Abstract

The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The main objective of our study is to extract the human mobility patterns from GPS traces in order to derive an indicator for enhancing Collaborative Mobility (CM) between individuals. The first step, extracting activity duration and location, is done using state-of-the-art automated recognition tools. Sensors data are used to reconstruct individual’s activity location and duration across time. For constructing the indicator, in a second step, we defined different variables and methods for specific case studies. Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator. Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling and reordering activities). View Full-Text