A Novel Method to Predict Traffic Features Based on Rolling Self-Structured Traffic Patterns

REFERENCES

• Brucker, P. (1978). On the complexity of clustering problems. In M. Beckmann & H. P. Kunzi (Eds.), Optimization and operation research (pp. 45–54). Heidelberg, Germany: Springer-Verlag.
   Google Scholar
• Campbell, J. L., Carney, C., & Kantowitz, B. H. (2003). Human factors design guidelines for advanced traveler information systems (ATIS) and commercial vehicle operation (CVO). Washington, DC: FHWA-RD-98-057-2, Federal Highway Administration, U.S. Department of Transportation.
   Google Scholar
• Chang, F. J., Chang, L. C., & Huang, H. L. (2002). Real time recurrent neural network for streamflow forecasting. Hydrological Processes, 16, 2577–2588.
   Web of Science ®Google Scholar
• Chang, J. L., & Miaou, S. P. (1999). Real-time prediction of traffic flows using dynamic generalized linear models. Transportation Research Record, 1678, 168–178.
   Google Scholar
• Chen, Y., Zhang, Y., & Hu, J. (2008). Multi-dimensional traffic flow time series analysis with self-organizing maps. Tsinghua Science and Technology, 13, 220–228.
   Google Scholar
• Chen, H., & Grant-Muller, S. (2001). Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C, 9, 319–336.
   Web of Science ®Google Scholar
• Chen, H., Grant-Muller, S., Mussone, L., & Montgomery, F. (2001). A study of hybrid neural network approaches and the effects of missing data on traffic forecasting. Neural Computing and Applications, 10, 277–286.
   Web of Science ®Google Scholar
• Chiou, Y. C., & Lan, L. W. (2001). Genetic clustering algorithms. European Journal of Operational Research, 135, 413–427.
   Web of Science ®Google Scholar
• Chiou, Y. C., Chiou, Y. C., & Ai, C. M. (2007). Rolling grey forecasting models for short-term traffics. Journal of the Eastern Asia Society for Transportation Studies, 7, 2486–2501.
   Google Scholar
• Chiou, Y. C., & Chou, S. T. (2010). Ant clustering algorithms. International Journal of Applied Evolutionary Computation, 1, 1–15.
   Google Scholar
• Chiou, Y. C., Lan, L. W., & Tseng, C. M. (2010). Estimation of dynamic freeway origin-destination matrices with cell-based arrival distribution modeling. Journal of the Eastern Asia Society for Transportation Studies, 8, 336–349.
   Google Scholar
• Dia, H. (2001). An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research, 131, 253–261.
   Web of Science ®Google Scholar
• Dimitriou, L., Tsekeris, T., & Stathopoulos, A. (2008). Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transportation Research Part C, 16, 554–573.
   Web of Science ®Google Scholar
• Dochy, T., Danech-Pajouh, M., and Lechevallier, Y. (1996). Short-term road traffic forecasting using neural network. RTS English Issue, 11.
   Google Scholar
• Dougherty, M., & Cobbett, M. (1997). Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting, 13, 21–31.
   Web of Science ®Google Scholar
• Ghosh, B., Basu, B., & O’Mahony, M. M. (2005, January). Time-series modelling for forecasting vehicular traffic flow in Dublin. Proceedings of TRB Annual Meeting, Washington, DC, 1–22.
   Google Scholar
• Ghosh, B., Basu, B., & O’Mahony, M. M. (2009). Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Transactions on Intelligent Transportation Systems, 10, 246–254.
   Web of Science ®Google Scholar
• Hong, W. C. (2011). Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing, 74, 2096–2107.
   Web of Science ®Google Scholar
• Huang, S., & Sadek, A. W. (2009). A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting. Transportation Research Part C, 17, 510–525.
   Web of Science ®Google Scholar
• Kamarianakis, Y., & Prastakos, P., (2003). Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches. Transportation Research Record, 1857, 74–84.
   Google Scholar
• Kirby, H., Dougherty, M., & Watson, S. (1997). Should we use neural networks or statistical models for short term motorway traffic forecasting? International Journal of Forecasting, 13, 43–50.
   Web of Science ®Google Scholar
• Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 50–69.
   Web of Science ®Google Scholar
• Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection (pp. 619–632). Cambridge, MA: MIT Press.
   Google Scholar
• Kumar, P., Singh, V., & Reddy, D. (2005). Advanced traveler information system for Hyderabad City. IEEE Transactions on Intelligent Transportation Systems, 6, 26–37.
   Web of Science ®Google Scholar
• Lam, W. H. K., Chan, K. S., Tam, M. L., & Shi, J. W. (2005). Short-term travel time forecasts for transport information system in Hong Kong. Journal of Advanced Transportation, 39, 289–306.
   Web of Science ®Google Scholar
• Lan, L. W., & Huang, Y. C. (2006). A rolling-trained fuzzy neural network approach for freeway incident detections. Transportmetrica, 2, 11–29.
   Web of Science ®Google Scholar
• Lan, L. W., Sheu, J. B., & Huang, Y. S. (2007). Prediction of short-interval traffic dynamics in multidimensional spaces. Journal of the Eastern Asia Society for Transportation Studies, 7, 2353–2367.
   Google Scholar
• Lan, L. W., Sheu, J. B., & Huang, Y. S. (2008). Investigation of temporal freeway traffic patterns in reconstructed state spaces. Transportation Research Part C, 16, 116–136.
   Web of Science ®Google Scholar
• Lan, L. W., Lin, F. Y., & Kuo, A. Y. (2010). Three novel methods to predict traffic time series in reconstructed state spaces. International Journal of Applied Evolutionary Computation, 1, 16–35.
   Google Scholar
• Ledoux, C. (1997). An urban traffic flow model integrating neural networks. Transportation Research Part C, 5, 287–300.
   Web of Science ®Google Scholar
• Maccubbin, R. P., Staples, B. L., & Mercer, M. R. (2003). Intelligent transportation systems benefits and costs: 2003 update. Washington, DC: FHWA-OP-03-075, Federal Highway Administration, U.S. Department of Transportation.
   Google Scholar
• Okutani, I., & Stephanedes, Y. J. (1984). Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B, 18, 1–11.
   Google Scholar
• Rauber, A., Merkl, D., & Dittenbach, M. (2002). The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks, 13, 1331–1341.
   PubMed Web of Science ®Google Scholar
• Sheu, J. B., Huang, Y. S., & Lan, L. W. (2009a). A real-time recurrent learning on predicting short-term traffic dynamics for sustainable management. International Journal of Environment and Sustainable Development, 8, 330–350.
   Google Scholar
• Sheu, J. B., Lan, L. W., & Huang, Y. S. (2009b). Short-term prediction of traffic dynamics with real-time recurrent learning algorithms. Transportmetrica, 5, 59–83.
   Web of Science ®Google Scholar
• Smith, B. L., & Demetsky, M. J. (1997). Traffic flow forecasting: Comparison of modeling approaches. Journal of Transportation Engineering, 123, 261–266.
   Web of Science ®Google Scholar
• Smith, B. L., Williams, B. M., & Oswald, R. K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C, 10, 303–321.
   Web of Science ®Google Scholar
• Szeto, W. Y., Ghosh, B., Basu, B., & O’Mahony, M., (2009). Multivariate traffic forecasting technique using cell transmission model and SARIMA model. Journal of Transportation Engineering, 135, 658–667.
   Web of Science ®Google Scholar
• Tangsripairoj, S., & Samadzadeh, M. H. (2006). Organizing and visualizing software repositories using the growing hierarchical self-organizing map. Journal of Information Science and Engineering, 22, 283–295.
   Web of Science ®Google Scholar
• Van Der Voort, M., Dougherty, M., & Watson, S. (1996). Combining Kohonen maps with ARIMA time-series models to forecast traffic flow. Transportation Research Part C, 4, 307–318.
   Web of Science ®Google Scholar
• Vlahogianni, E. I. (2009). Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. Journal of Intelligent Transportation Systems, 13, 73–84.
   Web of Science ®Google Scholar
• Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2005). Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transportation Research Part C, 13, 211–234.
   Web of Science ®Google Scholar
• Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2006). Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume. Transportation Research Part C, 14, 351–367.
   Web of Science ®Google Scholar
• Vlahogianni E. I., Karlaftis, M. G., & Golias, J. C. (2008). Temporal evolution of short-term urban traffic flow: A non-linear dynamics approach. Computer-Aided Civil and Infrastructure Engineering, 23, 536–548.
   Web of Science ®Google Scholar
• Welch, W. J. (1983). A mean squared error criterion for the design of experiment. Biometrika, 70, 205–213.
   Web of Science ®Google Scholar
• Williams, B. M., & Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering, ASCE, 129, 664–672.
   Web of Science ®Google Scholar
• Yang, H. T., Chen, S. C., & Tsai, W. N. (2004). Classification of direct load control curves for performance evaluation. IEEE Transactions on Power Systems, 19, 811–829.
   Web of Science ®Google Scholar
• Yang, Y., Jiang, D., & Xia, M. (2010). Using improved GHSOM for intrusion detection. Journal of Information Assurance and Security, 5, 232–239.
   Google Scholar
• Yao, L. & Lin, C. C. (2009). Identification of nonlinear systems by the genetic programming-based Volterra filter. IET Signal Process, 3, 93–105.
   Web of Science ®Google Scholar
• You, J. & Kim, T. J. (2000). Development and evaluation of a hybrid travel time forecasting model. Transportation Research Part C, 8, 231–256.
   Web of Science ®Google Scholar
• Zhang, Y. & Ye, Z. (2008). Short-term traffic flow forecasting using fuzzy logic system methods. Journal of Intelligent Transportation Systems, 12, 102–112.
   Web of Science ®Google Scholar