 they occur 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 (2): The objective of the study is to develop 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 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.
ODU’s proposed research: 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.
UVA’s Proposed Research: 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.
Potential implementation of project outcomes
- Developing predictive eco-routing algorithms, application programming interfaces (APIs, or software components), simulation models and results.
- Developing CV applications and field testing these applications that focus on the environment.
- Identifying strategic bundling of AERIS applications and implementation issues associated with implementing these systems.
Expected benefits and impacts
The expected benefits of the proposed research are:
- Enhancing existing eco-routing algorithms by considering the spatiotemporal evolution of the transportation network.
- Help to reduce urban congestion, vehicle fuel consumption levels, and greenhouse gas (GHG) emissions.
- Help to identify the optimum operational conditions for the various AERIS applications, including: eco-routing, eco-lanes, eco-driving, and carbon hot spot zones.
- Identifying strategic bundling of AERIS applications and other connected vehicle applications for the optimum network performance.
- Study the effect of network structure on the potential benefits of these applications.