Carnegie Mellon University

H. Scott Matthews

H. Scott Matthews

Professor, Civil and Environmental Engineering

Civil & Environmental Engineering
Carnegie Mellon University
Pittsburgh, PA 15213-3890


Scott Matthews is a professor in the Department of Civil and Environmental Engineering.   

Matthews’s research and teaching focuses on engineering, economic, and social decision-making under uncertainty via large datasets, computation, and visualization methods. His main 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 inspections and to implement mileage-based vehicle fees.

Previously, Matthews contributed to development of tools for environmental and energy life cycle assessment (LCA) of products and processes (such as the EIO-LCA model), estimating and tracking environmental effects across global supply chains (such as carbon footprinting), and the sustainability of infrastructure systems.

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 has taught graduate and undergraduate courses in the Departments of Economics, Civil and Environmental Engineering, Engineering and Public Policy, and Computer Science. 


PhD 1999 - Carnegie Mellon University
MS 1996 - Carnegie Mellon University
BS 1992 - Carnegie Mellon University


Research Group: AIS
Research Center: SiiCERCAMobility Data Analytics Center (MAC)

  • Use of connected vehicle communications and infrastructure to improve mobility
  • Supporting the transition from fuel taxes to mileage-based fees for transportation funding
  • Innovations in vehicle safety and emissions inspections via data analytics and connected vehicles
  • Sustainable, life cycle management of infrastructure
  • Data analytics in transportation

Applying Connected Vehicle Technology to Solve Social Problems

Most discussions of connected vehicle (CV) technologies discuss interactions such as vehicle to vehicle or vehicle to infrastructure, for example to coordinate mobility or traffic flow. However wide-scale adoption of those technologies is still many years away even in the developed world, even though the majority of vehicles sold in the past 5-10 years already have various CV technologies built-in. I am interested in leveraging CV technology to help solve problems with high social benefit in the short term. Such applications would scavenge from CV data sources, such as the routes driven at the trip level, or pertaining to vehicle performance.

The Transition to Mileage-Based Fees

For the past 90 years, the main mechanism for providing funding for ground transportation has come from federal and state fuel taxes. The federal taxes have not been increased since 1993, and factors such as inflation, improved fuel efficiency, and electric vehicles, construction and maintenance budgets have less resources available in today’s dollars. An often-proposed solution is to transition from taxes based on fuel use to fees based on miles driven.

The CV era provides the technology to pursue such strategies because data flows are available to document miles driven by vehicle. This data could be shared with agencies or third parties to create mechanisms to pay for transportation, and overcome the social challenges of vehicles not paying their fair share of providing transportation. I am interested in the engineering, economic, and social aspects of this transition.

Data Analytics for Improved Passenger Vehicle Safety

In many regions, vehicles are periodically inspected to ensure they are safe to be on the road. These programs physically assess components such as brakes, tires, and lights (not ABS or air bag systems). We have been collecting data from various regions over time that document the results of these inspection activities, have created large data analytics models, and have published estimates of failure rates in Pennsylvania.

We are also interested in comparing the performance of centralized versus decentralized inspection programs, as the failure rates are different. We have also begun to perform more detailed data analytics of the inspection data, such as to consider whether the inspection thresholds should be changed given demands for safety. For example, we have created data models and data analytics algorithms to track tire tread depth and mileage from consecutive vehicle inspection records to estimate tread deterioration and discuss whether the inspection thresholds for tire tread depth.

Reporting of Vehicle On-Board Diagnostic (OBD) Parameter for Improved Mobility and Emissions

Most developed countries across the world have programs requiring passenger vehicles to undergo a periodic inspection to identify and remedy vehicles with relatively high air pollution emissions; however, the failure rate in most jurisdictions is only 5-10%. Increasingly, these inspections are based solely on data from the vehicles’ on-board diagnostics (OBD-II) systems, as opposed to a directly measured tailpipe emissions test.

If jurisdictions could leverage connected vehicle infrastructure to monitor and assess vehicles’ OBD systems and use these data to reliably identify vehicles with a high likelihood of failing an inspection, we envision a system where jurisdictions can monitor all vehicles' OBD data, and select only those vehicles with a high likelihood of failure, to be subjected to some additional testing. This would dramatically reduce cost and inconvenience for drivers and jurisdictions. It would also provide a fast IT-based path for developing and emerging economies to follow in lieu of creating physical systems. For this vision to be viable, the remote monitoring would have to correctly identify high-emitters, i.e., we would need to develop and test machine learning models using only OBD-based data (which can already be collected remotely through CV technology), to dependably identify high-emitting vehicles


Giordano, A.,  Fischbeck, P.,  and Matthews, H. S.,. (2017). “Environmental and Economic Comparison of Diesel and Battery Electric Delivery Vans to Inform City Logistics Fleet Replacement Strategies”, Transportation Research Part D, DOI:

Seki, S. M., Griffin, W. M., Hendrickson, C, and Matthews, H. M. (2017). "Refueling and infrastructure costs of expanding access to E85 in Pennsylvania", ASCE Journal of Infrastructure Systems, Vol. 24, No. 1, 2018. DOI:

Peck, D., Matthews, H. S., Hendrickson, C., and Fischbeck, P. (2015). "An Analysis of Vehicle Safety Inspection Data in Pennsylvania: Expected Failure Rates", Transportation Research Part A, 78: 252–265.

DiPietro, G.,  Hendrickson, C. T., and Matthews, H. S.. (2014). "Estimating Economic and Resilience Consequences of Potential Navigation Infrastructure Failures: A Case Study of the Monongahela River", Transportation Research Part A, 2014, pp. 142-164. DOI:10.1016/j.tra.2014.08.009.

Nealer, R., Matthews, H. S., and Hendrickson, C.. (2012). “Assessing the Energy and Greenhouse Gas Emissions Mitigation Effectiveness of Potential US Modal Freight Policies”, Transportation Research Part A: Policy and Practice, Volume 46, Issue 3, March 2012, Pages 588–601, DOI: 10.1016/j.tra.2011.11.010.

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