DEVELOPING A TOUR-BASED TRIP IDENTIFICATION ALGORITHM USING MOBILE DEVICE LOCATION DATA

Abstract

This thesis presents a novel trip identification algorithm that supports travel behavior analysis based on mobile device location data. The proposed trip identification algorithm is applied to a large-scale Location-based Service (LBS) dataset consisting of the location points of a large representative sample of United States residents with over 40 million users in January 2020. Firstly, the proposed framework divides sightings into long-distance and short-distance home-based tours and then identifies the trips on each type of tour using different methods. Furthermore, the Maryland Statewide Household Travel Survey 2018/2019 and the National Household Travel Survey (NHTS) 2017 validate the derived trips. The results showed that several metrics of the trips from mobile device location data and travel surveys follow similar trends. In addition, the impact of coronavirus disease 2019 (COVID-19) on the travel behavior of the population is studied as a real-world application of the proposed algorithm.