Privacy-Preserving Human MObility Trajectory Data GeneratOr

People travel and leave their location history as mobility trajectory data. This data is invaluable for a wide range of location-based services, including city planning, traffic control, disease transmission and optimal control, trade area analysis, location-based marketing, and tourism, consequently leading to the rise of new business models. Recognising these opportunities, governments and industries have increasingly invested in exploring data-driven solutions. However, data challenges persist due to reduced data resolution from privacy concerns, high deployment and processing costs, and conflicts of commercial interests. Recently, ethical considerations regarding human location data have become even more critical. It remains unclear how organisations, regulators, and policymakers can effectively support the use, sharing, and reuse of location data across the geospatial ecosystem in ways that maximise public benefits. There is an urgent need to address these spatial data (not limited to trajectory data) access challenges, particularly for researchers and data analysts. We propose using synthetic data sets as an alternative to the original data sets, which could provide equal access, lower costs, and minimise ethical risks. Along this long-term pathway, this project aims to develop a prototype human mobility trajectory data generator, producing synthetic human trajectory data from original mobile in-app data. The synthetic data should enhance the original data while preserving essential human mobility patterns. The basic data will be in the form of trip-activity chains. Derived data sets could be in any commonly used format, e.g., counts, origin-destination matrix and indicators, meeting various applications in urban analytics and eliminating ethical concerns. There is a broad range of potential end-users, including the transport planning department, local governments and organisations, mobility service and facility providers and ITS-relevant industries.