Harvesting active travel from smartphone app data: opportunities and challenges

Collecting high-quality data on active travelers - pedestrians, cyclists and users of personal mobility devices (PMD) - continues to present challenges for transport analysts. The increasing penetration of smartphones, travel apps, and improved algorithms to infer modal trips, provides potential to collect this information with minimal participant involvement. However, challenges remain around how to expand this app-based data, which is typically limited to a sample of users, to the population at large. The current paper details the design, testing and validation of a new approach for extracting active trips from users of smartphone devices across Australia. An algorithm is developed to extract and route active trips from a digital multimodal journey planner dataset, called TripGo®, which is validated against secondary sources of pedestrian and bicycle counts in an iterative process. Results are encouraging, primarily around the ability to quickly generate reliable estimates of pedestrian activities and hotspots in urban centres from travel app data. Estimates of cycling, PMD users and regional/rural trips are less reliable due to limited coverage in the app data, pointing to the need to incorporate multiple secondary datasets to refine the approach and improve the accuracy and scalability of estimates. By enabling the integration of diverse datasets, the methodology provides a practical framework for generating population-level estimates and actionable insights.