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     Research Journal of Applied Sciences, Engineering and Technology


Artificial Neural Network Adaptive Path Time Prediction on a Road Network

1Shereen A. Taie and 2Wafaa A. Ghonaim
1Faculty of Computers and Information, Fayoum University, Fayoum, Egypt
2Faculty of Science, Al-Azhar University, Cairo, Egypt
Research Journal of Applied Sciences, Engineering and Technology  2016  9:722-729
http://dx.doi.org/10.19026/rjaset.13.3346  |  © The Author(s) 2016
Received: June ‎28, ‎2016  |  Accepted: September 23, 2016  |  Published: November 05, 2016

Abstract

This study proposes a time prediction approach to predict trip time from Cairo airport to the tourist sites in Cairo at different conditions. The proposed approach automatically predict the trip time based on different factors such as whether, roads network type, speed and connected edge length. The proposed time prediction approach is adopted using Artificial Neural Network (ANN) techniques with Radial Basis Function Neural Networks (RBF) model and Multilayer Preceptor (MLP) model. Experimental results show that ANN time prediction approach based on two models gives promising prediction time with a powerful alternative for bus arrival time prediction. MLP model has the better-predicting performance with more accurate results than based on RBF.

Keywords:

Artificial neural network, multilayer preceptor, path time prediction, radial basis function, road networks,


References

  1. Bin, Y., Y. Zhongzhen and Y. Baozhen, 2006. Bus arrival time prediction using support vector machines. J. Intell. Transport. S., 10(4): 151-158.
    Direct Link
  2. Cathey, F.W. and D.J. Dailey, 2003. A prescription for transit arrival/departure prediction using automatic vehicle location data. Transport. Res. C-Emer., 11(3-4): 241-264.
    Direct Link
  3. Chien, S.I.J., Y. Ding and C. Wei, 2002. Dynamic bus arrival time prediction with artificial neural networks. J. Transp. Eng-ASCE, 128(5): 429-438.
    Direct Link
  4. Feng, L., L. Dongmei and C. Weihong, 1999. Time shortest path algorithm for restricted searching area in transportation networks [J]. J. Image Graph., 10: 10.
  5. Goodwin, A., L. Thomas, B. Kirley, W. Hall, N. O'Brien and K. Hill, 2015. Countermeasures that work: A highway safety countermeasure guide for State highway safety offices. 8th Edn., Report No. DOT HS 812 202. National Highway Traffic Safety Administration, Washington, DC.
    Direct Link
  6. Halpern, J., 1977. Shortest route with time dependent length of edges and limited delay possibilities in nodes. Z. Oper. Res., 21(3): 117-124.
    Direct Link
  7. Kim, D.Y., X.Y. Lehto and A.M. Morrison, 2007. Gender differences in online travel information search: Implications for marketing communications on the internet. Tourism Manage., 28(2): 423-433.
    Direct Link
  8. Lin, W.H., J. Zeng and L. Zeng, 1999. Experimental study of real-time bus arrival time prediction with GPS data. Transp. Res. Record J. Transp. Res. Board, 1666(1): 101-109.
    Direct Link
  9. Mehar, A., S. Chandra and S. Velmurugan, 2013. Speed and acceleration characteristics of different types of vehicles on multi-lane highways. Eur. Transp., 55(1): 1-12.
    Direct Link
  10. Nakat, Z. and S. Herrera, 2010. Traffic congestion study: Phase 1. Final Report, ECORYS Nederland BV and SETS Lebanon for the World Bank and the Government of Egypt.
  11. Niu, X., Y. Zhu, Q. Cao, X. Zhang, W. Xie and K. Zheng, 2015. An online-traffic-prediction based route finding mechanism for smart city. Int. J. Distrib. Sens. N., 11(8): 16.
    Direct Link
  12. Padmanaban, R.P.S., K. Divakar, L. Vanajakshi and S.C. Subramanian, 2010. Development of a real-time bus arrival prediction system for Indian traffic conditions. IET Intell. Transp. Sy., 4(3): 189-200.
    Direct Link
  13. Pan, B., U. Demiryurek and C. Shahabi, 2012. Utilizing real-world transportation data for accurate traffic prediction. Proceeding of the IEEE 12th International Conference on Data Mining (ICDM'12). IEEE Computer Society, Washington, DC, USA, pp: 595-604.
    Direct Link
  14. Pan, B., U. Demiryurek, C. Shahabi and C. Gupta, 2013. Forecasting spatiotemporal impact of traffic incidents on road networks. Proceeding of the IEEE 13th International Conference on Data Mining (ICDM, 2013), pp: 587-596.
    Direct Link
  15. Suwardo, S., M. Napiah, I. Kamaruddin, and W. Oyas, 2009. Bus travel time prediction in the mixed traffic by using statistica neural network. Proceeding of the Workshop dan Simposium XII, Universities Kristen Petra Surabaya.
    Direct Link
  16. Shalaby, A. and A. Farhan, 2003. Bus travel time prediction model for dynamic operations control and passenger information systems. Proceeding of the TRB 82nd Annual Meeting, Washington D.C.
    Direct Link
  17. Sinn, M., J.W. Yoon, F. Calabrese and E. Bouillet, 2012. Predicting arrival times of buses using real-time GPS measurements. Proceeding of the 15th IEEE International Conference on Intelligent Transportation Systems, pp: 1227-1232.
    Direct Link
  18. Sun, S., C. Zhang and G. Yu, 2006. A bayesian network approach to traffic flow forecasting. IEEE T. Intell. Transp., 7(1): 124-132.
    Direct Link
  19. Tiesyte, D. and C.S. Jensen, 2008. Similarity-based prediction of travel times for vehicles traveling on known routes. Proceeding of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Article No. 14.
  20. Torres-Sospedra, J., M. Fernandez-Redondo and C. Hernandez-Espinosa, 2005. A research on combination methods for ensembles of multilayer feedforward. Proceeding of the IEEE International Joint Conference on Neural Networks, 2: 1125-1130.
    Direct Link
  21. Wang, F.Y., F.Q. Pan, L.X. Zhang and X. Zou, 2005. Optimal path algorithm of road network with traffic restriction. J. Traffic Transp. Eng., 5: 92-95.
  22. Werthner, H. and F. Ricci, 2004. E-commerce and tourism. Commun. ACM, 47(12): 101-105.
    Direct Link
  23. World Tourism Organization (WTO), 2005. Making Tourism More Sustainable-a Guide for Policy Makers. WTO, Madrid.
  24. Yamamoto, Y. and P.N. Nikiforuk, 2000. A new supervised learning algorithm for multilayered and interconnected neural networks. IEEE T. Neural Networ., 11(1): 36-46.
    Direct Link
  25. Yang, B., C. Guo and C.S. Jensen, 2013. Travel cost inference from sparse, spatio-temporally correlated time series using Markov models. Proc. VLDB Endow., 6(9): 769-780.
    Direct Link

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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