Observing Individual Dynamic Choices of Activity Chains From Location-Based Crowdsourced Data

The existing efforts on studying human mobility and activity using location-based crowdsourced data mainly focus on obtaining the activity chain pattern in a region at an aggregate level. To observe individual dynamic choices of activity chains, this paper presents a data-driven approach to estimate individual-specific activity chain set and corresponding choice probabilities for a given person over a 24-hour period using crowdsourced data from location-based service apps. Based on the time geography theory, the authors refine a space-time bicone concept to construct the individual-specific activity chain set. These space-time bicone constraints define a set of potential activity location spaces to reduce the search space of activity location and duration choices in activity-based travel demand models. They demonstrate the proposed approach through conducting numerical analyses using crowdsourced data from a location-based service app - Foursquare to construct individual-specific activity choice sets and corresponding choice probabilities.