Abstract
With the rapid development of urban traffic, it is very important to achieve accurate short-term traffic flow forecasting. Firstly, with the problem of short-term traffic flow forecasting, the key features that affect the traffic flow are extracted and the KNN non-parametric regression method is used for forecasting. Secondly, in order to solve the problem of dynamic traffic flow assignment, we build a simulation model and achieved good results. Finally, we use the case of short-term flow forecasting in airport to carry out a data experiment. The experimental results show that the traffic flow of traffic nodes and routes can be forecasted completely by using KNN algorithm combined with discrete event simulation technology, and the results are more credible.
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References
Chuan, L.: Short-term traffic flow forecasting algorithm based on K-nearest Neighbor Nonparametric Regression (2015)
Yakowitz, S.: Nearest-neighbor methods for time series analysis. J. Time Ser. Anal. 8(2), 10–26 (1987)
Davis, G., Nihan, N.: Nonparametric regression and short-term freeway traffic forecasting. J. Transp. Eng. 117(2), 178–188 (1991)
Smith, B.L., Demetsky, M.J.: Traffic flow forecasting: comparison of modeling approaches. J. Transp. Eng. 123(4), 261–266 (1997)
Smith, B.L., Williams, B.M.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C 10, 303–321 (2002)
Clark, S.: Traffic forecasting using multivariate nonparametric regression. J. Transp. Eng. 129(2), 161–168 (2003)
Turochy, R.E.: Enhancing short-term traffic forecasting with traffic condition information. J. Transp. Eng. 132(6), 469–474 (2006)
Kindzerske, M.D., Ni, D.H.: Composite nearest neighbor nonparametric regression to improve traffic forecasting. J. Transp. Res. Board 1993, 30–35 (2007)
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Yu, S., Li, Y., Sheng, G., Lv, J. (2019). Research on Short-Term Traffic Flow Forecasting Based on KNN and Discrete Event Simulation. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_63
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DOI: https://doi.org/10.1007/978-3-030-35231-8_63
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