Hybrid Methods for Short-Term Traffic Flow Prediction Based on ARIMA-GARCH Model and Wavelet Neural Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8
Abstract
Accurate short-term traffic flow prediction is essential for real-time traffic control. A linear hybrid method and a nonlinear hybrid method for short-term traffic flow prediction are proposed with vehicle type as one concern. Traffic flow data are divided into the similar, volatile, and irregular parts. The selected methods are the autoregressive integrated moving average and generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model, the Markov model with state membership degree, and the wavelet neural network. The ARIMA-GARCH model is used to predict the similar and volatile parts, and the other methods are adopted to predict the irregular part. This paper aims at providing better prediction methods for short-term traffic flow, and comparing the advantages and disadvantages of the linear and nonlinear hybrid methods. Additionally, the impacts of vehicle type on the predicted values are analyzed. The proposed methods are tested using field data from Dalian, China, and Hefei, China. The results suggest that the developed nonlinear hybrid method should be used with vehicle type and sampling interval as concerns.
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Data Availability Statement
Some data used to support the findings of this study are available from http://www.openits.cn/ (accessed March 1, 2019). The rest of the data and the models or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research reported in this paper has been funded by the National Natural Science Foundation of China (Grant No. 51578111). The authors thank Meng Long, Lan Yang and Xiaojing Du for their assistance in English writing and proofreading. The authors acknowledge the anonymous reviewers for valuable comments.
References
Ahmed, M., and A. Cook. 1979. “Analysis of freeway traffic time-series data by using Box-Jenkins techniques.” Transp. Res. Rec. 722 (1): 1–9.
Behbahani, H., A. M. Amiri, R. Imaninasab, and M. Alizamir. 2018. “Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques.” J. Forecasting 37 (7): 767–780. https://doi.org/10.1002/for.2542.
Bollerslev, T. 1986. “Generalized autoregressive conditional heteroscedasticity.” J. Econometrics 31 (3): 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.
Brock, W. A., J. A. Scheinkman, W. D. Dechert, and B. LeBaron. 1996. “A test for independence based on the correlation dimension.” Econometric Rev. 15 (3): 197–235. https://doi.org/10.1080/07474939608800353.
Cui, Q., and J. Xia. 2014. “Time-varying confidence interval forecasting of travel time for urban arterials using ARIMA-GARCH model.” J. Southeast Univ. 30 (3): 358–362. https://doi.org/10.3969/j.issn.1003-7985.2014.03.019.
Dai, X., R. Fu, E. Zhao, Z. Zhang, Y. Lin, F. Wang, and L. Li. 2019. “DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending.” Transp. Res. Part C 103 (Jun): 142–157. https://doi.org/10.1016/j.trc.2019.03.022.
Ding, C., J. Duan, Y. Zhang, X. Wu, and G. Yu. 2018. “Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility.” IEEE Trans. Intell. Transp. Syst. 19 (4): 1054–1064. https://doi.org/10.1109/TITS.2017.2711046.
Ding, F., Z. Zhang, Y. Zhou, X. Chen, and B. Ran. 2019. “Large-scale full-coverage traffic speed estimation under extreme traffic conditions using a big data and deep learning approach: Case study in China.” J. Transp. Eng. Part A Syst. 145 (5): 1–13. https://doi.org/10.1061/JTEPBS.0000230.
Engle, R. F. 1982. “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation.” Econometrica 50 (4): 987–1007. https://doi.org/10.2307/1912773.
Feng, X., X. Ling, H. Zheng, Z. Chen, and Y. Xu. 2019. “Adaptive multi-kernel SVM with spatial-temporal correlation for short-term traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 20 (6): 2001–2013. https://doi.org/10.1109/TITS.2018.2854913.
Gao, Y., and S. Sun. 2010. “Multi-link traffic flow forecasting using neural networks.” In Proc., 2010 6th Int. Conf. on Natural Computation (ICNC 2010), 398–401, Yantai, China: IEEE. https://doi.org/10.1109/ICNC.2010.5582914.
Hansen, P. R., and A. Lunde. 2005. “A forecast comparison of volatility models: Does anything beat a GARCH(1,1)?” J. Appl. Econ. 20 (7): 873–889. https://doi.org/10.1002/jae.800.
Hara, Y., J. Suzuki, and M. Kuwahara. 2018. “Network-wide traffic state estimation using a mixture Gaussian graphical model and graphical lasso.” Transp. Res. Part C 86 (Jan): 622–638. https://doi.org/10.1016/j.trc.2017.12.007.
Kamarianakis, Y., H. O. Gao, and P. Prastacos. 2010. “Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions.” Transp. Res. Part C 18 (5): 821–840. https://doi.org/10.1016/j.trc.2009.11.001.
Kamarianakis, Y., and P. Prastacos. 2003. “Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches.” Transp. Res. Rec. 1857 (1): 74–84. https://doi.org/10.3141/1857-09.
Kang, J., Z. Duan, L. Tang, Y. Liu, and C. Wang. 2015. “A short term traffic flow prediction method based on Gaussian processes regression.” J. Transp. Syst. Eng. Inf. Technol. 15 (4): 51–56. https://doi.org/10.16097/j.cnki.1009-6744.2015.04.008.
Li, D., Y. Yan, and X. Zeng. 2017. “Combined short-term prediction model of station entry flow in urban rail transit.” Urban Rapid Rail Transit 30 (1): 54–58. https://doi.org/10.3969/j.issn.1672-6073.2017.01.012.
Li, L., S. He, J. Zhang, and R. Bin. 2016. “Short-term highway traffic flow prediction based on a hybrid strategy considering temporal-spatial information.” J, Adv. Transp. 50 (8): 2029–2040. https://doi.org/10.1002/atr.1443.
Liu, L., and R. Chen. 2017. “A novel passenger flow prediction model using deep learning methods.” Transp. Res. Part C 84 (Nov): 74–91. https://doi.org/10.1016/j.trc.2017.08.001.
Liu, Q., Y. Cai, H. Jiang, X. Chen, and J. Lu. 2018. “Traffic state spatial-temporal characteristic analysis and short-term forecasting based on manifold similarity.” IEEE Access 6 (Jan): 9690–9702. https://doi.org/10.1109/ACCESS.2017.2788639.
Ma, D., B. Sheng, S. Jin, X. Ma, and P. Gao. 2018. “Short-term traffic flow forecasting by selecting appropriate predictions based on pattern matching.” IEEE Access 6 (Nov): 75629–75638. https://doi.org/10.1109/ACCESS.2018.2879055.
Ministry of Transport of the People’s Republic of China. 2014. Technical standard of highway engineering. JTG B01. Beijing: Ministry of Transport of the People’s Republic of China.
Min, W., and L. Wynter. 2011. “Real-time road traffic prediction with spatio-temporal correlations.” Transp. Res. Part C 19 (4): 606–616. https://doi.org/10.1016/j.trc.2010.10.002.
Okutani, I., and Y. J. Stephanedes. 1984. “Dynamic prediction of traffic volume through Kalman filtering theory.” Transp. Res. Part B 18 (1): 1–11. https://doi.org/10.1016/0191-2615(84)90002-X.
Polson, N. G., and V. O. Sokolov. 2017. “Deep learning for short-term traffic flow prediction.” Transp. Res. Part C 79 (Jun): 1–17. https://doi.org/10.1016/j.trc.2017.02.024.
Qian, C., H. Xu, and N. Xu. 2013. “Research on combinational prediction model for expressway traffic volume.” Comput. Simul. 30 (4): 178–182. https://doi.org/10.3969/j.issn.1006-9348.2013.04.041.
Stathopoulos, A., D. Loukas, and T. Theodore. 2008. “Fuzzy modeling approach for combined forecasting of urban traffic flow.” Comput.-Aided Civ. Infrastruct. Eng. 23 (7): 521–535. https://doi.org/10.1111/j.1467-8667.2008.00558.x.
Sun, H., H. Liu, H. Xiao, R. R. He, and B. Ran. 2003. “Use of local linear regression model for short-term traffic forecasting.” Transp. Res. Rec. 1836 (1): 143–150. https://doi.org/10.3141/1836-18.
Sun, S., G. Yu, and C. Zhang. 2004 “Short-term traffic flow forecasting using sampling Markov chain method with incomplete data.” In Proc., 2004 IEEE Intelligent Vehicles Symp., 437–441. New York: IEEE. https://doi.org/10.1109/IVS.2004.1336423.
Tan, M., L. Feng, and J. Xu. 2007. “Traffic flow prediction based on hybrid ARIMA and ANN MODEL.” China J. Highway Transp. 20 (4): 118–121. https://doi.org/10.19721/j.cnki.1001-7372.2007.04.023.
Wang, J., and Q. Shi. 2013. “Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory.” Transp. Res. Part C 27 (Feb): 219–232. https://doi.org/10.1016/j.trc.2012.08.004.
Wang, Q., Q. Shu, and H. Huang. 2016. “Study on GARCH effect in traffic flow data and prediction model.” Comput. Simul. 33 (2): 194–197. https://doi.org/10.3969/j.issn.1006-9348.2016.02.041.
Wang, X., and L. Xu. 2018. “Short-term traffic flow prediction based on deep learning.” J. Transp. Syst. Eng. Inf. Technol. 18 (1): 81–88. https://doi.org/10.16097/j.cnki.1009-6744.2018.01.012.
Wei, L., H. Chen, Y. Wang, M. Zhang, and L. Wang. 2017. “Smoothing method of short-term traffic flow based on relevance vector machine.” J. Northwest Univ. (Nat. Sci. Ed.) 47 (1): 38–42. https://doi.org/10.16152/j.cnki.xdxbzr.2017-01-007.
Williams, B. 2001. “Multivariate vehicular traffic flow prediction: Evaluation of ARIMAX modeling.” Transp. Res. Rec. 1776 (1): 194–200. https://doi.org/10.3141/1776-25.
Williams, B., P. Durvasula, and D. Brown. 1998. “Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models.” Transp. Res. Rec. 1644 (1): 132–141. https://doi.org/10.3141/1644-14.
Wu, Y., H. Tan, and L. Qin. 2018. “A hybrid deep learning based traffic flow prediction method and its understanding.” Transp. Res. Part C 90 (May): 166–180. https://doi.org/10.1016/j.trc.2018.03.001.
Xie, J., and Y.-K. Choi. 2017. “Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data.” Int. J. Distrib. Sens. Netw. 13 (11): 1–11. https://doi.org/10.1177/1550147717745009.
Xie, Y., and Y. Zhang. 2006. “A wavelet network model for short-term traffic volume forecasting.” J. Intell. Transp. Syst. 10 (3): 141–150. https://doi.org/10.1080/15472450600798551.
Yang, H., and X. Hu. 2016. “Wavelet neural network with improved genetic algorithm for traffic flow time series prediction.” Optik 127 (19): 8103–8110. https://doi.org/10.1016/j.ijleo.2016.06.017.
Yang, H., Y. Zou, Z. Wang, and B. Wu. 2018. “A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain.” Can. J. Civ. Eng. 45 (2): 77–86. https://doi.org/10.1139/cjce-2017-0231.
Yang, M., Y. Liu, and Z. You. 2010. “The reliability of travel time forecasting.” IEEE Trans. Intell. Transp. Syst. 11 (1): 162–171. https://doi.org/10.1109/TITS.2009.2037136.
Yang, Z., Y. Wang, and Q. Guan. 2006. “Short-term traffic flow prediction method based on SVM.” J. Jilin Univ. (Eng. Technol. Ed.) 36 (6): 881–884. https://doi.org/10.13229/j.cnki.jdxbgxb2006.06.010.
Yao, R. H., L. Sun, and M. Long. 2019. “VSP-based emission factor calibration and signal timing optimisation for arterial streets.” IET Intel. Transport Syst. 13 (1): 228–241. https://doi.org/10.1049/iet-its.2018.5066.
Yao, R. H., X. T. Zhang, N. Wu, and X. M. Song. 2018. “Modeling and control of variable approach lanes on an arterial road: A case study of Dalian.” Can. J. Civ. Eng. 45 (11): 986–1003. https://doi.org/10.1139/cjce-2017-0432.
Yao, Z., Y. Jiang, P. Han, X. Luo, and T. Xu. 2017. “Traffic flow prediction model based on neural network in small time granularity.” J. Transp. Syst. Eng. Inf. Technol. 17 (1): 67–73. https://doi.org/10.16097/j.cnki.1009-6744.2017.01.011.
Ye, J., B. Li, and F. Liu. 2013. “Grey-Markov model with state membership degree and its application.” In Proc., 11th Int. Conf. of Numerical Analysis and Applied Mathematics (ICNAAM), 1745–1755. Athens, Greece: AMER INST PHYSICS. https://doi.org/10.1063/1.4825863.
Yu, G., J. Hu, C. Zhang, L. Zhuang, and J. Song. 2003. “Short-term traffic flow forecasting based on Markov chain model.” In Proc., IEEE IV2003: Intelligent Vehicles Symp., 208–212. New York: IEEE. https://doi.org/10.1109/IVS.2003.1212910.
Zhang, C., R. Song, and Y. Sun. 2011. “Kalman filter-based short-term passenger flow forecasting on bus stop.” J. Transp. Syst. Eng. Inf. Technol. 11 (4): 154–159. https://doi.org/10.16097/j.cnki.1009-6744.2011.04.019.
Zhang, Q., and A. Benveniste. 1992. “Wavelet networks.” IEEE Trans. Neural Networks 3 (6): 889–898. https://doi.org/10.1109/72.165591.
Zhang, Y. R., Y. L. Zhang, and A. Haghani. 2014. “A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model.” Transp. Res. Part C 43 (Jun): 65–78. https://doi.org/10.1016/j.trc.2013.11.011.
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©2020 American Society of Civil Engineers.
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Received: Oct 5, 2019
Accepted: Feb 19, 2020
Published online: Jun 12, 2020
Published in print: Aug 1, 2020
Discussion open until: Nov 12, 2020
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