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Efficient and Effective Route Planning in Road Networks with Probabilistic Data using Skyline Paths

In this paper, we study the problem of effective route search in road networks. Given a pair of source and destination locations, the aim is to find a path from the source to the destination that visits k different types of sites in a particular order as prescribed by the user. The route planning problem has two objectives to optimize: minimize the total path length and maximize the probability of getting served from the k sites. Since the problem has a multi-objective nature, we utilize the skyline setting and retrieve all skyline paths according to the two aggregated attributes. The naïve way of determining the path lengths can involve a large number of shortest path computations. Although the shortest paths between the sites can be pre-computed, the shortest paths from the source to the first type of site and those from the last type of site to the destination cannot be computed in an offline manner as the source and destination are arbitrary points that are available only at runtime. Similarly, the choice and order of the k different types of sites are also specified at runtime only. Since in a large road network, it is prohibitory to compute many shortest paths, we employ a heuristic to approximately solve the problem. The shortest path computation from the source to a site (and similarly, from a site to the destination) is approximated by introducing reference points. The reference points are chosen by employing a grid-based partitioning method on the space underlying the road network. We show that the above heuristic introduces only an additive error to the distance but not to the probability of service while reducing the running times by up to orders of magnitude.