Query processing in time-dependent spatial networks

Recent advances in online map services and their wide deployment in hand-held devices and car-navigation systems have led to extensive use of location-based services. The most popular class of such services are route planning and k-nearest neighbor(kNN) queries where users search for geographical points of interests (e.g., restaurants, gas stations) with corresponding travel-times to these locations. Accordingly, many recent studies focused on developing efficient techniques to answer point-to-point fastest path and k-nearest neighbor search queries in the spatial network space. However, most of the existing approaches in spatial networks make the simplifying assumption that the cost of traveling each edge of the spatial network is constant (e.g., corresponding to the length of the edge). Whereas in real world, the actual travel cost of a network edge is time-dependent i.e., the cost of a network edge depends on the arrival-time to that edge. Unfortunately, once we consider the time-dependent edge weights in road networks, all proposed kNN and shortest path query solutions assuming constant edge weights fail. With time-dependent edge costs, the network distance between two nodes is not unique and varies based on the departure time from the source. This dynamism of the distance introduces great challenges in developing precomputation techniques to expedite spatial query processing in time-dependent spatial networks. In this thesis, for the first time we study the problem of k-nearest neighbor search in time-dependent road networks where the weight of each edge is a function of time. We propose series of index structures that efficiently and accurately answer the k nearest neighbor queries in time-dependent road networks and effectively handle the database updates where points of interests are added or removed. In addition, we study the problem of point-to-point shortest path computation in time-dependent spatial networks and present a technique which speeds-up the path computation using a bidirectional time-dependent A* search based on a novel heuristic function. In this thesis, the efficacy of all proposed techniques for both kNN search and point-to-point shortest path computation have been verified with extensive experiments using real data-sets including a variety of large spatial networks with real traffic data.