Abstract:

Despite its emergence as an important urban travel mode, Chicago is among only a handful of cities with access to data on all ride-hailing trips that occur in their city. This lack of data limits our understanding of the effects of ride-hailing and the effectiveness of strategies that cities can use to manage those effects. In this research, we use Chicago’s data in combination with other open data to estimate a direct-ridership model of ride-hailing trips between each Census tract pair. We apply the model to two case studies:
1. We evaluate the effect of Chicago’s ride-hailing Traffic Congestion Mitigation (TCM) Tax that went into effect in January 2020.
2. After testing the model out-of-sample and comparing to publicly available ride-hailing data for cities in Massachusetts, we apply the model to estimate the ride-hailing trips in every Census tract in Kentucky and the sensitivity to price changes. This application provides an order-of-magnitude estimate for planners interested in understanding the potential for automated vehicle passenger services in rural Kentucky.
In ongoing work, we consider strategies for ride-hailing pricing schemes that would price trips that compete with transit and subsidize those that complement it.

Bio:

Greg Erhardt is a Raymond-Blythe Associate Professor of Civil Engineering at the University of Kentucky, a Hans Fischer Fellow at the Institute for Advanced Study, Technical University of Munich, and a Senior Advisor to Bay Area Metro. He brings more than 20 years of experience in both research and practice developing and applying metropolitan and statewide travel demand models. Dr. Erhardt is the recipient of the 2023 Zephyr Foundation Leadership Award, and his research on ride-hailing has been covered in publications including the Boston Globe, the Economist and NPR’s Science Friday. He is a 2001 MS graduate in Civil Engineering from Northwestern University.