TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

摘要

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called , leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a for personalized trip planning. In the , works interactively with users to generate candidate routes with . In the , applies heuristic algorithms to add user's iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.