References
[1] Pamula T. Traffic flow analysis based on the real data using neural networks. Communications in Computer and Information Science. 2012;329: 364-371. doi: 10.1007/978-3-642-34050-5_41.
[2] Mai T, Ghosh B, Wilson S. Multivariate short-term traffic flow forecasting using Bayesian vector autoregressive moving average model. Proceedings of the 91st Transportation Research Board Annual Meeting, Washington, D.C. 2012. p. 233-240. doi: 10.1108/01371097456.
[3] Chang X, Gao M, Wang Y, Hou X. Seasonal autoregressive integrated moving average (SARIMA) model for precipitation time series. Journal of Mathematics and Statistics. 2012;8(4): 500-505. doi: 10.3844/jmssp.2012.500.505.
[4] Yadav RK, Balakrishnan M. Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series. Journal on Wireless Communication and Network. 2014; 15. doi: 10.1186/1687-1499-2014-15.
[5] Zhu J, Xu W, Jin H, Sun H. Prediction of urban rail traffic flow based on multiply wavelet-ARIMA model. In: Wang W, Bengler K, Jiang X. (eds) Green Intelligent Transportation Systems. GITSS 2016. Lecture Notes in Electrical Engineering. 2018;419: 561-576. doi: 10.1007/978-981-10-3551-7_44.
[6] Mahalakshmi G, Sridevi S, Rajaram S. A survey on forecasting of time series data. International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16). 2016. p. 1-8. doi: 10.1109/ICCTIDE.2016.7725358.
[7] Mehrmolaei S, Keyvanpour MR. Time series forecasting using improved ARIMA. 2016 Artificial Intelligence and Robotics (IRANOPEN), Qazvin. 2016. p. 92-97. doi: 10.1109/RIOS.2016.7529496.
[8] Gómez-Losada Á, Duch-Brown N. Time series forecasting by recommendation: An empirical analysis on Amazon marketplace. In: Abramowicz W, Corchuelo R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing. 2019;353: 45-54. doi: 10.1007/978-3-030-20485-3_4.
[9] Berk K. Time series analysis. In: Modeling and Forecasting Electricity Demand. Springer Spektrum Wiesbaden; 2015. p. 25-52. doi: 10.1007/978-3-658-08669-5_3.
[10] Ismail Fawaz H, Forestier G, Weber J. Deep learning for time series classification: A review. Data Mining and Knowledge Discovery. 2019;33: 917-963. doi: 10.1007/s10618-019-00619-1.
[11] Patarwal P, Dagar A, Bala R, Singh RP. Financial time series forecasting using deep learning network. In: Deka G, Kaiwartya O, Vashisth P, Rathee P. (eds) Applications of Computing and Communication Technologies. ICACCT 2018. Communications in Computer and Information Science. 2018;899: 23-33. doi: 10.1007/978-981-13-2035-4_3.
[12] Romeu P, et al. Time-series forecasting of indoor temperature using pre-trained deep neural networks. In: Mladenov V, et al. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science. 2018;8131: 451-458. doi: 10.1007/978-3-642-40728-4_57.
[13] Alghamdi T, et al. Forecasting traffic congestion using ARIMA modeling. 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). 2019. p. 1227-1232. doi: 10.1109/IWCMC.2019.8766698.
[14] Pan F, Zhang H, Xia M. A hybrid time-series forecasting model using extreme learning machines. 2009 Second International Conference on Intelligent Computation Technology and Automation. 2009;1: 933-936. doi: 10.1109/ICICTA.2009.232.
[15] Ma, et al. Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transportation Research Part C: Emerging Technologies. 2020;111: 352-372. doi: 10.1016/j.trc.2019.12.022.
[16] Wu, et al. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies. 2018;90: 166-180. doi: 10.1016/j.trc.2018.03.001.
[17] Azzedine B, Wang J. A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model. Ad Hoc Networks. 2020; 102224. doi: 10.1016/j.adhoc.2020.102224.
[18] Kumar, S. Vasantha, Lelitha Vanajakshi. Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review. 2015; 7(3):21. doi: 10.1007/s12544-015-0170-8.
[19] Li T, Hua M, Wu X. A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5). IEEE Access. 2020;8: 26933-26940. doi: 10.1109/ACCESS.2020.2971348.
[20] Kasun B, Bergmeir C, Smyl S. Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert Systems with Applications. 2020;140: 112896. doi: arXiv:1710.03222v2.
[21] Osipov, et al. Urban traffic flows forecasting by recurrent neural networks with spiral structures of layers. Neural Computing and Applications. 2020;32: 14885-14897. doi: 10.1007/s00521-020-04843-5.
[22] Xu, et al. Real-time road traffic state prediction based on ARIMA and Kalman filter. Frontiers of Information Technology & Electronic Engineering. 2017;18(2): 287-302. doi: 10.1631/FITEE.1500381.
[23] Hosseini, et al. Traffic flow prediction using MI algorithm and considering noisy and data loss conditions: An application to Minnesota traffic flow prediction. Promet – Traffic&Transportation. 2014;26(5): 393-403. doi: 10.7307/ptt.v26i5.1429.
[24] Lv, et al. Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems. 2014;16(2): 865-873. doi: 10.1109/TITS.2014.2345663.
[25] Luo, et al. Traffic flow prediction during the holidays based on DFT and SVR. Journal of Sensors. 2019; 1-10. doi: 10.1155/2019/6461450.
[26] Kranti K, Parida M, Katiyar VK. Short term traffic flow prediction for a non urban highway using artificial neural network. Procedia - Social and Behavioral Sciences. 2013;104(2): 755-764. doi. 10.1016/j.sbspro.2013.11.170.
[27] Ardalani-Farsa M, Zolfaghari S. Chaotic time series prediction with residual analysis method using hybrid Elman–NARX neural networks. Neurocomputing. 2010;73(13): 2540-2553. doi: 10.1016/j.neucom.2010.06.004.
[28] Lu S, Zhang Q, Chen G, Seng D. A combined method for short-term traffic flow prediction based on recurrent neural network. Alexandria Engineering Journal. 2020;60(1): 87-94. doi: 10.1016/j.aej.2020.06.008.
[29] Shen, et al. A novel time series forecasting model with deep learning. Neurocomputing. 2020;396: 302-313. doi: 10.1016/j.neucom.2018.12.084.
[30] Rajendran S, Bharathi A. Short‑term traffic prediction model for urban transportation using structure pattern and regression: An Indian context. SN Appl. Sci. 2020;2: 1159. doi: 10.1007/s42452-020-2946-2.
[31] Sharma B, et al. ANN based short-term traffic flow forecasting in undivided two lane highway. Journal of Big Data. 2018;5(1): 48. doi: 10.1186/s40537-018-0157-0.
[32] De H, Acquah G. Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship. Journal of Development and Agricultural Economics. 2010;2: 1-6. doi: 10.18551/rjoas.2017-05.05.
[33] Milenkovic M, et al. SARIMA modelling approach for railway passenger flow forecasting. Transport. 2015;60(2): 1-8. doi: 10.3846/16484142.2016.1139623.
[34] Cromwell JB, Labys WC, Terraza M. Univariate tests for time series models. Thousand Oaks, CA: Sage; 1994. p. 32-36. doi: 10.4135/9781412986458.
[35] Dataset: http://tris.highwaysengland.co.uk/detail/trafficflowdata#site-collapse-2011.