Prediction of the Impact of Typhoons on Transportation Networks with Support Vector Regression
Publication: Journal of Transportation Engineering
Volume 141, Issue 4
Abstract
The ability to predict the impact of typhoons on transportation infrastructure is important as it can help to avoid serious delays and dangers when roads are closed due to such events. This research uses support vector regression (SVR) to predict the impact of typhoons on transportation infrastructure. It first integrates and examines the infrastructure and precipitation data from different authorities. An SVR model is constructed to solve a nonlinear prediction problem for small size data. The SVR model is calibrated and validated by a heuristic process. The calibrated and validated results are then applied to predict closed roads in a real network through a simulation assignment model. Several traffic management strategies are developed to reduce the negative impacts of typhoons. The results show that the mean absolute percentage error (MAPE) of SVR prediction is 9.7%. The impact of typhoons on transportation networks can thus be predicted and simulated based on the calibrated SVR model, and appropriate strategies can then be developed in order to reduce both delays and risks.
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Acknowledgments
This paper is based on work supported by Ministry of Science and Technology, Taiwan, through the project 100-2410-H-006-075. Of course, the authors are solely responsible for the contents of this paper.
References
Bajo, M., and Umgiesser, G. (2010). “Storm surge forecast through a combination of dynamic and neural network models.” Ocean Model., 33(1–2), 1–9.
Bhattacharjee, S. (2013). “Assessment of reliability and load factor design approach against fatigue cracking in flexible pavements.” Transportation Research Record 2368, Transportation Research Board, Washington, DC, 56–65.
Box, G. E. P., and Jenkins, G. M. (1994). Time series analysis: Forecasting and control, 3rd Ed., Prentice Hall, Englewood Cliffs, NJ.
Central Weather Bureau (CWB). (2013). “Maximum accumulated precipitation by district.” 〈http://www.cwb.gov.t/〉 (Sep. 15, 2013).
Chen, S., and Chen, F. (2010). “Simulation-based assessment of vehicle safety behavior under hazardous driving conditions.” J. Transp. Eng., 304–315.
Dalziell, E., and Nicholson, A. (2001). “Risk and impact of natural hazards on a road network.” J. Transp. Eng., 159–166.
Directorate General of Budget, Accounting and Statistics (DGBAS). (2013). “Statistical yearbook.” 〈http://eng.dgbas.gov.tw/mp.asp?mp=2〉 (Sep. 15, 2013).
Dutta, D., Wright, W., Nakayama, K., and Sugawara, Y. (2013). “Design of synthetic impact response functions for flood vulnerability assessment under climate change conditions: Case studies in two selected coastal zones in Australia and Japan.” Nat. Hazard. Rev., 52–65.
Edara, P., Sharma, S., and McGhee, C. (2010). “Development of a large-scale traffic simulation model for hurricane evacuation—Methodology and lessons learned.” Nat. Hazard. Rev., 127–139.
Federal Highway Administration (FHWA). (2013). “National traffic and road closure information.” 〈http://www.fhwa.dot.gov/trafficinfo/#weat〉 (Oct. 20, 2013).
Guo, Z., Dai, X., Li, X., and Ye, S. (2013). “Predict typhoon-induced storm surge deviation in a principal component back-propagation neural network model.” Chin. J. Oceanol. Limnol., 31(1), 219–226.
Hamid, S., Pinelli, J., Chen, S., and Gurley, K. (2011). “Catastrophe model-based assessment of hurricane risk and estimates of potential insured losses for the state of Florida.” Nat. Hazard. Rev., 171–176.
Hansen, J. V., and Nelson, R. D. (1997). “Neural networks and traditional time series methods: A synergistic combination in state economic forecasts.” IEEE Trans. Neural Networks, 8(4), 863–873.
Highway Disaster Information System (HDIS). (2013). “Information of road closure.” 〈http://bobe168.tw/〉 (Oct. 20, 2013).
Ho, W. M., Chen, L. W., and Hu, T. Y. (2012). “Impact assessment of network reliability with route information under severe weather.” IEEE Conf. on Intelligent Transportation Systems, IEEE, 1459–1464.
Hu, T. Y., and Mahmassani, H. S. (1997). “Day to day evolution of network flows under real-time information and responsive signal control systems.” Transp. Res. C, 5(1), 51–69.
Iida, Y. (1999). “Basic concepts and future directions of road network reliability analysis.” J. Adv. Transp., 33(2), 125–134.
Kazem, A., Sharifi, E., Hussain, F. K., Saberic, M., and Hussaind, O. K. (2013). “Support vector regression with chaos-based firefly algorithm for stock market price forecasting.” Appl. Soft Comput., 13(2), 947–958.
Lee, M. C., and To, C. (2010). “Comparison of support vector machine and back propagation neural network in evaluating the enterprise financial distress.” Int. J. Artif. Intell. Appl., 1(3), 31–43.
Lee, T. L. (2009). “Prediction of typhoon storm surge in Taiwan using artificial neural networks.” Adv. Eng. Software, 40(11), 1200–1206.
Leviäkangas, P., et al. (2011). “Extreme weather impacts on transport systems.” EWENT Project Deliverable D1, 〈http://www.vtt.fi/publications/index.jsp〉 (Oct. 20, 2013).
Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth Scientific, London.
Li, Y. F., Ng, S. H., Xie, M., and Goh, T. N. (2010). “A systematic comparison of metamodeling techniques for simulation optimization in decision support systems.” Appl. Soft Comput., 10(4), 1257–1273.
Liao, T. Y., Hu, T. Y., Chen, L. W., and Ho, W. M. (2010). “Development and empirical study of real-time simulation-based dynamic traffic assignment model.” J. Transp. Eng., 1008–1020.
Lin, N., Emanuel, K. A., Smith, J. A., and Vanmarcke, E. (2010). “Risk assessment of hurricane storm surge for New York City.” J. Geophys. Res., 115, D18121.
Ma, W., Cheu, R. L., and Lee, D. H. (2004). “Scheduling of lane closures using genetic algorithms with traffic assignments and distributed simulations.” J. Transp. Eng., 322–329.
MATLAB 8.3 [Computer software]. Natick, MA, MathWorks.
Mills, B., and Andrey, J. (2002). “Climate change and transportation: Potential interactions and impacts.” Proc., U.S. Dept. of Transportation Research Workshop: Potential Impacts of Climate Change on Transportation, Transportation Research Board, Washington, DC.
Ministry of Transportation and Communications (MOTC). (2013). “Roads in Taiwan area.” 〈http://www.motc.gov.tw/en/index.jsp〉 (Oct. 20, 2013).
Moura, M. D. C., Zio, E., Lins, I. D., and Droguett, E. (2011). “Failure and reliability prediction by support vector machines regression of time series data.” Reliab. Eng. Syst. Saf., 96(11), 1527–1534.
Naghawi, H., and Wolshon, B. (2012). “Performance of traffic networks during multimodal evacuations: Simulation-based assessment.” Nat. Hazard. Rev., 196–204.
Noh, H., Chiu, Y. C., Zheng, H., Hickman, M., and Mirchandani, P. B. (2009). “An approach to modeling demand and supply for a short-notice evacuation.” Transportation Research Record 2091, Transportation Research Board, Washington, DC, 91–99.
Parr, S., Kaisar, E., and Stevanovic, A. (2014). “Application of transit signal priority for no-notice urban evacuation.” Nat. Hazard. Rev., 167–170.
Qian, Z., and Michael Zhang, H. (2013). “Full closure or partial closure? evaluation of construction plans for the I-5 closure in downtown Sacramento.” J. Transp. Eng., 273–286.
Shin, K. S., Lee, T. S., and Kim, H. J. (2005). “An application of support vector machines in bankruptcy prediction model.” Expert Syst. Appl., 28(1), 127–135.
Siek, M., and Solomatine, D. P. (2010). “Nonlinear chaotic model for predicting storm surges.” Nonlinear Processes Geophys., 17(5), 405–420.
Smola, A. J., and Schölkopf, B. (2004). “A tutorial on support vector regression.” J. Stat. Comput., 14(3), 199–222.
SPSS 17.0 [Computer software]. Armonk, NY, IBM.
Suarez, P., Anderson, W., Mahal, V., and Lakshmanan, T. R. (2005). “Impacts of flooding and climate change on urban transportation: A system-wide performance assessment of the Boston metro area.” Transp. Res. Part D, 10(3), 231–244.
Tanasic, N., Ilic, V., and Hajdin, R. (2013). “Vulnerability assessment of bridges exposed to scour.” Transportation Research Record 2360, Transportation Research Board, Washington, DC, 23–44.
Torkian, B., Pinelli, J., Gurley, K., and Hamid, S. (2014). “Cost-and-benefit evaluation of windstorm damage mitigation techniques in Florida.” Nat. Hazard. Rev., 150–157.
Trainor, J., Murray-Tuite, P., Edara, P., Fallah-Fini, S., and Triantis, K. (2013). “Interdisciplinary approach to evacuation modeling.” Nat. Hazard. Rev., 151–162.
Tseng, C. M., Jan, C. D., Wang, J. S., and Wang, C. M. (2007). “Application of artificial neural networks in typhoon surge forecasting.” Ocean Eng., 34(11–12), 1757–1768.
Tweedie, S. W., Rowland, J. R., Walsh, S., Rhoten, R. R., and Hagle, P. L. (1986). “A methodology for estimating emergency evacuation times.” Social Sci. J., 23(2), 189–204.
Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
VISUM Version 12.0 [Computer software]. Switzerland, Transport Research & Innovation Portal (TRIP).
Watkins, E. K., and Hallenbeck, M. (2010). “Impact of weather on freeway travel times in the rain city.” Proc., 89th Annual Meeting of the Transportation Research Board, Transportation Research Board, Washington, DC.
Wu, C. H., Ho, J. M., and Lee, D. T. (2004). “Travel-time prediction with support vector regression.” IEEE Trans. Intell. Transp. Syst., 5(4), 276–281.
Yang, S. R., Shen, C. W., Huang, C. M., Lee, C. T., Cheng, C. T., and Chen, C. Y. (2012). “Prediction of mountain road closure due to rainfall-induced landslides.” J. Perform. Constr. Facil., 197–202.
Yeh, C. Y., Huang, C. W., and Lee, S. J. (2011). “A multiple-kernel support vector regression approach for stock market price forecasting.” Expert Syst. Appl., 38(3), 2177–2186.
Zhang, L., Zhou, W. D., Chang, P. C., Yang, J. W., and Li, F. Z. (2013). “Iterated time series prediction with multiple support vector regression models.” Neurocomputing, 99, 411–422.
Zhao, D., and Xue, D. (2010). “A comparative study of metamodeling methods considering sample quality merits.” Struct. Multidiscip. Optim., 42(6), 923–938.
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Published In
Journal of Transportation Engineering
Volume 141 • Issue 4 • April 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Sep 1, 2013
Accepted: Oct 16, 2014
Published online: Nov 24, 2014
Published in print: Apr 1, 2015
Discussion open until: Apr 24, 2015
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