Finding the Most Navigable Path in Road Networks: A Summary of Results

  • Ramneek KaurEmail author
  • Vikram Goyal
  • Venkata M. V. Gunturi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11029)


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).


  1. 1.
    Aly, A.M., et al.: AQWA: adaptive query workload aware partitioning of big spatial data. Proc. VLDB Endow. 8(13), 2062–2073 (2015)CrossRefGoogle Scholar
  2. 2.
    Archetti, C., Corberán, A., Plana, I., Sanchis, J.M., Speranza, M.G.: A branch-and-cut algorithm for the orienteering arc routing problem. Comput. Oper. Res. 66(C), 95–104 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bolzoni, P., Helmer, S.: Hybrid best-first greedy search for orienteering with category constraints. In: Gertz, M., et al. (eds.) SSTD 2017. LNCS, vol. 10411, pp. 24–42. Springer, Cham (2017). Scholar
  4. 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). Scholar
  5. 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
  6. 6.
    Delling, D., Goldberg, A.V., Nowatzyk, A., Werneck, R.F.: Phast: hardware-accelerated shortest path trees. J. Parallel Distrib. Comput. 73(7), 940–952 (2013)CrossRefGoogle Scholar
  7. 7.
    Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G., Vathis, N.: Approximation algorithms for the arc orienteering problem. Inf. Process. Lett. 115(2), 313–315 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jing, N., Huang, Y.W., Rundensteiner, E.A.: Hierarchical encoded path views for path query processing: an optimal model and its performance evaluation. IEEE Trans. Knowl. Data Eng. 10, 409–432 (1998)CrossRefGoogle Scholar
  9. 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. 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. 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
  12. 12.
    Martins, E., Pascoal, M.: A new implementation of Yen’s ranking loopless paths algorithm. Q. J. Belg. French Ital. Oper. Res. Soc. 1(2), 121–133 (2003)MathSciNetzbMATHGoogle Scholar
  13. 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
  14. 14.
    Souffriau, W., Vansteenwegen, P., Berghe, G.V., Oudheusden, D.V.: The planning of cycle trips in the province of east flanders. Omega 39(2), 209–213 (2011)CrossRefGoogle Scholar
  15. 15.
    Vansteenwegen, P., Souffriau, W., Oudheusden, D.V.: The orienteering problem: a survey. Eur. J. Oper. Res. 209(1), 1–10 (2011)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Verbeeck, C., Vansteenwegen, P., Aghezzaf, E.H.: An extension of the arc orienteering problem and its application to cycle trip planning. Transp. Res. Part E: Logist. Transp. Rev. 68, 64–78 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ramneek Kaur
    • 1
    Email author
  • Vikram Goyal
    • 1
  • Venkata M. V. Gunturi
    • 2
  1. 1.IIIT-DelhiNew DelhiIndia
  2. 2.IIT RoparRupnagarIndia

Personalised recommendations