Emissions inspection and maintenance (I/M) programs for light-duty motor vehicles manage ambient air quality by enforcing emissions standards and requiring non-compliant vehicles to be repaired or retired. I/M programs in the United States typically identify over-emitters through on-board diagnostics (OBD) systems and vehicles’ proprietary firmware (i.e., indirect tests), rather than through physical measurements of exhaust gases (i.e., tailpipe tests). Analyzing data from Colorado’s I/M program, this study finds the OBD test to have an accuracy of 87%, but a false pass rate of 50%, when predicting the result of a corresponding tailpipe test. As an alternative, transparent data-driven models—using logistic regression and gradient boosting machines—to consistently identify over-emitting vehicles are proposed. These models were up to 24% more accurate, or 85% more sensitive than the current OBD test in a stratified data sample. A key benefit of transparent statistical models— jurisdictions’ ability to tune the test methods to best suit program needs—is also assessed. Finally, this study shows how these results support a vision for cloud-based, selective I/M programs where statistical models are applied to OBD data—collected over-the- air from vehicles—to identify and require additional inspection for only the most probable over-emitters.
Scott is a professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University. His research and teaching focuses on engineering, economic, and social decision-making under uncertainty via large datasets, computation, and visualization methods. His current interests are in the use of connected vehicle technologies to provide high-resolution data on vehicle performance and use to improve mobility. Examples of particular topics of interest include using such data to improve vehicle safety and emissions, and to implement mileage-based vehicle fees.
Matthews has served as chair of the Committee on Sustainable Systems and Technology with the Institute of Electrical and Electronic Engineers and on the Executive Committee for the American Center for Life Cycle Assessment. He participated in the National Research Council study on the Hidden Costs of Energy and was a member of the NRC Board on Environmental Studies and Toxicology. He is currently involved in ASCE and TRB committees related to data and connected vehicles.
At Carnegie Mellon, he is a member of the Green Design Institute, an interdisciplinary research consortium at Carnegie Mellon focused on modeling energy and environmental problems in the developing world. He has taught graduate and undergraduate courses in the Departments of Economics, Civil and Environmental Engineering, Engineering and Public Policy, and Computer Science.