Mining regular routes from GPS data for ridesharing recommendations

Abstract

The widely use of GPS-enabled devices has provided us amount of trajectories related to individuals' activities. This gives us an opportunity to learn more about the users' daily lives and offer optimized suggestions to improve people's trip styles. In this paper, we mine regular routes from a users' historical trajectory dataset, and provide ridesharing recommendations to a group of users who share similar routes. Here, regular route means a complete route where a user may frequently pass through approximately in the same time of day. In this paper, we first divide users' GPS data into individual routes, and a group of routes which occurred in a similar time of day are grouped together by a sliding time window. A frequency-based regular route mining algorithm is proposed, which is robust to slight disturbances in trajectory data. A feature of Fixed Stop Rate (FSR) is used to distinguish the different types of transport modes. Finally, based on the mined regular routes and transport modes, a grid-based route table is constructed for a quick ride matching. We evaluate our method using a large GPS dataset collected by 178 users over a period of four years. The experiment results demonstrate that the proposed method can effectively extract the regular routes and generate rideshare plan among users. This work may help ridesharing to become more efficient and convenient.