Spatial Intelligence - Route Prediction and Planning Base on Geospatial Information

Abstract

With the advances in localization techniques and popularity of mobile devices, Spatial Intelligence penetrated people's lives. What is Spatial Intelligence? It is 1) able to provide “smart” and intelligent services to assist people to make decisions, 2) based on efficient data processing and interactive data analytic, and 3) using big data on Location-based Social Networks. In this proposal, I focus two problems of Spatial Intelligence about route prediction and planning.

First, we study the problem of recommending time-sensitive venue sequence for mobile users using their check-in footprints on LBSNs. Most of the current studies in Point of Interest (POI) recommendation and prediction fail to address the following key challenges: (1) how to recommend an optimal time-sensitive visit sequence where each point's time is specified by users, (2) how to handle the scenario where the user-location matrix is very sparse (i.e., each user has a very limited number of check-ins, or to say, cold-start users), and (3) how to dig deep into the user's behavior to assist the recommendation system. Motivated by the challenges above, we propose a predictive framework that enables time-sensitive location sequence recommendation leveraging both the users' preference and social opinions, especially for cold-start users.

Next, we presents an exact solution and a heuristic solution to a UAV-assisted parcel delivery problem, in which UAVs can only be operated in Visual-Line-Of-Sight (VLOS) areas. In our proposed problem, we assume that trucks travel on road networks, and UAVs move in Euclidean spaces and can launch at any locations on roads. We first demonstrate the overview of our exact solution that iterates all permutations of destinations for an optimal delivery route.Given a specific delivery order, an intuitive approach needs to check all possible locations on roads in the VLOS areas and find a globally optimal location for every destination if UAVs are used for delivery. To avoid high computational cost of searching the optimal location at run-time, we propose an advanced index-based alternative, which computes optimal delivery routes in a pre-processing stage. Due to the nature of NP-hard problems, we also propose a heuristic approach that utilizes delivery groups for the proposed problem of practical size. All proposed solutions are evaluated through extensive experiments.