Effects of Crime on Ridesharing Platforms
Ilan Morgenstern, Professor
Business, Haas School
Applications for Spring 2026 are closed for this project.
This project explores how crime affects the operation of ridesharing platforms like Uber and Lyft. In many areas, rideshare drivers face safety concerns that shape how and where they choose to operate. Some platforms respond by allowing drivers to reject trips to high-crime areas or by excluding those zones altogether from their service maps. This raises important questions about how crime influences platform activity, driver behavior, and access to transportation, especially in neighborhoods that are already underserved. In this project, students will investigate these dynamics by analyzing transportation and crime datasets.
This is a good opportunity for students interested in the intersection of technology, urban policy, and the gig economy.
Role: More specific tasks include:
- Collecting and cleaning datasets on crime incidents and rideshare activity.
- Analyzing patterns of trip volumes, cancellations, and driver earnings across different areas.
- Conduct econometric/statistical analysis to estimate how crime levels affect driver behavior and platform metrics.
The project is especially well-suited for students considering graduate studies / a PhD in economics, statistics / data science, public policy, or operations research.
Qualifications: - Strong experience with R or Python and data analysis workflows.
- Coursework in statistics, econometrics, and machine learning.
- Ability to work independently and manage large datasets.
- Interest in economic and operational questions related to digital platforms, transportation, and urban economics.
Hours: 9-11 hrs
Related website: https://www.ilanmorgen.com/
Social Sciences