Safest Nearby Neighbor Queries in Road Networks (Full Version)

TL;DR

This paper introduces the k safest nearby neighbors (kSNN) query for road networks, which finds the safest paths to points of interest considering road safety, and develops novel indexes and algorithms for efficient processing.

Contribution

It proposes the kSNN query, along with two new indexing structures, Ct-tree and SNVD, enabling efficient safe path queries in road networks.

Findings

The proposed methods are on average ten times faster than baseline approaches.

The new indexes effectively reduce search space and improve query efficiency.

Experimental results validate the effectiveness of the proposed approach.

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 $v_l$, a distance constraint $d_c$ and a point of interest $p_i$, we define the safest path from $v_l$ to $p_i$ as the path with the highest path safety score among all the paths from $v_l$ to $p_i$ with length less than $d_c$. The path safety score is computed considering the road safety of each road segment on the path. Given a query location $v_l$, a distance constraint $d_c$ 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.