Abstract

Traditional data collection approaches present significant drawbacks in computational costs and limited privacy protection. This research evaluates the bathtub traffic flow model as a privacy-preserving alternative to traditional methods that require detailed network layouts and individual trip data. The study assesses the feasibility of the bathtub model through calibration and validation using Bluebikes data from Metro Boston, focusing on three key components: the unified relative space paradigm, conservation equations, and the generalized bathtub model. Results demonstrate that the unified relative space paradigm successfully integrates network trips by considering remaining trip distances, though the trip distance distribution exhibited a log-normal pattern rather than the time-independent negative exponential distribution in Vickrey’s original bathtub model. Conservation equations for total trips and trip-miles traveled showed high accuracy, and the generalized bathtub model yielded accurate results, particularly for space-mean speed. This novel approach preserves privacy by eliminating the need for origin-destination data while still effectively capturing network dynamics.