Skip to main content
SpringerLink
Log in

Prediction of Bus Travel Time over Intervals between Pairs of Adjacent Bus Stops Using City Bus Probe Data

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Prediction of bus travel time is a crucial tool for passengers. We present methods to predict bus travel time over intervals between pairs of adjacent bus stops using city bus probe data. We apply Gradient Boosting Decision Trees to several kinds of features extracted from the probe data. Experimental results illustrate that adding a combination of features improves the accuracy of travel time prediction over the target interval. In particular, the method using a combination of the travel time over the interval previous to the target one and the number of stops the bus makes before reaching the target interval has better performance than the other methods which use all the other combinations of four features used in this study.

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
Fig. 7

Similar content being viewed by others

Notes

  1. https://www.furuno.com/en/its/articles/20171026/

References

  1. Zhou, P., Zheng, Y., Li, M.: How long to wait? Predicting bus arrival time with Mobile phone based participatory sensing. IEEE Trans. Moblile Comput. 13(6), 1228–1241 (2014)

    Article  Google Scholar 

  2. Zhang, R., Liu, W., Jia, Y., Jiang, G., Xing, J., Jiang, H., Liu, J.: WiFi sensing-based real-time bus tracking and arrival time prediction in urban environments. IEEE Sensors J. 18(11), 4746–4760 (2018)

    Article  Google Scholar 

  3. Uchimura, K., Narimatsu, Y., Eto, A., Hu, Z.: The time required prediction between bus stops using the bus location service. IATSS Rev. 32(3), 224–231 (2007)

    Google Scholar 

  4. Sinn, M., Yoon, J.W., Calabrese, F., Bouillet, E.: Predicting arrival times of buses using real-time GPS measurements, 2012 IEEE 15th International Conference on Intelligent Transportation Systems (ITSC), pp.1227–1232, (2012)

  5. Amita, J., Jain, S., Garg, P.: Prediction of bus travel time using ann: a case study in Delhi. Transp. Res. Proced. 17, 263–272 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Pang, J., Huang, J., Du, Y., Yu, H., Huang, Q., Yin, B.: Learning to predict bus arrival time from heterogeneous measurements via recurrent neural network. IEEE Trans. Intell. Transp. Syst. 20(9), 3283–3293 (2019)

    Article  Google Scholar 

  9. Imai, H., Hiroi, K., Kawaguchi, N.: Arrival prediction model and precision analysis based on bus traffic data. J. Inf. Process. Soc. Japan. 60(1), 101–117 (2019) (in Japanese)

    Google Scholar 

  10. Chen, C.: An arrival time prediction method for bus system. IEEE Internet Things J. 5(5), 4231–4232 (2018)

    Article  Google Scholar 

  11. Maiti, S., Pal, A., Pal, A., Chattopadhyay, T., Mukherjee, A.: Historical data based real time prediction of vehicle arrival time, IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp.1837–1842, (2014)

  12. Yu, B., Lam, W., Tam, M.: Bus arrival time prediction at bus stop with multiple routes. Transp. Res. Part C Emerg. Technol. 19(6), 1157–1170 (2011)

    Article  Google Scholar 

  13. Noor, R.M., Yik, N.S., Kolandaisamy, R., Ahmedy, I., Hossain, M.A., Yau, K.A., Shah, W.M., Nandy, T.: Predict Arrival Time By Using Machine Learning Algorithm To Promote Utilization of Urban Smart Bus, Preprints, DOI:https://doi.org/10.20944/preprints202002. 0197.v1, (2020)

  14. Bai, C., Peng, Z., Lu, Q., Sun, J.: Dynamic bus travel time prediction models on road with multiple bus routes. Comput. Intell. Neurosci. 2015, 432389 (2015)

    Article  Google Scholar 

  15. Chen, M., Liu, X., Xia, J.: Dynamic prediction method with schedule recovery impact for bus arrival time. Transp. Res. Rec. 1923(1), 208–217 (2005)

    Article  Google Scholar 

  16. Gurmu, Z.K., Nall, T., Perkins, I.: Artificial neural network travel time prediction model for buses using only GPS data. J. Public Transp. 17(2), 45–65 (2014)

    Article  Google Scholar 

  17. Huang, Z., Li, Q., Li, F., Xia, J.: A novel bus-dispatching model based on passenger flow and arrival time prediction. IEEE Access. 7, 106453–106465 (2019)

    Article  Google Scholar 

  18. Berrebi, S.J., Hans, E., Chiabaut, N., Laval, J.A., Leclercq, L., Watkins, K.E.: Comparing bus holding methods with and without real-time predictions. Transp. Res. C. 87, 197–211 (2018)

    Article  Google Scholar 

  19. Yu, H., Wu, Z., Chen, D., Ma, X.: Probabilistic prediction of bus headway using relevance vector machine regression. IEEE Trans. Intell. Transp. Syst. 18(7), 1772–1781 (2017)

    Article  Google Scholar 

  20. Kim, S., Kim, H.: A new metric of absolute percentage error for intermittent demand forecasts. Int. J. Forecast. 32(3), 669–679 (2016)

    Article  Google Scholar 

  21. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  22. Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)

    Article  MathSciNet  Google Scholar 

  23. Chen, T., Guestrin, C., Xgboost: A scalable tree boosting system, arXiv. [Online]. Available: https://arxiv.org/abs/1603.02754, (2016)

  24. Yamaguchi, T., As, M., Mine, T., Prediction of bus delay over intervals on a various kinds of routes using bus probe data, in The 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2018), 97–106, (2018)

  25. M. As and T. Mine, “An adaptive approach for predicting bus travel time over unstable intervals,” in ITS AP Forum 2018 Fukuoka, 2018, pp. 1115–1128

  26. As, M., Mine, T., Dynamic bus travel time prediction using machine learning technique, in ACM IMCOM2018. [Online]. Available: https://dl.acm.org/citation.cfm?id=3164541.3164630, (2018)

  27. As, M., Mine, T., Nakamura, H., Estimation of travel time variability using bus probe data, in The 6th IEEE International Conference on Advanced Logistics and Transport (IEEE ICALT 2017), 68–74, (2017)

  28. As, M., Mine, T., Prediction of Travel Time over Unstable Intervals Between Adjacent Bus Stops Using Historical Travel Time in Both the Previous and Current Time Periods. Singapore: Springer Singapore, 131–153. [Online]. Available: https://doi.org/10.1007/978-981-13-7434-0_8, (2019)

  29. Yamaguchi, T., Maita, R., Kawatani, T., Mine, T. Prediction of Travel Time over Intervals between Two Bus Stops Using Bus Probe Data, Proceedings of 17th ITS Symposium 2019, (2019)

  30. Huo, Y., Li, W., Zhao, J., Zhu, S.: Modelling bus delay at bus stop. Transport. 33(1), 12–21 (2018)

    Article  Google Scholar 

  31. “How to choose an appropriate error indicator”, https://analysis-navi.com/?p=2875 (in Japanese) (accessed at 2020.05.28)

  32. Wilcoxon, F., Katti, S.K., Wilcox, R.A.: Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test, Selected tables in mathematical statistics, 1, pp. 171–259, (1970)

Download references

Acknowledgments

The probe data used in this study were provided by Showa Bus Co. Ltd., Saga, Japan under the Collaboration Research. This work is partly supported by JSPS KAKENHI Grant Numbers JP19KK0257 and JP20H01728.

Author information

Authors and Affiliations

  1. Department Advanced Information Technology, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Takuya Kawatani, Tsubasa Yamaguchi, Yuta Sato & Ryotaro Maita

  2. Department Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Tsunenori Mine

Authors
  1. Takuya Kawatani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tsubasa Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuta Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ryotaro Maita
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tsunenori Mine
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takuya Kawatani.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kawatani, T., Yamaguchi, T., Sato, Y. et al. Prediction of Bus Travel Time over Intervals between Pairs of Adjacent Bus Stops Using City Bus Probe Data. Int. J. ITS Res. 19, 456–467 (2021). https://doi.org/10.1007/s13177-021-00251-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-021-00251-8

Keywords

Search

Navigation