Pattern-Based Time-Discretized Method for Bus Travel Time Prediction
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 6
Abstract
Predicting and providing information about bus arrival time to passengers accurately is a very important aspect of advanced public transportation systems (APTS), a major functional area of intelligent transportation systems. However, the information provided to passengers should be reliable. The reliability of such information provided to passengers depends on the prediction method used, which in turns depends on the input data used in the method. This means that identifying the most significant/appropriate input data and using them in the method are important. So, in the present study, travel time pattern analysis was carried out to find the most significant inputs by performing statistical tests for each day of the week separately. Also, a model-based Kalman filtering algorithm was developed to predict bus travel time by using the identified patterns effectively based on temporal discretization under heterogeneous traffic conditions. The performance of the proposed algorithm showed a clear improvement in prediction accuracy when compared with a prediction method using space discretization.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors acknowledge the support for this study as a part of the sub-project CIE/10-11/168/IITM/LELI under the Centre of Excellence in Urban Transport project funded by the Ministry of Urban Development, Government of India, through Letter No. N-11025/30/2008-UCD.
References
Bhandari, R. R. (2005). “Bus arrival time prediction using stochastic time series and Markov chains.” Ph.D. dissertation, Dept. of Civil Engineering, New Jersey Institute of Technology, Newark, NJ.
Bo, Y., Jing, L., Bin, Y., and Zhongjen, Y. (2009). “An adaptive bus arrival time prediction model.” ⟨http://www.easts.info/publications/journalproceedings/journal2010/100064.pdf⟩ (Jan. 15, 2013).
Cathey, F. W., and Dailey, D. J. (2003). “A prescription for transit arrival/departure prediction using AVL data.” Transp. Res. Part C: Emerging Technol., 11(3), 241–264.
Census of India. (2011). “Cities having population 1 lakh and above, Census 2011.” The Registrar General & Census Commissioner, New Delhi, India, ⟨http://www.censusindia.gov.in/2011-prov-results/paper2/data_files/India2/Table_2_PR_Cities_1Lakh_and_Above.pdf⟩ (Aug. 1, 2016).
Chamberlain, R. G. (2013). “Great circle distance between two points.” ⟨http://www.movabletype.co.uk/scripts/gis-faq-5.1.html.⟩ (Feb. 14, 2013).
Chennai District. (2016). “Location and area.” ⟨http://www.chennai.tn.nic.in/chndistprof.htm#loc⟩ (Aug. 4, 2016).
Chien, S. I. J., Ding, Y., and Wei, C. (2002). “Dynamic travel time prediction with artificial neural networks.” J. Transp. Eng., 429–438.
Chris, D. E. (2012). Doing business in India, Springer, New York, 218.
Crout, D. T. (2007). “Accuracy and precision of TriMet’s transit tracker system.” J. Transp. Res. Board, 93–100.
Jeong, R., and Rilett, L. R. (2004). “Bus arrival time prediction using artificial neural network model.” Proc., 7th Int. IEEE Conf. on Intelligent Transportation Systems, Washington, DC, 988–993.
Kalman, R. E. (1960). “A new approach to linear filtering and prediction problems.” ASME J. Basic Eng., 82(1), 35–45.
Kumar, B. A., Vanajakshi, L., and Subramanian, S. C. (2013). “Day-wise travel time pattern analysis under heterogeneous traffic conditions.” Procedia-Social Behav. Sci., 104, 746–754.
Kumar, S. V., and Vanajakshi, L. (2011). “Pattern identification based bus arrival time prediction.” ⟨http://www.icevirtuallibrary.com/content/article/10.1680/tran.12.00001⟩ (Jan. 14, 2013).
Kwon, J., Coifman, B., and Bickel, P. (2000). “Day-to-day travel-time trends and travel-time prediction from loop-detector data.” J. Transp. Res. Board, 1717, 120–129.
Lee, W. C., Si, W., Chen, L. J., and Chen, M. C. (2012). “HTTP: A new framework for bus travel time prediction based on historical trajectories.” Proc., 20th Int. Conf. on Advances in Geographic Information Systems, ACM Digital Library, New York, 279–288.
Lin, W. H., and Zeng, J. (1999). “Experimental study of real-time bus arrival time prediction with GPS data.” J. Transp. Res. Board, 1666(1), 13–20.
MTC (Metropolitan Transport Corporation). (2014). “The growth.” ⟨http://www.mtcbus.org/⟩ (Aug. 1, 2016).
Nanthawichit, C., Nakatsuji, T., and Suzuki, H. (2003). “Application of probe vehicle data for real time traffic state estimation and short time travel time prediction on a freeway.” J. Transp. Res. Board, 1855, 49–59.
Ohba, Y., Ueno, H., and Kuwahara, M. (1999). “Travel time calculation method for expressway using toll collection system data.” Proc., IEEE/IEEJ/JSAI Int. Conf. on Intelligent Transportation Systems (Cat. No.99TH8383), Tokyo, 471–475.
Padmanabhan, R. P. S., Divakar, K., Vanajakshi, L., and Subramanian, S. C. (2010). “Development of a real-time bus arrival prediction system for Indian traffic conditions.” IET J. Intell. Transp. Syst., 4(3), 189–200.
Patnaik, J., Chien, S., and Bladikas, A. (2004). “Estimation of bus arrival times using APC data.” J. Public Transp., 7(1), 1–20.
Ramakrishna, Y., Lakshmanan, V., and Sivanandan, R. (2006). “Bus travel time prediction using GPS data.” ⟨http://www.gisdevelopment.net/proceedings/mapindia/2006/student%20oral/mi06stu_html⟩ (Feb. 12, 2013).
Rees, D. G. (2001). Essential statistics, Chapman & Hall/CRC Publishing, Inc., London.
Schweiger, C. L. (2003). “Real-time bus arrival information systems: A synthesis of transit practice.”, Transportation Research Board of the National Academics, Washington, DC.
Shalaby, A., and Farhan, A. (2004). “Prediction models of bus arrival and departure times using AVL and APC data.” J. Public Transp., 7(1), 41–61.
Son, B., Kim, H. J., Shin, C. H., and Lee, S. K. (2004). “Bus arrival time prediction method for ITS application.” Int. Conf. on Knowledge-Based and Intelligent Information and Engineering Systems, Springer, Berlin, 88–94.
Sprinthall, R. C. (2003). Basic statistical analysis, Pearson Education Group, Inc., New York.
Vanajakshi, L., et al. (2016). “An interim report on development of a real time bus arrival time prediction system under Indian traffic conditions.” ⟨https://coeut.iitm.ac.in/APTS_Finalreport_2016.pdf⟩ (Aug. 1, 2016).
Vanajakshi, L., Subramanian, S. C., and Sivanandan, R. (2009). “Travel time prediction under heterogeneous traffic conditions using GPS data from buses.” IET J. Intell. Transp. Syst., 3(1), 1–9.
Verma, A., Sreenivasulu, S., and Dash, N. (2011). “Achieving sustainable transportation system for Indian cities—Problems and issues.” Curr. Sci., 100(9), 1328–1339.
Warman, P. (2003). “Measure impacts of real-time control and information systems for bus services.” Transport Direct, Dept. of Transport, London.
Wu, C. H., et al. (2003). “An advanced traveler information system with emerging network technologies.” Proc., 6th Asia-Pacific Conf. Intelligent Transportation Systems Forum, Taipei, Chinese-Taipei, 230–231.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 6 • June 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Apr 9, 2014
Accepted: Oct 26, 2016
Published online: Feb 14, 2017
Published in print: Jun 1, 2017
Discussion open until: Jul 14, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.