Skip to main content
SpringerLink
Log in

Bus arrival time prediction with real-time and historic data

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. GPS-equipped buses can be regarded as mobile sensors probing traffic flows on road surfaces. In this paper, we present an approach that predicts bus arrival time in terms of the knowledge learned from a large number of historical bus GPS trajectories. In our approach, we build time-dependent path-section graph, where a path-section is a road segment between two adjacent bus stops, to model the properties of dynamic road networks. Then, a clustering approach is designed to estimate the distribution of travel time on each path-section in different time slots. Finally, bus arrival time is predicted based on the path-section graph and real-time GPS information. Using a real-world trajectory dataset generated by 1000 buses in a period of 2 months, a bus arrival time prediction system is built. Then we evaluate the system with extensive experiments and realistic evaluations. Experiments show that our method is close to the actual value and better than some typical algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Chien, S.I.J., Ding, Y., Wei, C.: Dynamic bus arrival time prediction with artificial neural networks. J. Transp. Eng. 128, 429–438 (2002)

    Article  Google Scholar 

  2. Cathey, F.W., Dailey, D.J.: A prescription for transit arrival/departure prediction using automatic vehicle location data. J Transp. Res. Part C 11(3), 241–264 (2003)

    Article  Google Scholar 

  3. Hinsbergen, C.P.I.J., Van, Lint J.W.C., Van, Zuylen H.J.: Bayesian committee of neural networks to predict travel times with confidence intervals. J Transp. Res. Part C 17(5), 498–509 (2009)

    Article  Google Scholar 

  4. Liu, J., Yu, X., Xu, Z., et al.: A cloud-based taxi trace mining framework for smart city. Software (2016). doi:10.1002/spe.2435

  5. Kumar, V., Kumar, B.A., Vanajakshi, L., et al.: Comparison of model based and machine learning approaches for bus arrival time prediction. In: Transportation Research Board 93rd Annual Meet-ing. 2014, 14–2518 (2014)

  6. Steven, I., Chien, J., Kuchipudi, C.M.: Dynamic travel time prediction with real-time and historic data. J. Transp. Eng. 129(6), 608–616 (2003)

    Article  Google Scholar 

  7. Shalaby, A., Farhan, A.: Prediction model of bus arrival and departure times using AVL and APC data. J. Public Transp. 7(1), 3 (2004)

    Article  Google Scholar 

  8. Chen, M., Liu, X., Xia, J., et al.: A dynamic bus-arrival time prediction model based on APC data. J. Comput. Aided Civ. Infrastruct Eng. 19(5), 364–376 (2004)

    Article  Google Scholar 

  9. Bin, Y., Zhongzhen, Y., Baozhen, Y.: Bus arrival time prediction using support vector machines. J. Intell. Transp. Syst. 10(4), 151–158 (2006)

    Article  MATH  Google Scholar 

  10. Yu, B., Yang, Z.Z., Wang, J.: Bus travel-time prediction based on bus speed. J. Proc. ICE-Transp. 163(1), 3–7 (2010)

    Google Scholar 

  11. Yu, B., Lam, W.H.K., Tam, M.L.: Bus arrival time prediction at bus stop with multiple routes. J. Transp. Res. Part C 19(6), 1157–1170 (2011)

    Article  Google Scholar 

  12. Yang, M., Chen, C., Wang, L., Yan, X., Zhou, J.: Bus arrival time prediction using support vector machine with genetic algorithm. J. Neural Netw. World 26(3), 205–217 (2016)

    Article  Google Scholar 

  13. Jeong, R., Rilett, L.: Prediction model of bus arrival time for real-time applications. J Transp. Res. Rec. 1927, 195–204 (2005)

    Article  Google Scholar 

  14. Sapankevych, N., Sankar, R.: Time series prediction using support vector machines: a survey. J. Comput. Intell. Mag. IEEE 4(2), 24–38 (2009)

    Article  Google Scholar 

  15. Zhu, T., Ma, F., Ma, T., et al.: The prediction of bus arrival time using global positioning system data and dynamic traffic information. In: Wireless and Mobile Networking Conference (WMNC), 2011 4th Joint IFIP. IEEE, pp. 1–5 (2011)

  16. Zhou, Y., Yao, L., Chen, Y., et al.: Bus arrival time calculation model based on smart card data. J. Transp. Res. Part C 74(8), 1634–1638 (2016)

    Google Scholar 

  17. Min, W., Wynter, L.: Vehicle arrival prediction using multiple data sources including passenger bus arrival prediction. J. U.S. Patent 9,177,473 (2015)

  18. Sayarshad, H.R., Chow, J.Y.J.: Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data. J. Adv. Transp. 50(7), 1275–1294 (2016)

    Article  Google Scholar 

  19. O’Sullivan, A., Pereira, F.C., Zhao, J., et al.: Uncertainty in bus arrival time predictions: treating heteroscedasticity with a metamodel approach. J IEEE Trans. Intell. Transp. Syst. 17(11), 3286–3296 (2016)

    Article  Google Scholar 

  20. Cats, O., Loutos, G.: Real-time bus arrival information system: an empirical evaluation. J. Intell. Transp. Syst. 2015, 1–14 (2015)

    Google Scholar 

  21. Cats, O.: An agent-based approach for modeling real-time travel information in transit systems. J. Proc. Comput. Sci. 32, 744–749 (2014)

    Article  Google Scholar 

  22. Cats, O., Loutos, G.: Evaluating the added-value of online bus arrival prediction schemes. J. Transp. Res. Part A 86, 35–55 (2016)

    Google Scholar 

  23. Cats, O.: Regularity-driven bus operation: principles, implementation and business models. J. Transp. Policy 36, 223–230 (2014)

    Article  Google Scholar 

  24. Hernandez, T.: Flex scheduling for bus arrival time prediction. J Transp. Res. Rec. 2418, 110–115 (2014)

    Article  Google Scholar 

  25. Balasubramanian, P., Rao, K.R.: An adaptive long-term bus arrival time prediction model with cyclic variations. J. Public Transp. 18(1), 6 (2015)

    Article  Google Scholar 

  26. Moreira-Matias, L., Mendes-Moreira, J., Sousa, J.F.D., et al.: Improving mass transit operations by using AVL-based systems: a survey. J. IEEE Trans. Intell. Transp. Syst. 16(4), 1636–1653 (2015)

    Article  Google Scholar 

  27. Silva, V.B.C.D., Sciammarella, T., Campista, M.E.M., et al.: Time of arrival prediction with dynamic route tracking for public transportation systems. In: Ad Hoc NETWORKING Workshop. IEEE, pp. 1–8 (2015)

Download references

Acknowledgements

This work was financially supported by Chinese National Natural Science Foundation (61572165) and Public Projects of Zhejiang Province (2015C33067).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Haitao Xu & Jing Ying

Authors
  1. Haitao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haitao Xu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, H., Ying, J. Bus arrival time prediction with real-time and historic data. Cluster Comput 20, 3099–3106 (2017). https://doi.org/10.1007/s10586-017-1006-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1006-1

Keywords

Search

Navigation