Time-sensitive route planning using location-based data

Location-based services allow users to perform geo-spatial recording actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale time-stamped location sequence data (e.g. check-ins and GPS traces). We argue a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a route goodness function which aims to measure the quality of a route. Equipped with the route goodness, we recommend time-sensitive routes for two scenarios. The first is about constructing the route based on the user-specified source location with the starting time. The second is about composing the route between the specified source location and the destination location given a starting time. To handle these queries, we propose a search method, Guidance Search, which consists of a novel heuristic satisfaction function which guides the search towards the destination location, and a backward checking mechanism to boost the effectiveness of the constructed route. Experiments on the Go Walla check-in datasets demonstrate the effectiveness of our model on detecting real routes and performing cloze test of routes, comparing with other baseline methods.