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

TL;DR

This paper tackles path planning in road networks with height, width, and weight restrictions, where traditional index-based methods suffer from a combinatorial explosion of restriction combinations. It introduces TRAPP, which uses traffic-flow data to filter restriction combinations per dense cell, derives compact representation vectors via clustering to capture representative vehicle dimensions, and applies cell-aware refinement to map these vectors to a small set of feasible restriction combinations. Two optimizations—Shortcuts Merger and Shortcuts Pre-sorting—reduce memory usage and speed up shortcut matching, while building shortcuts only for retained combinations keeps online queries fast. Experiments on six OpenStreetMap-based networks show TRAPP achieves large reductions in indexing overhead and memory, while delivering high feasibility and near-optimal path quality, making restriction-aware path planning scalable for real-world road networks.

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.

Figures (17)

• Figure 1: An illustrative example to show the combinatorial explosion problem in shortcut construction for a road network cell with height (he), width (wi), and weight (wt) restrictions. For this road network cell, $v_7$ is the entry vertex and $v_6$ is the exit vertex. A vehicle with a height of 1.5, width of 2.0 and weight of 2.0 wants to travel from the entry vertex to the exit vertex.
• Figure 2: An example to illustrate the process of shortcut building by existing methods. The vertices of the road network represent the intersections, the edges represent roads, and the shortcuts represent the record of pre-computed shortest paths.
• Figure 3: An example to illustrate the principle of combination rematch. The dashed boxes contain the currently existing restriction combinations, where he, wi, and wt denote height, width, and weight, respectively.
• Figure 4: An illustration to show how different storage methods store shortcut paths.
• Figure 5: Path planning time comparison.

Theorems & Definitions (3)

• Definition 1: representation vector
• Definition 2: representation vector mapping
• Definition 3: Feasible Edge Set