Abstract
In this digital interconnected era, Intelligent Transportation System (ITS) bridges the gap between communication and transportation engineering in a smarter way, thereby facilitating the trespassers and travellers with forecasting of traffic and broadcasting of traffic incidents, and infotainment data. Automatic prediction of congestion and traffic flow at one point is a challenging task. Although many machine learning algorithms exist for prediction, the selection of appropriate parameters of algorithms had a great impact on the accuracy of prediction. Hybrid combination of Grey Wolf Optimization (GWO) with new emerging Bald Eagle Search (BES) Optimization algorithm has been proposed to optimize the parameters of Support Vector regression to predict the traffic flow. This hybrid SVR-GWO-BES, has been applied to real-time traffic data of the open-source Performance Measurement system dataset and Indian road traffic, which has been proven to be better than existing methodologies.
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14 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04148-6
References
Abdulhai B, Porwal H, Recker W (2002) Short-term traffic flow prediction using neuro-genetic algorithms. J Intell Transport Syst Technol Plan Oper 7(1):3–41
Al Shorman A, Faris H, Aljarah I (2019) Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection. J Amb Intell Hum Comput. https://doi.org/10.1007/s12652-019-01387-y
Alsattar HA, Zaidan AA, Zaidan BB (2019) Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev
Anitha P, Kaarthick B (2019) Oppositional based Laplacian grey wolf optimization algorithm with SVM for data mining in intrusion detection system. J Amb Intell Hum Comput. https://doi.org/10.1007/s12652-019-01606-6
Bidisha G, Biswajit B, Margaret O (2007) Bayesian time-series model for short-term traffic flow forecasting. J Transport Eng 133(3):180–189
Cai L, Chen Q, Cai W, Xu X, Zhou T, Qin J (2019) SVRGSA: a hybrid learning based model for short-term traffic flow forecasting. IET Intell Transp Syst
Caltrans PEMS (2020) http://pems.dot.ca.gov/
Cambridge Systematics (2005) Traffic congestion and reliability: trends and advanced strategies for congestion Mitigation. https://ops.fhwa.dot.gov/congestion_report/congestion_report_05.pdf. Accessed October 2019
Chenyi C, Hu J, Meng Q, Zhang Y (2011) Short-time traffic flow prediction with ARIMA-GARCH model. IEEE Intell Veh Symp
Chun-Hsin W, Ho JM, Lee DT (2004) Travel-time prediction with support vector regression. IEEE Trans Intell Transp Syst 5(4):276–281
Cong Y, Wang J, Li X (2016) Traffic flow forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Proc Eng
Fan J, Jiang J, Zhang C, Zhou Z (2003) Time-dependent diffusion models for term structure dynamics. Stat Sin 965–992
Huang A, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transport Syst
Kadam VJ, Jadhav SM, Vijayakumar K (2019) Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression. J Med Syst. https://doi.org/10.1007/s10916-019-1397-z
Kamarianakis Y, Prastacos P (2005) Space-time modeling of traffic flow. Comput Geosci 119–133
Khalid N, Mumtaz H, Mansoor H, Rashid A et al (2017) Physiological, biochemical and defense system responses of Parthenium hysterophorus to vehicular exhaust pollution. Pak J Bot 49(1):67–75
Li X (2019) Intelligent transportation systems in big data. J Amb Intell Hum Comput. https://doi.org/10.1007/s12652-018-1028-4
Liu M, Wang R, Wu J, Kemp R (2005) A genetic-algorithm-based neural network approach for short-term traffic flow forecasting. International Symposium on Neural Networks, Berlin
Lopez-Garcia P, Onieva E, Osaba E, Masegosa AD, Perallos A (2015) A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans Intell Transp Syst 17(2):557–569
Lv Y, Duan Y, Kang W, Li Z, Wang F (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873
Mariette A, Rahul K (2015) Support vector regression. Efficient learning machines theories, concepts, and applications for engineers and system designers 67–80
Mirjalili S, Seyed MM, Andrew L (2014) Grey wolf optimizer. Adv Eng Softw
Moorthy CK, Ratcliffe BG (1988) Short term traffic forecasting using time series methods. Transport Plan Technol 12(1):45–56
Pradeep MKK, Saravanan M, Thenmozhi M, Vijayakumar K (2019) Intrusion detection system based on GA-fuzzy classifier for detecting malicious attacks. Wiley, New York. https://doi.org/10.1002/cpe.5242
Rashedi E, Hossein NP, Saeid S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Sangsoo L, Fambro DB (1999) Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp Res Rec 1678(1):179–188
Shiliang S, Zhang C, Yu G (2006) A Bayesian network approach to traffic flow forecasting. IEEE Trans Intell Transp Syst 7(1):124–132
Shuyu D, Niu D, Han Y (2018) Forecasting of power grid investment in china based on support vector machine optimized by differential evolution algorithm and grey wolf optimization algorithm. Appl Sci 8:4
Smith BL, Williams BM (2002) Comparison of parametric and nonparametric models for traffic flow forecasting. Transp Res 10(4):303–321
Su H, Zhang L, Yu S (2007) Short-term traffic flow prediction based on incremental support vector regression. Third International Conference on Natural Computation
The Third Eye: Managing the traffic (2019) https://www.trafficinfratech.com/the-third-eye-managing-the-traffic/4/
Vijayakumar K, Pradeep MKK, Jesline D (2019) Implementation of software agents and advanced aoa for disease data analysis. J Med Syst. https://doi.org/10.1007/s10916-019-1411-5
Wenbin H, Yan L, Liu K, Wang H (2016) A short-term traffic flow forecasting method based on the hybrid PSO-SVR. Neural Process Lett 43:155–172. https://doi.org/10.1007/s11063-015-9409-6
Yalda R, Amir HR, Hamidreza A (2017) Short-term traffic flow prediction using time-varying Vasicek model. Transport Rese Part C Emerg Technol 74:168–181
Zhou T, Han G, Xu X, Lin Z, Han C, Huang Y, Qin J (2017) δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting. Neurocomputing
Zhang F, Yan X, Zeng C, Zhang M, Shrestha S, Devkota LP, Yao T (2012) Influence of traffic activity on heavy metal concentrations of roadside farmland soil in mountainous areas. Int J Environ Res Public Health 9(5):1715–1731
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04148-6"
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Angayarkanni, S.A., Sivakumar, R. & Ramana Rao, Y.V. RETRACTED ARTICLE: Hybrid Grey Wolf: Bald Eagle search optimized support vector regression for traffic flow forecasting. J Ambient Intell Human Comput 12, 1293–1304 (2021). https://doi.org/10.1007/s12652-020-02182-w
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DOI: https://doi.org/10.1007/s12652-020-02182-w