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
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.
Discusses the largest study of its kind, comparing traffic forecasts to post-opening outcomes.
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.
Provides a method and recommendations for how to incorporate uncertainty into road project forecasts and decisions.
Provides empirical evidence on traffic forecast accuracy and develops a tool to assist decision makers faced with managing the uncertainty associated with forecasts.
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.
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.
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.
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.
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.