Finding the Most Navigable Path in Road Networks: A Summary of Results
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. This problem finds its applications in navigation systems for developing nations where streets, quite often, do not display their names. MNP problem would help in such cases by providing routes which are more convenient for a driver to identify and follow. Our problem is 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 two novel algorithms for the MNP problem. 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. We also propose an indexing structure for the MNP problem which significantly reduces the running time of our algorithms.
This work was in part supported by the Infosys Centre for Artificial Intelligence at IIIT-Delhi, Visvesvaraya Ph.D. Scheme for Electronics and IT, and DST SERB (ECR/2016/001053).
- 4.Bolzoni, P., Persia, F., Helmer, S.: Itinerary planning with category constraints using a probabilistic approach. In: Benslimane, D., Damiani, E., Grosky, W.I., Hameurlain, A., Sheth, A., Wagner, R.R. (eds.) DEXA 2017. LNCS, vol. 10439, pp. 363–377. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64471-4_29CrossRefGoogle Scholar
- 5.Chekuri, C., Pal, M.: A recursive greedy algorithm for walks in directed graphs. In: Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2005, pp. 245–253 (2005)Google Scholar
- 9.Kanoulas, E., Du, Y., Xia, T., Zhang, D.: Finding fastest paths on a road network with speed patterns. In: 22nd International Conference on Data Engineering (ICDE 2006), p. 10, April 2006Google Scholar
- 10.Kriegel, H.P., Renz, M., Schubert, M.: Route skyline queries: a multi-preference path planning approach. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), pp. 261–272 (2010)Google Scholar
- 11.Lu, Y., Shahabi, C.: An arc orienteering algorithm to find the most scenic path on a large-scale road network. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 46:1–46:10 (2015)Google Scholar
- 13.Singh, A., Krause, A., Guestrin, C., Kaiser, W., Batalin, M.: Efficient planning of informative paths for multiple robots. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI 2007, pp. 2204–2211 (2007)Google Scholar