Traveler's Next Activity Predication with Location-Based Social Network Data

Abstract

The rise of technology and the internet provides powerful means for people from all around the world to communicate and connect with one another. Online social network platforms become go- to places for users to express and share their individuality, which includes choice of activities, locations, and associated timestamps. In turn, their opinions affect the point of view of others, who are in their online friendship circle. Users’ increasing usage of social networks help accumulate massive amount of data that can be further explored. Particularly, this type of data attracts and allows researchers, who are interested in studying and understanding how social factors and previous experience influence user behavior in terms of activity-related travel choice. In this paper, the goal is to utilize such rich data sources to build a model that predicts user next activity. Such model contributes a powerful tool for integrating the location prediction with transportation planning and operations processes. Besides, it is valuable in commercial applications to create better recommendation systems with higher accuracy and ultimately attract more customers to partnering businesses. By studying the dataset, which contains millions of historical check-ins from thousands of users, it is possible to derive information that is useful in predicting user next activity. The proposed approach applies machine learning techniques on the collected features to deliver highly accurate prediction results with fast training and prediction time.