TRAPP: An Efficient Point-to-Point Path Planning Algorithm for Road Networks with Restrictions

TL;DR

TRAPP is a novel path planning algorithm that efficiently handles road restrictions by leveraging traffic flow data to optimize index construction, reducing computational and memory overhead in real-world road networks.

Contribution

It introduces a traffic flow-based filtering approach to manage road restrictions, significantly improving efficiency over naive methods that build multiple indices for all restriction combinations.

Findings

Reduces index construction time and memory usage.

Maintains efficient path planning despite restrictions.

Effective on real-world road network data.

Abstract

Path planning is a fundamental problem in road networks, with the goal of finding a path that optimizes objectives such as shortest distance or minimal travel time. Existing methods typically use graph indexing to ensure the efficiency of path planning. However, in real-world road networks, road segments may impose restrictions in terms of height, width, and weight. Most existing works ignore these road restrictions when building indices, which results in returning infeasible paths for vehicles. To address this, a naive approach is to build separate indices for each combination of different types of restrictions. However, this approach leads to a substantial number of indices, as the number of combinations grows explosively with the increase in different restrictions on road segments. In this paper, we propose a novel path planning method, TRAPP(Traffic Restrictions Adaptive Path Planning algorithm), which utilizes traffic flow data from the road network to filter out rarely used road restriction combinations, retain frequently used road restriction combinations, and build indices for them. Additionally, we introduce two optimizations aimed at reducing redundant path information storage within the indices and enhancing the speed of index matching. Our experimental results on real-world road networks demonstrate that TRAPP can effectively reduce the computational and memory overhead associated with building indices while ensuring the efficiency of path planning.