Prediction of Bus Motion and Continuous Query Processing for Traveler Information Services

  • Bratislav Predic
  • Dragan Stojanovic
  • Slobodanka Djordjevic-Kajan
  • Aleksandar Milosavljevic
  • Dejan Rancic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4690)


The paper presents the methods for prediction of bus arrival times and continuous query processing as foundations of traveler information services. The time series of data from automatic vehicle location (AVL) system, consisting of time, location and speed data, is used with historical statistics and bus schedule information to predict future arrivals and motion. Based on predicted and AVL data, continuous query processing technique is proposed to extend traveler information service with notification/alarm features. Extensive experiments have shown that the proposed algorithm for bus motion prediction is efficient enough to function in real conditions and that augmented with continuous query processing techniques can produce services that useful to the travelers.


Automatic Vehicle Location (AVL) prediction of arrival/travel time continuous query processing information services intelligent transportation systems (ITS) 


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  1. 1.
    Cathey, F.W., Dailey, D.J.: A Prescription for Transit Arrival/Departure Prediction using AVL Data. Transportation Research 11(3-4), 241–264 (2003)CrossRefGoogle Scholar
  2. 2.
    Chien, S.I.J., Ding, Y., Wei, C.: Dynamic Bus Arrival Time Prediction with Artificial Neural Networks. Journal of Transportation Engineering, 429–438 (2002)Google Scholar
  3. 3.
    Chien, S.I.J., Kuchipudi, C.M.: Dynamic Travel Time Prediction with Real-Time and Historic Data. Journal on Transportation Engineering 129(6), 608–616 (2003)CrossRefGoogle Scholar
  4. 4.
    Dailey, D.J., Wall, Z.R., Maclean, S.D., Cathey, F.W.: An Algorithm and Implementation to Predict the Arrival of Transit Vehicles. In: ITSC 2000 (last accessed March 2007), available from
  5. 5.
    Dublin Bus, Automatic Vehicle Location (AVL) and Control (AVLC) and Real Time Passenger Information System (last accessed March 2007),
  6. 6.
    iBus - Passenger information System, London Buses (last accessed March 2007),
  7. 7.
    Jeong, R.H.: The Prediction of Bus Arrival time Using Automatic Vehicle LocationSystems Data. A Ph.D. Dissertation at Texas A&M University (2004)Google Scholar
  8. 8.
    Maclean, S.D., Dailey, D.J.: Real-time bus information on mobile devices. In: Proceeding of the Intelligent Transportation Systems, pp. 988–993 (2001)Google Scholar
  9. 9.
    Maclean, S., Dailey, D.: MyBus: helping bus riders make informed decisions. IEEE Intelligent Systems 16(1), 84–87 (2001)CrossRefGoogle Scholar
  10. 10.
    NextBus (last accessed March 2007),
  11. 11.
    Park, T., Lee, S., Moon, Y.-J.: Real Time Estimation of Bus Arrival Time under Mobile Environment. In: Laganà, A., Gavrilova, M., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3043, pp. 1088–1096. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Philipp, M.: Arrival Time Prediction in BTAPS. BTAPS project technical guide (last accessed March 2007),
  13. 13.
    Raman, M., Schweiger, C., Shammout, K., Williams, D.: Guidance for Developing and Deploying Real-time Traveler Information Systems for Transit. Federal Transit Administration, U.S. Department of Transportation, National Technical Information Service/NTIS, Springfield, Virginia (2003)Google Scholar
  14. 14.
    Ramjattan, A.N., Cross, P.A.: A Kalman Filter Model for an Integrated Land Vehicle Navigation System. Journal of Navigation 48(2), 293–302 (1995)CrossRefGoogle Scholar
  15. 15.
    Repenning, A., Ioannidou, A.: Mobility agents: guiding and tracking public transportation users. In: Proceedings of the Working Conference on Advanced Visual interfaces, pp. 127–134. ACM Press, New York, NY (2006)CrossRefGoogle Scholar
  16. 16.
    Shalaby, A., Farhan, A.: Prediction Model of Bus Arrival and Departure Times Using AVL and APC Data. Journal of Public Transportation 7(1), 41–61 (2004)Google Scholar
  17. 17.
    Stojanovic, D., Djordjevic-Kajan, S., Papadopoulos, A.N., Nanopoulos, A.: Continuous Range Query Processing for Network Constrained Mobile Objects. In: International Conference on Enterprise Information Systems (ICEIS), pp. 63–70 (2006)Google Scholar
  18. 18.
    Tiesyte, D., Jensen, C.: Challenges in the Tracking and Prediction of Scheduled-Vehicle Journeys. In: Proc. Of the 5th IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 407–412. IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  19. 19.
    TransDB: GPS Data Management with Applications in Collective Transport (last accessed March 2007),
  20. 20.
    Wall, Z., Dailey, D.J.: An Algorithm for Predicting the Arrival Time of Mass Transit Vehicles Using Automatic Vehicle Location Data. In: Transportation Research Board 78th Annual Meeting Washington (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Bratislav Predic
    • 1
  • Dragan Stojanovic
    • 1
  • Slobodanka Djordjevic-Kajan
    • 1
  • Aleksandar Milosavljevic
    • 1
  • Dejan Rancic
    • 1
  1. 1.Faculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 NisSerbia

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