Finding the most navigable path in road networks

• Published in: GeoInformatica Vol. 25; no. 1; pp. 207 - 240
• Main Authors: Kaur, Ramneek, Goyal, Vikram, Gunturi, Venkata M. V.
• Format: Journal Article
• Language: English
• Published: New York Springer US 2021; Springer; Springer Nature B.V
• Subjects: Adaptation; Algorithms; Budgets; Computer Science; Data Structures and Information Theory; Dynamic programming; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Graph theory; Graphical representations; Information Storage and Retrieval; Multimedia Information Systems; Orienteering; Roads; Spatial networks; Routing; Road networks
• ISSN: 1384-6175; 1573-7624
• DOI: 10.1007/s10707-020-00428-5

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.