Abstract
Transportation agencies increasingly rely on detailed trip data to analyze traffic patterns and plan infrastructure improvements. However, traditional data collection methods require extensive personal information about travelers’ origins, destinations, and routes, raising serious privacy concerns. Current “big data” approaches can track individual movements with alarming precision, often without explicit consent. As privacy regulations tighten and public concerns grow, transportation planners need alternative methods that balance analytical needs with privacy protection. To address this challenge, the research team evaluated the “bathtub model” as a privacy-preserving alternative to traditional traffic data collection methods. This simple, network-level approach treats all trips in a region as part of one system. Instead of tracking each person’s path, a bathtub model represents trips by how much distance they have left to travel. This allows for analyzation of network performance while protecting privacy.
