A Crowd-enabled Solution for Privacy-Preserving and Personalized Safe Route Planning for Fixed or Flexible Destinations (Full Version)

TL;DR

This paper presents a privacy-preserving, crowd-enabled safe route planning system that personalizes safety scores without centralized data sharing, efficiently finding safe routes for individuals and groups within user-defined distance constraints.

Contribution

It introduces a novel decentralized safe route planning model that preserves user privacy and incorporates personalized safety scores based on crowd experiences.

Findings

Finds the safest route in seconds using real datasets.

Reduces safety score exposure by 47% with an iterative algorithm.

Effectively supports both fixed and flexible destination planning.

Abstract

Ensuring travelers' safety on roads has become a research challenge in recent years. We introduce a novel safe route planning problem and develop an efficient solution to ensure the travelers' safety on roads. Though few research attempts have been made in this regard, all of them assume that people share their sensitive travel experiences with a centralized entity for finding the safest routes, which is not ideal in practice for privacy reasons. Furthermore, existing works formulate safe route planning in ways that do not meet a traveler's need for safe travel on roads. Our approach finds the safest routes within a user-specified distance threshold based on the personalized travel experience of the knowledgeable crowd without involving any centralized computation. We develop a privacy-preserving model to quantify the travel experience of a user into personalized safety scores. Our algorithms for finding the safest route further enhance user privacy by minimizing the exposure of personalized safety scores with others. Our safe route planner can find the safest routes for individuals and groups by considering both a fixed and a set of flexible destination locations. Extensive experiments using real datasets show that our approach finds the safest route in seconds. Compared to the direct algorithm, our iterative algorithm requires 47% less exposure of personalized safety scores.