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.

翻译：路径规划是路网中的基础问题，其目标在于寻找满足最短距离或最少通行时间等优化目标的路径。现有方法通常采用图索引技术以保证路径规划的效率。然而，在实际路网中，路段常存在高度、宽度及载重等通行限制。多数现有工作在构建索引时忽略了这些道路限制，导致为车辆返回不可行的路径。针对此问题，一种简单方法是为不同类型的限制条件组合分别建立索引。但随着路段限制种类的增加，组合数量呈爆炸式增长，该方法将导致索引数量急剧膨胀。本文提出一种新颖的路径规划方法TRAPP（交通限制自适应路径规划算法），该方法利用路网中的交通流数据过滤极少使用的道路限制组合，保留常用道路限制组合并为其构建索引。此外，我们引入两项优化措施：旨在减少索引中冗余路径信息存储，并提升索引匹配速度。在真实路网上的实验结果表明，TRAPP能够在保证路径规划效率的同时，有效降低索引构建的计算与内存开销。