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Applications for the Environment Real-Time Synthesis State-of-the-Practice Support

Project Information

Project ID: 
Project Status: 
Start Date: 
Thursday, September 23, 2010
End Date: 
Wednesday, July 6, 2011
FHWA Program: 
Operations, Intelligent Transportation Systems
TRT Terms: 
Environment; Energy; Emissions; Greenhouse Gases; Intelligent Transportation Systems; Operations; Research; Highway Traffic Control
FHWA Discipline: 
Subject Area: 
Environment, Operations and Traffic Management, Research

Contact Information

First Name: 
Last Name: 
(202) 493-3484
Email Address: 

Project Details

Project Abstract: 

The AERIS (Applications for the Environment: Real-Time Information Synthesis) research program is intended to conduct research on generating and/or acquiring environmentally relevant real-time transportation data to create actionable information to support and facilitate “green” transportation choices by transportation system users and operators. The AERIS program will better define how connected vehicle data and applications might contribute to mitigating some of the negative environmental impacts of surface transportation. The purpose of this project is to conduct for the AERIS program a state of the practice assessment of behavioral and activity-based models that will be of use to assess how behaviors may be influenced to reduce negative environmental impacts of the transportation system; environmental models; and technologies that will allow the capture of environmental data and data needed to measure environmental impacts.


The key project objectives are:

(1) The performance of intelligent transportation systems (ITS) applications and operating strategies using connected vehicle technologies can be assessed in terms of emissions, greenhouse gas (GHG), and energy consumption.

(2) Environmental data can be acquired through connected vehicle technologies.

Project Outputs: 

(1) In order to quantify the emissions impacts of intelligent transportation systems (ITS) strategies, it is necessary to adopt a modeling approach that integrates travel demand models with traffic simulation models and feeds the results from traffic simulation models to emissions models. Activity-based models are best suited to predict traveler behavior changes and microsimulation models are best suited to estimate the transportation system efficiency changes. The key inputs to emissions models are "speed" and "vehicle activity data" (if advanced emission models such as the Comprehensive Modal Emissions Model (CMEM) and the Motor Vehicle Emission Simulator (MOVES) are used). Vehicle activity data typically include distribution of vehicle miles traveled by vehicle class, vehicle miles of travel (VMT) distribution by hour, starts per day distribution by vehicle class and vehicle age, engine starts per day and their distribution by hour of the day, average trip length distribution, and engine start soak time distribution by hour (cold soak distribution). Once detailed speed data and vehicle data are generated, establishing the linkage between traffic simulation models and emissions models is relatively straightforward. Further research is needed to determine the most effective way to integrate travel demand model outputs with microscopic emissions models to estimate regional emissions impacts more accurately. 

(2) More accurate emissions measurements may be obtainable by combining vehicle-based data with infrastructure-based data. In such a hybrid model, test vehicles drive by Roadside Sensing Devices (RSDs) at various speeds and under various conditions. Their emissions are both determined from the vehicular data (from a Portable Emissions Measurement System (PEMS) or an Electronic On-Board Recorder (EOBR) connected to On-Board Diagnostics-II (OBD-II) or the Controller-Area Network (CAN) bus) and measured by the RSDs. The ratio of the emissions determined from vehicular data to their RSD-measured emissions is then used as a scale factor on all further vehicular data collected. While such hybrid models have thus far been used in PEMS-related models, such a hybrid approach can also be used to improve the accuracy of emissions modeled from connected vehicle data.