Efficient Path Routing Over Road Networks in the Presence of Ad-Hoc Obstacles (Technical Report)

TL;DR

This paper introduces a novel path routing method over road networks that accounts for ad-hoc obstacles like weather and keyword-based restrictions, optimizing for travel time while avoiding obstacles.

Contribution

It formulates the PRAO problem considering ad-hoc obstacles and proposes pruning and indexing techniques for efficient query processing.

Findings

Proposed effective pruning methods and indexing mechanisms.

Achieved significant improvements in query efficiency and accuracy.

Validated approaches on real and synthetic datasets.

Abstract

Nowadays, the path routing over road networks has become increasingly important, yet challenging, in many real-world applications such as location-based services (LBS), logistics and supply chain management, transportation systems, map utilities, and so on. While many prior works aimed to find a path between a source and a destination with the smallest traveling distance/time, they do not take into account the quality constraints (e.g., obstacles) of the returned paths, such as uneven roads, roads under construction, and weather conditions on roads. Inspired by this, in this paper, we consider two types of ad-hoc obstacles, keyword-based and weather-based obstacles, on road networks, which can be used for modeling roads that the returned paths should not pass through. In the presence of such ad-hoc obstacles on roads, we formulate a path routing query over road networks with ad-hoc obstacles (PRAO), which retrieves paths from source to destination on road networks that do not pass ad-hoc keyword and weather obstacles and have the smallest traveling time. In order to efficiently answer PRAO queries, we design effective pruning methods and indexing mechanism to facilitate efficient PRAO query answering. Extensive experiments have demonstrated the efficiency and effectiveness of our approaches over real/synthetic data sets.