With the emergence of smartphone technologies already becoming an essential part of most commuters’ daily activity, the opportunity for integrating this source of “Big Data” with transportation planning and analysis procedures and techniques is becoming a prevalent practice within the industry. This research provides a robust application for collecting and analyzing trajectory data from smartphone devices. Processing such extensive data is able to compliment current data collection techniques (e.g. travel surveys) in an efficient and cost effective manner. More specifically, our technique describes a user’s movements through a transportation network in a high resolution format that includes both spatial and temporal dimensions. Thus, providing meaningful data to aid the planning community identify detailed characteristics and trends of users’ travel behavior patterns associated with trip chaining, frequently visited locations, trip distance, travel time and so on.
The process of data collection based on smartphone technology will firstly be presented, followed by the methodology of examining GPS trajectory for trip analysis purpose. Based on the complete trajectories of users, the system will flag points that represent a start/end point of a trip, then the user trip chains can be extracted from the raw GPS trajectory data. By applying the spatial clustering algorithms, the frequency of each frequently visited locations, either within a group of users or individually, can be determined and visualized either graphically or on a GIS platform. Typical types of those locations, e.g. home, office can also be determined associating with the time dimension of the GPS trajectory points, which will greatly facilitate the trip purpose determination.