Abstract

Path queries such as “finding the shortest path in travel time from my hotel to the airport” are heavily used in many applications of road networks. Currently, simple statistic aggregates such as the average travel time between two vertices are often used to answer path queries. However, such simple aggregates often cannot capture the uncertainty inherent in traffic. In this paper, we study how to take traffic uncertainty into account in answering path queries in road networks. To capture the uncertainty in traffic such as the travel time between two vertices, the weight of an edge is modeled as a random variable and is approximated by a set of samples. We propose three novel types of probabilistic path queries using basic probability principles: (1) a probabilistic path query asks a question like “what are the paths from my hotel to the airport whose travel time is at most 30 minutes with a probability of at least 90%?”; (2) a weight-threshold top-k path query asks a question like “what are the top-3 paths from my hotel to the airport with the highest probabilities to take at most 30 minutes?”; and (3) a probability-threshold top-k path query asks a question like “in terms of the travel time of a path guaranteed by a probability of at least 90%, what are the top-3 shortest paths from my hotel to the airport?” To evaluate probabilistic path queries efficiently, we develop three methods: (1) a two-phase exact algorithm using effective bounding and pruning techniques; (2) a sampling algorithm; and (3) a hierarchical approximation algorithm for large networks based on pre-computation. An extensive empirical study using real road networks and synthetic data sets shows the effectiveness of the proposed path queries and the efficiency of query evaluation methods.