Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Xuedong Hua
Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China
Wei Wang
Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China
Yinhai Wang
Dept of Civil and Environmental Engineering, University of Washington, Seattle, United States
Min Ren
Business School, Hohai University, Nanjing, China


The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.


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Balasubramanian, P.; Rao, K. R. 2015. An adaptive long-term bus arrival time prediction model with cyclic variations, Journal of Public Transportation 18(1): 1–18. https://doi.org/10.5038/2375-0901.18.1.6

Cathey, F. W.; Dailey, D. J. 2003. A prescription for transit arrival/ departure prediction using automatic vehicle location data, Transportation Research Part C: Emerging Technologies 11(3–4): 241–264. https://doi.org/10.1016/S0968-090X(03)00023-8

Chang, C.-C.; Lin, C.-J. 1989. LIBSVM: a Library for Support Vector Machines. Available from Internet: https://www.csie. ntu.edu.tw/~cjlin/libsvm

Chang, H.; Park, D.; Lee, S.; Lee, H.; Baek, S. 2010. Dynamic multi-interval bus travel time prediction using bus transit data, Transportmetrica 6(1): 19–38. https://doi.org/10.1080/18128600902929591

Chen, M.; Liu, X.; Xia, J.; Chien, S. I. 2004. A dynamic bus-arrival time prediction model based on APC data, Computer- Aided Civil and Infrastructure Engineering 19(5): 364–376. https://doi.org/10.1111/j.1467-8667.2004.00363.x

Chien, S. I.; Ding, Y.; Wei, C. 2002. Dynamic bus arrival time prediction with artificial neural networks, Journal of Transportation Engineering 128(5): 429–438. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:5(429)

Cristianini, N.; Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press. 204 p.

Dailey, D.; Maclean, S.; Cathey, F.; Wall, Z. 2001. Transit vehicle arrival prediction: algorithm and large-scale implementation, Transportation Research Record: Journal of the Transportation Research Board 1771: 46–51. https://doi.org/10.3141/1771-06

Ding, Y.; Chien, S. I. 2000. The Prediction of Transit Arrival Times Using Link-Based and Stop-Based Artificial Neural Networks. New Jersey Institute of Technology, Newark, US. 5 p.

Horning, J.; El-Geneidy, A. M.; Hourdos, J. 2009. Estimating Running Time and Demand for a Bus Rapid Transit Corridor. Report No CTS 09-24. University of Minnesota, US. 69 p. Available from Internet: http://www.its.umn.edu/Publications/ ResearchReports/reportdetail.html?id=1852

Khetarpaul, S.; Gupta, S. K.; Malhotra, S.; Subramaniam, L. V. 2015. Bus arrival time prediction using a modified amalgamation of fuzzy clustering and neural network on spatiotemporal data, Lecture Notes in Computer Science 9093: 142–154. https://doi.org/10.1007/978-3-319-19548-3_12

Lin, Y.; Yang, X.; Zou, N.; Jia, L. 2013. Real-time bus arrival time prediction: case study for Jinan, China, Journal of Transportation Engineering 139(11): 1133–1140. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000589

Maiti, S.; Pal, Arp.; Pal, Ari.; Chattopadhyay, T.; Mukherjee, A. 2014. Historical data based real time prediction of vehicle arrival time, in 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), 8–11 October 2014, Qingdao, China, 1837–1842. https://doi.org/10.1109/ITSC.2014.6957960

Park, S. H.; Jeong, Y. J.; Kim, T. J. 2007. Transit travel time forecasts for location-based queries: implementation and evaluation, Proceedings of the Eastern Asia Society for Transportation Studies 6: 1–11. https://doi.org/10.11175/eastpro.2007.0.237.0

Patnaik, J.; Chien, S.; Bladikas, A. 2004. Estimation of bus arrival times using APC data, Journal of Public Transportation 7(1): 1–20. https://doi.org/10.5038/2375-0901.7.1.1

Shalaby, A.; Farhan, A. 2004. Prediction model of bus arrival and departure times using AVL and APC data, Journal of Public Transportation 7(1): 41–61. https://doi.org/10.5038/2375-0901.7.1.3

Van Hinsbergen, C. P.; Van Lint, J. W. C.; Van Zuylen, H. J. 2009. Bayesian committee of neural networks to predict travel times with confidence intervals, Transportation Research Part C: Emerging Technologies 17(5): 498–509. https://doi.org/10.1016/j.trc.2009.04.007

Van Lint, J. W. C.; Hoogendoorn, S. P.; Van Zuylen, H. J. 2005. Accurate freeway travel time prediction with state-space neural networks under missing data, Transportation Research Part C: Emerging Technologies 13(5–6): 347–369. https://doi.org/10.1016/j.trc.2005.03.001

Xia, J.; Chen, M.; Huang, W.; 2011. A multistep corridor traveltime prediction method using presence-type vehicle detector data, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 15(2): 104–113. https://doi.org/10.1080/15472450.2011.570114

Yu, B.; Lam, W. H. K.; Tam, M. L. 2011. Bus arrival time prediction at bus stop with multiple routes, Transportation Research Part C: Emerging Technologies 19(6): 1157–1170. https://doi.org/10.1016/j.trc.2011.01.003

Yu, B.; Yang, Z.; Chen, K.; Yao, B. 2006. Bus arrival time prediction using support vector machines, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 10(4): 151–158. https://doi.org/10.1080/15472450600981009

Yu, B.; Yang, Z.-Z.; Chen, K.; Yu, B. 2010. Hybrid model for prediction of bus arrival times at next station, Journal of Advanced Transportation 44(3): 193–204. https://doi.org/10.1002/atr.136

Zaki, M.; Ashour, I.; Zorkany, M.; Hesham, B. 2013. Online bus arrival time prediction using hybrid neural network and Kalman filter techniques, International Journal of Modern Engineering Research 3(4): 2035–2041.

Zhang, Ya.; Zhang, Yu.; Haghani, A. 2014. A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model, Transportation Research Part C: Emerging Technologies 43(1): 65–78. https://doi.org/10.1016/j.trc.2013.11.011

Zheng, C.-J.; Zhang, Y.-H.; Feng X.-J. 2012. Improved iterative prediction for multiple stop arrival time using a support vector machine, Transport 27(2): 158–164. https://doi.org/10.3846/16484142.2012.692710

Zou, Y.; Zhu, X.; Zhang, Y.; Zeng, X. 2014. A space–time diurnal method for short-term freeway travel time prediction, Transportation Research Part C: Emerging Technologies 43(1): 33–49. https://doi.org/10.1016/j.trc.2013.10.007
How to Cite
Hua, X., Wang, W., Wang, Y., & Ren, M. (2017). Bus arrival time prediction using mixed multi-route arrival time data at previous stop. OJS Journal B, 33(2), 543–554. https://doi.org/10.3846/16484142.2017.1298055