Efficient computation of safest nearby neighbors in road networks

Abstract

Traditional route planning and k nearest neighbors queries only consider distance or travel time and ignore road safety altogether. However, many travellers prefer to avoid risky or unpleasant road conditions such as roads with high crime rates (e.g., robberies, kidnapping, riots etc.) and bumpy roads. To facilitate safe travel, we introduce a novel query for road networks called the k safest nearby neighbors (kSNN) query. Given a query location vl, a distance constraint dc and a point of interest pi, we define the safest path from vl to pi as the path with the highest path safety score among all the paths from vl to pi with length less than dc. The path safety score is computed considering the road safety of each road segment on the path. Given a query location vl, a distance constraint dc and a set of POIs P , a kSNN query returns k POIs with the k highest path safety scores in P along with their respective safest paths from the query location. We develop two novel indexing structures called Ct-tree and a safety score based Voronoi diagram (SNVD). We propose two efficient query processing algorithms each exploiting one of the proposed indexes to effectively refine the search space using the properties of the index. Our extensive experimental study on real datasets demonstrates that our solution is on average an order of magnitude faster than the baselines.