Greg D. Erhardt

Associate Professor of Civil Engineering, University of Kentucky
Chapter Member: Kentucky SSN
Areas of Expertise:

About Greg

Erhardt's research focuses on providing the information necessary to make smart decisions about transportation infrastructure and operations. He does this by developing more sophisticated modeling tools to forecast the effects of proposed projects, analyzing new and emerging data sources to understand the effects of past projects, and communicating both to improve policy and planning decisions. Erhardt's major applications include evaluating major road and transit projects, measuring the effects of ride-hailing and other emerging modes of travel, understanding the causes of recently observed declines in public transit ridership, and planning for connected and autonomous vehicles. Overarching themes in Erhardt's writings include the mechanisms for open, objective and credible science in public decision-making, and the importance of access to Big Data to serve not only private interests, but also the public good. Erhardt serves state Departments of Transportation, transit agencies, city and regional government, and federal agencies.

In the News

Quoted by David Harrison in "Billions Spent on Roads and Transit Projects Are Often Based on Optimistic Forecasts," The Wall Street Journal, September 21, 2022.
Quoted by Nicole Dungca, Saurabh Datar, Rebecca Ostriker, Adam Vaccaro, Andrew Ryan, in "Tech and Consequences. Companies Like Uber, Lyft and Amazon Intensify Gridlock With Little Government, Part of the 3-Part Front-Page “Seeing Red” Series," The Boston Globe, November 21, 2019.
Quoted by Laura Bliss in "How Much Traffic Do Uber and Lyft Cause?," Bloomberg, August 5, 2019.
Quoted by in "British Columbia Gives Uber a Cautious Go-Ahead," The Economist, August 1, 2019.
Guest on Science Friday, May 10, 2019.
Quoted by Angie Schmitt in "Study: Uber and Lyft Caused U.S. Transit Decline," Streets Blog USA, January 22, 2019.


"Why Has Public Transit Ridership Declined in the United States?" (with Jawad Mahmud Hoque, Vedant Goyal, Simon Berrebi, Candace Brakewood, and Kari E. Watkins). Transportation Research Part A: Policy and Practice 161 (2022): 68-87.

Reports between 2012 and 2018, bus ridership in the US declined 15% and rail ridership declined 3%. Identifies the factors responsible and quantifies the contributions of each. Finds that ride-hailing is the biggest contributor to transit ridership decline, with higher fares, incomes, teleworking and car ownership, and lower gas prices also contributing.

"Estimating the Uncertainty of Traffic Forecasts From Their Historical Accuracy" (with Jawad Mahmud Hoque, David Schmitt, Mei Chen, and Martin Wachs). Transportation Research Part A: Policy and Practice 147 (2021): 339-349.

Provides a method and recommendations for how to incorporate uncertainty into road project forecasts and decisions.

"The Changing Accuracy of Traffic Forecasts" (with Jawad Mahmud Hoque, David Schmitt, Mei Chen, Ankita Chaudhary, Martin Wachs, and Reginald R. Souleyrette). Transportation, no. 49 (2021): 445–466.

Discusses the largest study of its kind, comparing traffic forecasts to post-opening outcomes.

"Do Transportation Network Companies Increase or Decrease Transit Ridership? Empirical Evidence From San Francisco" (with R. A. Mucci , Drew Cooper, Bhargava Sana, Mei Chen, and Joe Castiglione). Semantic Scholar (2021).

Quantifies the effect of ride-hailing on transit ridership in San Francisco.  Finds that between 2010 and 2015, ride-hailing’s introduction led to 10% less bus ridership and no significant effect on rail ridership.

"Do Transportation Network Companies Decrease or Increase Congestion?" (with Sneha Roy, Drew Cooper, Bhargava Sana, Mei Chen, and Joe Castiglione). Science Advances 5, no. 5 (2019).

Examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Uses data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco.

"Traffic Forecasting Accuracy Assessment Research, National Cooperative Highway Research Program Report 934 ," (with Schmitt, D., Hoque, Hoque, J., Chaudhary, A., Rapolu, S., Kim K., Weller, S., Sall, E., Chen, M., Souleyrette, R., Wachs, and M.), Transportation Research Board of the National Academies, July 1, 2019.

Provides empirical evidence on traffic forecast accuracy and develops a tool to assist decision makers faced with managing the uncertainty associated with forecasts.

"Understanding the Recent Transit Ridership Decline in Major US Cities: Service Cuts or 2 Emerging Modes?," (with Michael Graehler, Jr., and Richard Alexander Mucci), 2019.

Shows that the decline in 22 large cities is correlated with the entry of ride-hailing providers, such as Uber and Lyft, into the market, suggesting a diversion of transit users into cars.

"Recommendations for Big Data Programs at Transportation Agencies" (with Batty, M., Arcaute, and E.), in Big Data for Regional Science, edited by Laurie A. Schintler, Zhenhua Chen (Routledge, 2017), 292-303.

Provides recommendations for planning and establishing big data programs at transportation agencies, focusing topics such as identifying and prioritizing data sources and uses, managing privacy considerations and data sharing policies, and addressing data issues that may arise in contracting situations.

"Estimating Emissions Benefits of Bicycle Facilities with Stand-Alone Software Tools: Incremental Nested Logit Analysis of Bicycle Trips in California's Monterey Bay Area" (with Jeffrey Hood and Christopher Frazier). Journal of the Transportation Research Board 2430, no. 1 (2014).

Uses GPS traces from bicyclists' smartphones to develop a software tool to estimate the emissions benefits of building bike lanes. Mentions how the tool is used by regional government staff to prioritize emission reduction strategies and shows that bike lanes may benefit users substantially but have limited air quality impacts.

"Evaluating Regional Pricing Strategies in San Francisco--Application of the SFCTA Activity-Based Regional Pricing Model" (with E. Sall and Elizabeth M. Bent). Semantic Scholar (2010).

Entails how San Francisco would consider a congestion pricing strategy that would toll vehicles entering the downtown area during peak hours. Presents the tools and process used to evaluate that proposal.