Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks.

“最通航路径”问题的输入包括以下内容：（a）以有向图表示的道路网络，其中每个边都与成本和“通航性得分”值的数值属性相关联；（b）来源和目的地；及 （c）预算值，表示解决方案的最大允许成本。有了输入，MNP的目的是确定在源和目的地之间的路径，该路径可以最大化可导航性分数，同时将其成本限制在给定的预算值之内。可以将该问题建模为已知为NP难的弧向定向问题。当前针对该问题的最新技术可能会生成具有环路的路径，并且发现它对生成简单路径的MNP的适配效率低下。在本文中，我们针对MNP问题提出了五种新颖的算法。我们的算法首先计算从源到目的地的种子路径，然后修改种子路径以提高其可导航性。我们探索两种方法来计算种子路径。为了修改种子路径，我们探索了基于动态规划的不同方法。我们还提出了MNP问题的索引结构，这有助于减少某些算法的运行时间。我们的实验结果表明，所提出的解决方案可产生可比或更好的解决方案，但比大型现实道路网络的最新技术要快几个数量级。我们探索了不同的基于动态编程的方法。我们还提出了MNP问题的索引结构，这有助于减少某些算法的运行时间。我们的实验结果表明，所提出的解决方案可产生可比或更好的解决方案，但比大型现实道路网络的最新技术要快几个数量级。我们探索了不同的基于动态编程的方法。我们还提出了MNP问题的索引结构，这有助于减少某些算法的运行时间。我们的实验结果表明，所提出的解决方案可产生可比或更好的解决方案，但比大型现实道路网络的最新技术要快几个数量级。