Exploring the Value of Traffic Flow Data in Bus Travel Time Prediction
Publication: Journal of Transportation Engineering
Volume 138, Issue 4
Abstract
The accurate prediction of transit travel times has a range of applications to benefit operators and passengers. Transit travel time is affected by several factors such as traffic flow and passenger demand, which have to be considered to make accurate predictions. However, previous studies have not considered real world traffic flow variables in their prediction models. This paper develops artificial neural network (ANN) models to predict bus travel time on the basis of a range of input variables including traffic flow data collected from a bus route in Melbourne, Australia. To overcome the drawback of ANNs in determining the effect of each input variable on the independent variable, the paper adopts a regression analysis to determine the important input variables for prediction. The paper examines the value that traffic flow data would make to the prediction accuracy. To this end, two alternative models are developed and the results are compared with those obtained from the traffic flow data–based models. A historical data–based ANN in which temporal variables are substituted with the traffic flow variable and a timetable-based model that traditionally utilizes scheduled travel times are developed. Although the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared with when temporal variables are used.
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Acknowledgments
Ventura Bus Company, VicRoads, and Bureau of Meteorology are appreciated for supplying the GPS, SCATS, and weather data, respectively.
References
Abdelfattah, A. M., and Khan, A. M. (1998). “Models for predicting bus delays.” Transp. Res. Rec., 1623(1), 8–15.TRREDM
Bishop, C. M. (1995). Neural networks for pattern recognition, Clarendon, Oxford, UK.
Ceder, A. (2007). Public transit planning and operation: Theory, modelling and practice, Elsevier, Oxford, UK.
Chen, M., Liu, X., and Xia, J. (2005). “Dynamic prediction method with schedule recovery impact for bus arrival time.” Transp. Res. Rec., 1923(1), 208–217.TRREDM
Chen, M., Liu, X., Xia, J., and Chien, S. I. (2004). “A dynamic bus-arrival time prediction model based on APC data.” Comp. Aided Civ. Infrastruct. Eng., 19(5), 364–376.CCIEFR
Chen, M., Yaw, J., Chien, S. I., and Liu, X. (2007). “Using automatic passenger counter data in bus arrival time prediction.” J. Adv. Transp., 41(3), 267–283.JATRDC
Chien, S. I., Ding, Y., and Wei, C. (2002). “Dynamic bus arrival time prediction with artificial neural networks.” J. Transp. Eng., 128(5), 429–438.JTPEDI
Dailey, D. J., Maclean, S. D., Cathey, F. W., and Wall, Z. R. (2001). “Transit vehicle arrival prediction-algorithm and large-scale implementation.” Transp. Res. Rec., 1771(1), 46–51.TRREDM
Dobson, A. J., and Barnett, A. G. (2008). Introduction to generalized linear models, Chapman and Hall/CRC, Boca Raton, FL.
Fu, L., Lio, Q., and Calamai, P. (2003). “Real-time optimization model for dynamic scheduling of transit operations.” Transp. Res. Rec., 1857(1), 48–55.TRREDM
Fu, L., and Yang, X. (2002). “Design and implementation of bus holding control strategies with real time information.” Transp. Res. Rec., 1791(1), 6–12.TRREDM
Hagan, M. T., Demuth, H. B., and Beale, M. (1996). Neural network design, PWS, Boston.
Hastie, T., Tibshirani, R., and Friedman, J. (2001). The elements of statistical learning, data mining, inference, and prediction, Springer-Verlag, New York.
Jeong, R., and Rilett, R. L. (2004). “Bus arrival time prediction using artificial neural network model.” IEEE Intelligent Transportation System Conf., IEEE, Washington, DC.
Kalaputapu, R., and Demetsky, M. J. (1995). “Modelling schedule deviations of buses using automatic vehicle location data and artificial neural networks.” Transp. Res. Rec., 1497, 44–52.TRREDM
Khosravi, A., Mazloumi, E., Nahavandi, S., Creighton, D., and Van Lint, H. (2011). “Constructing optimal intervals to predict day-to-day variability of bus travel times.” IEEE Trans. Intell. Ttransp. Syst.ITISFG, 12(2), 537–547.
Li, R. (2006). “Enhancing motorway travel time prediction models through explicit incorporation of travel time variability.” Ph.D. thesis, Monash Univ., Melbourne, Australia.
Lin, W. H., and Bertini, R. L. (2004). “Modelling schedule recovery processes in transit operations for bus arrival time prediction.” J. Adv. Transp., 38(3), 347–365.JATRDC
Lin, W. H., and Zeng, J. (1999). “Experimental study of real-time bus arrival time prediction with GPS data.” Transp. Res. Rec., 1666(1), 101–109.TRREDM
Liu, H. (2008). “Travel time prediction for urban networks.” Ph.D. thesis, Delft Univ. of Technology, Delft, Netherlands.
MacKay, D. J. C. (1992). “Bayesian interpolation.” Neural Comput., 4(3), 415–447.NEUCEB
Mazloumi, E. (2010). “Public transport travel time and its variability.” Ph.D. thesis, Civil Engineering Dept., Monash Univ., Melbourne, Australia.
Mazloumi, E., Currie, G., and Rose, G. (2010). “Using GPS data to gain insight into public transport travel time variability.” J. Transp. Eng., 136(7), 623–631.JTPEDI
Mazloumi, E., Rose, G., Currie, G., and Moridpour, S. (2011). “Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction.” Eng. Appl. Artif. Intell., 24(3), 534–542.EAAIE6
Mishalani, R. G., McCord, M. R., and Forman, S. (2008). “Schedule-based and autoregressive bus running time modelling in the presence of driver-bus heterogeneity.” Lecture notes in economics and mathematical systems: Computer-aided systems in public transport, Springer, Berlin, 301–317.
Park, T., Lee, S., and Moon, Y. J. (2004). “Real time estimation of bus arrival time under mobile environment.” Computational science and its applications, ICCSA, Assisi, Italy.
Patnaik, J., Chien, S. I., and Bladikas, A. (2004). “Estimation of bus arrival times using APC data.” J. Public Transp., 7(1), 1–20.
Shalaby, A., and Farhan, A. (2004). “Prediction model of bus arrival and departure times using AVL and APC data.” J. Public Transp., 7(1), 41–61.
Sharkey, A. J. C. (1996). “On combining artificial neural nets.” Connect. Sci., 8(3-4), 299.CNTSEU
Sun, D., Luo, H., Fu, L., Liu, W., Liao, X., and Zhao, M. (2007). “Predicting bus arrival time on the basis of global positioning system data.” Transp. Res. Rec., 2034, 62–72.TRREDM
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Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 4 • April 2012
Pages: 436 - 446
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Feb 24, 2010
Accepted: Jul 18, 2011
Published online: Jul 20, 2011
Published in print: Apr 1, 2012
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