Author: Wenlong Jin and Joseph Lo
Institute of Transportation Studies, University of California, Irvine
At ITS-Irvine, researchers are pioneering innovative methods that balance the power of data with the need to protect individual privacy. In this study, Dr. Wen-Long Jin and Joseph Lo explores how a “bathtub model” can serve as a privacy-preserving alternative to traditional traffic data collection. Transportation agencies depend on detailed trip data to improve roads and transit systems, yet conventional and “big data” methods often track travelers’ movements with concerning precision—raising ethical and legal challenges under growing privacy regulations.
The bathtub model offers a fresh, system-level approach that captures the overall flow of trips across a network without identifying individual travelers. By representing travel based on the remaining distance rather than specific routes, the model enables meaningful insights into network performance while keeping personal data anonymous. This work demonstrates that it’s possible to advance transportation planning and analytics without compromising public trust or individual privacy.
Key Research Findings
- The bathtub model effectively captures network traffic dynamics while preserving traveler privacy
- The bathtub model was successfully validated using public bike-sharing data
- Computational needs are low, enabling rapid assessments
Stay connected with ITS-Irvine for more insights on mobility innovation. To learn more...
Read about this project Read the Policy Brief