Proportion-Based and Tendency-Based Bus Trajectory Prediction Models
Publication: Journal of Transportation Engineering
Volume 139, Issue 9
Abstract
At the terminal station, the path headway bias of a trip is linearly correlated to the travel time of the path when it is beyond a critical range, which marks the tendency between successive trips. A tendency-based model for trajectory prediction was proposed in which the path headway bias was assumed to be proportionally amplified at downstream stops with an increase in path travel time, when buses fall into the amplifying tendency, or remain the same, when buses fall into the maintaining tendency. The boundary of the tendency was calibrated by the relative performance between the proportion-based model and the history-based model. The calibrated tendency-based model succeeds in avoiding the amplification of nondirectional path headway bias in the proportion-based model by keeping buses in the maintaining tendency and promoting the amplification of directional path headway bias by allowing buses to fall into the amplifying tendency.
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Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 1600219195). The authors wish to acknowledge Jiangyin Public Transportation Group Co., Ltd. for providing AVL data during the project.
References
Abkowitz, M., and Engelstein, I. (1982). “Temporal and spatial dimensions of running time in transit system.”, Transportation Research Board, Washington, DC, 64–67.
Alfa, A., Menzies, W., Purcha, J., and Mcpherson, R. (1988). “A regression model for bus running times in suburban areas of Winnipeg.” J. Adv. Transp., 21(3), 227–237.
Cathey, F. W., and Dailey, D. J. (2003). “A prescription for transit arrival/departure prediction using automatic vehicle location data.” Transp. Res. Part C, 11(3), 241–264.
Ceder, A. (2007). “Public transit planning and operation: Theory, modeling and practice.” Elsevier, Butterworth-Heinemann, Oxford, UK.
Chen, G., Yang, X., An, J., and Zhang, D. (2012). “Bus-arrival-time prediction models: Link-based and section-based.” J. Transp. Eng., 138(1), 60–66.
Chen, M., Liu, X., and Xia, J. (2005). “Dynamic prediction method with schedule recovery effect for bus arrival time.”, Transportation Research Board, Washington, DC, 208–217.
Chien, S., and Kuchipudi, C. M. (2003). “Dynamic travel time prediction with real-time and historical data.” J. Transp. Eng., 129(6), 608–616.
Jeong, R., and Rilent, L. R. (2005). “Bus arrival time prediction model for real-time applications.”, Transportation Research Board, Washington, DC, 195–204.
Lin, W., and Bertini, R. (2004). “Modeling schedule recovery processes in transit operations for bus arrival time prediction.” J. Adv. Transp., 38(3), 347–365.
Lin, W., and Zeng, J. (1999). “Experimental study on real-time bus arrival time prediction with GPS data.”, Transportation Research Board, Washington, DC, 101–109.
Padmanabanl, R. P. S., Divakarl, K., Vanajakshil, L., and Subramanian, S. C. (2010). “Development of a real-time bus arrival prediction system for Indian traffic conditions.” IET Intell. Transp. Syst., 4(3), 189–200.
Tétreault, P., and El-Geneidy, A. (2010). “Estimating bus run times for new limited-stop service using archived AVL and APC data.” Transp. Res. Part A, 44(6), 390–402.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 139 • Issue 9 • September 2013
Pages: 896 - 902
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Mar 1, 2012
Accepted: Nov 6, 2012
Published online: Nov 8, 2012
Discussion open until: Apr 8, 2013
Published in print: Sep 1, 2013
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