Learning Identification Strategies for Traffic Flow Model: A Review Study
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
محبوبه زارع فیض آبادی
1
,
سید عابد حسینی
2
,
محبوبه هوشمند
3
1 - گروه مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
2 - گروه مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
3 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
کلید واژه: Traffic flow model prediction, ARIMA model, Hybrid model, Deep learning, Nonlinear macroscopic traffic model.,
چکیده مقاله :
ترافیک سفر و نتایج آن که شامل آلودگی هوا و اوضاع نابسامان اقتصادی می شود از جمله عواملی است که توسعه شهرهای سالم و پایدار را محدود می کند. پارامترهای مدل جریان ترافیک برای مدیریت شبکه راه های شهری مهم هستند. یادگیری استراتژی شناسایی باید به گونه ای باشد که در مدل سازی شبکه ترافیک موارد سادگی، دقت و اعتبار مدل را تضمین کند. در این مقاله روش‌های مختلف شناسایی سیستم جریان ترافیک از جمله مدل‌سازی جریان ترافیک و پیش‌بینی آن در مقالات مختلف بررسی و تحلیل می‌شود. در انتها مزایا و معایب روش های مختلف در این حوزه دسته بندی می شوند.
چکیده انگلیسی:
Travel traffic and its results, which include air pollution and the chaotic economic situation, are among the factors that limit the development of healthy and sustainable cities. Traffic flow model parameters are important for urban road network management. Learning the identification strategy should be such that the modeling of the traffic network guarantees the simplicity, accuracy, and validation of the model. In this paper, different methods of traffic flow system identification, including traffic flow modeling and its prediction, are reviewed and analyzed in articles. At the end, the advantages and disadvantages of different methods in this field are categ orized.
منابع و مأخذ:
References
[1] Abbas, N., Sharafeddine, S., Hajj, H., & Dawy, Z. (2020). Price-aware traffic splitting in D2D HetNets with cost-energy-QoE tradeoffs. Computer Networks, 172, 107169.
[2] Zhang, J., Wang, F. Y., Wang, K., Lin, W. H., Xu, X., & Chen, C. (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624-1639.
[3] Genders, W., & Razavi, S. (2019). Asynchronous n-step Q-learning adaptive traffic signal control. Journal of Intelligent Transportation Systems, 23(4), 319-331.
[4] Husein, D. M., Rizk, S., & Naim, H. Y. (2020). Differential effects of sucrase-isomaltase mutants on its trafficking and function in irritable bowel syndrome: similarities to congenital sucrase-isomaltase deficiency. Nutrients, 13(1), 9.
[5] Smith, B. L., Williams, B. M., & Oswald, R. K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 10(4), 303-321.
[6] Rahman, S. A., Mourad, A., & El Barachi, M. (2019). An infrastructure-assisted crowdsensing approach for on-demand traffic condition estimation. IEEE Access, 7, 163323-163340.
[7] Jiang, J., Chen, Q., Xue, J., Wang, H., & Chen, Z. (2020). A novel method about the representation and discrimination of traffic state. Sensors, 20(18), 5039.
[8] Chen, Z., Lu, Z., Chen, Q., Zhong, H., Zhang, Y., Xue, J., & Wu, C. (2022). Spatial–temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism. Information Sciences, 611, 522-539.
[9] Dai, L., Qin, W., Xu, H., Chen, T., & Qian, C. (2014, October). Urban traffic flow prediction: A MapReduce based parallel multivariate linear regression approach. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 2823-2827). IEEE.
[10] Song, Y., & Lu, J. (2018). RNN-based traffic flow prediction for dynamic reversible lane control decision. In Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018) (pp. 323-330).
[11] Zhang, D., & Kabuka, M. R. (2018). Combining weather condition data to predict traffic flow: a GRU‐based deep learning approach. IET Intelligent Transport Systems, 12(7), 578-585.
[12] Cao, S., & Liu, W. (2019). Lstm network based traffic flow prediction for cellular networks. In Simulation Tools and Techniques: 11th International Conference, SIMUtools 2019, Chengdu, China, July 8–10, 2019, Proceedings 11 (pp. 643-653). Springer International Publishing.
[13] Yang, D., Li, S., Peng, Z., Wang, P., Wang, J., & Yang, H. (2019). MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion. IEICE TRANSACTIONS on Information and Systems, 102(8), 1526-1536.
[14] Chen, Y., Zou, X., Li, K., Li, K., Yang, X., & Chen, C. (2021). Multiple local 3D CNNs for region-based prediction in smart cities. Information Sciences, 542, 476-491.
[15] Chen, Z., Zhao, B., Wang, Y., Duan, Z., & Zhao, X. (2020). Multitask learning and GCN-based taxi demand prediction for a traffic road network. Sensors, 20(13), 3776.
[16] Ma, D., Song, X., & Li, P. (2020). Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter-and intra-day traffic patterns. IEEE Transactions on Intelligent Transportation Systems, 22(5), 2627-2636.
[17] Ma, T., Antoniou, C., & Toledo, T. (2020). Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transportation Research Part C: Emerging Technologies, 111, 352-372.
[18] Du, S., Li, T., Gong, X., & Horng, S. J. (2020). A hybrid method for traffic flow forecasting using multimodal deep learning. International journal of computational intelligence systems, 13(1), 85-97.
[19] Ma, C., Dai, G., & Zhou, J. (2021). Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5615-5624.
[20] Zheng, H., Lin, F., Feng, X., & Chen, Y. (2020). A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 22(11), 6910-6920.
[21] Dong, S., Xia, Y., & Peng, T. (2021). Network abnormal traffic detection model based on semi-supervised deep reinforcement learning. IEEE Transactions on Network and Service Management, 18(4), 4197-4212.
[22] Van Hasselt, H., Guez, A., & Silver, D. (2016, March). Deep reinforcement learning with double q-learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 30, No. 1).
[23] Choi, H., Kim, M., Lee, G., & Kim, W. (2019). Unsupervised learning approach for network intrusion detection system using autoencoders. The Journal of Supercomputing, 75, 5597-5621.
[24] Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295.
[25] Hardegen, C., Pfülb, B., Rieger, S., & Gepperth, A. (2020). Predicting network flow characteristics using deep learning and real-world network traffic. IEEE Transactions on Network and Service Management, 17(4), 2662-2676.
[26] Yan, F., Qiu, J., & Tian, J. (2022). An iterative learning identification strategy for nonlinear macroscopic traffic flow model. Physica A: Statistical Mechanics and its Applications, 604, 127901.
[27] Wang, Z., Chu, R., Zhang, M., Wang, X., & Luan, S. (2020). An improved selective ensemble learning method for highway traffic flow state identification. IEEE Access, 8, 212623-212634.
[28] Billot, R., Faouzi, N. E. E., Sau, J., & De Vuyst, F. (2010). Integrating the impact of rain into traffic management: online traffic state estimation using sequential Monte Carlo techniques. Transportation research record, 2169(1), 141-149.
[29] Khan, S. M., Dey, K. C., & Chowdhury, M. (2017). Real-time traffic state estimation with connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1687-1699.
[30] Wang, Z., Chu, R., Wu, W., Li, Q., Cai, Z., Cao, N., & Gu, M. (2018). Identification and optimization models for a freight-integrated transportation corridor with line importance and freight communication capability. Ieee Access, 7, 11114-11126.
[31] Tian, G., Ren, Y., Feng, Y., Zhou, M., Zhang, H., & Tan, J. (2018). Modeling and planning for dual-objective selective disassembly using AND/OR graph and discrete artificial bee colony. IEEE Transactions on Industrial Informatics, 15(4), 2456-2468.
[32] Li, L., He, S., Zhang, J., & Ran, B. (2016). Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information. Journal of Advanced Transportation, 50(8), 2029-2040.
[33] An, J., Fu, L., Hu, M., Chen, W., & Zhan, J. (2019). A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information. Ieee Access, 7, 20708-20722.
[34] Mou, L., Zhao, P., Xie, H., & Chen, Y. (2019). T-LSTM: A long short-term memory neural network enhanced by temporal information for traffic flow prediction. Ieee Access, 7, 98053-98060.
[35] Chen, X., Cai, X., Liang, J., & Liu, Q. (2018). Ensemble learning multiple LSSVR with improved harmony search algorithm for short-term traffic flow forecasting. Ieee Access, 6, 9347-9357.
[36] Ngoduy, D., & Liu, R. (2007). Multiclass first-order simulation model to explain non-linear traffic phenomena. Physica A: Statistical Mechanics and its Applications, 385(2), 667-682.
[37] Acosta, C. D., Bürger, R., & Mejía, C. E. (2015). Efficient parameter estimation in a macroscopic traffic flow model by discrete mollification. Transportmetrica A: Transport Science, 11(8), 702-715.
[38] Thonhofer, E., & Jakubek, S. (2018). Investigation of stochastic variation of parameters for a macroscopic traffic model. Journal of Intelligent Transportation Systems, 22(6), 547-564.
[39] Zammit, L. C., Fabri, S. G., & Scerri, K. (2019). Real-time parametric modeling and estimation of urban traffic junctions. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4579-4589.
[40] Zammit, L. C., Fabri, S. G., & Scerri, K. (2017, July). Online state and parameter estimation for a 4-arm traffic junction. In 2017 25th Mediterranean Conference on Control and Automation (MED) (pp. 737-742). IEEE.
[41] Ma, T., Antoniou, C., & Toledo, T. (2020). Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transportation Research Part C: Emerging Technologies, 111, 352-372.
[42] Lu, S., Zhang, Q., Chen, G., & Seng, D. (2021). A combined method for short-term traffic flow prediction based on recurrent neural network. Alexandria Engineering Journal, 60(1), 87-94.
[43] Aljuaydi, F., Wiwatanapataphee, B., & Wu, Y. H. (2023). Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Alexandria engineering journal, 65, 151-162.
[44] Shekhar, S., & Williams, B. M. (2007). Adaptive seasonal time series models for forecasting short-term traffic flow. Transportation Research Record, 2024(1), 116-125.
[45] Guo, J., Huang, W., & Williams, B. M. (2014). Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies, 43, 50-64.
[46] Zhou, B., He, D., & Sun, Z. (2006). Traffic modeling and prediction using ARIMA/GARCH model. In Modeling and Simulation Tools for Emerging Telecommunication Networks: Needs, Trends, Challenges and Solutions (pp. 101-121). Springer US.
[47] Xian, Z. B., & Qiang, L. (2002). Arma-based traffic prediction and overload detection of network [j]. Journal of Computer Research and Development, 12.
[48] 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, 129(6), 664-672.
[49] Thomas, T., Weijermars, W., & Van Berkum, E. (2009). Predictions of urban volumes in single time series. IEEE Transactions on Intelligent Transportation Systems, 11(1), 71-80.
[50] Ma, C., Dai, G., & Zhou, J. (2021). Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5615-5624.
[51] Gu, Y., Lu, W., Xu, X., Qin, L., Shao, Z., & Zhang, H. (2019). An improved Bayesian combination model for short-term traffic prediction with deep learning. IEEE Transactions on Intelligent Transportation Systems, 21(3), 1332-1342.
[52] Zheng, J., & Ni, L. M. (2013, June). Time-dependent trajectory regression on road networks via multi-task learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 27, No. 1, pp. 1048-1055).
[53] Deng, D., Shahabi, C., Demiryurek, U., Zhu, L., Yu, R., & Liu, Y. (2016, August). Latent space model for road networks to predict time-varying traffic. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1525-1534).
[54] Liu, L., Zhen, J., Li, G., Zhan, G., He, Z., Du, B., & Lin, L. (2020). Dynamic spatial-temporal representation learning for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 22(11), 7169-7183.
[55] Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. New York, NY: Springer New York.
[56] Zhang, J., Huang, T., Wang, S., & Liu, Y. J. (2019). Future Internet: trends and challenges. Frontiers of Information Technology & Electronic Engineering, 20(9), 1185-1194.
[57] Yao, H., Mai, T., Jiang, C., Kuang, L., & Guo, S. (2019). AI routers & network mind: A hybrid machine learning paradigm for packet routing. IEEE computational intelligence magazine, 14(4), 21-30.
[58] Rusek, K., Suárez-Varela, J., Mestres, A., Barlet-Ros, P., & Cabellos-Aparicio, A. (2019, April). Unveiling the potential of graph neural networks for network modeling and optimization in SDN. In Proceedings of the 2019 ACM Symposium on SDN Research (pp. 140-151).
[59] Zhuang, Z., Wang, J., Qi, Q., Sun, H., & Liao, J. (2019). Toward greater intelligence in route planning: A graph-aware deep learning approach. IEEE Systems Journal, 14(2), 1658-1669.
[60] Zhang, J., Zheng, Y., & Qi, D. (2017, February). Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1).
[61] Zhang, J., Zheng, Y., Qi, D., Li, R., & Yi, X. (2016, October). DNN-based prediction model for spatio-temporal data. In Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 1-4).
[62] Xu, Z., Wang, Y., Long, M., Wang, J., & KLiss, M. (2018, July). PredCNN: Predictive Learning with Cascade Convolutions. In IJCAI (pp. 2940-2947).
[63] Zhang, J., Zheng, Y., Sun, J., & Qi, D. (2019). Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Transactions on Knowledge and Data Engineering, 32(3), 468-478.
[64] Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143.
[65] Zhao, Z., Chen, W., Wu, X., Chen, P. C., & Liu, J. (2017). LSTM network: a deep learning approach for short‐term traffic forecast. IET intelligent transport systems, 11(2), 68-75.
[66] Gu, Y., Lu, W., Qin, L., Li, M., & Shao, Z. (2019). Short-term prediction of lane-level traffic speeds: A fusion deep learning model. Transportation research part C: emerging technologies, 106, 1-16.
[67] Bao, X., Jiang, D., Yang, X., & Wang, H. (2021). An improved deep belief network for traffic prediction considering weather factors. Alexandria Engineering Journal, 60(1), 413-420.
[68] Qiao, W., Haghani, A., & Hamedi, M. (2012). Short-term travel time prediction considering the effects of weather. Transportation research record, 2308(1), 61-72.
[69] Yu, F., Wei, D., Zhang, S., & Shao, Y. (2019, September). 3D CNN-based accurate prediction for large-scale traffic flow. In 2019 4th International Conference on Intelligent Transportation Engineering (ICITE) (pp. 99-103). IEEE.
[70] Chen, Z., Zhao, B., Wang, Y., Duan, Z., & Zhao, X. (2020). Multitask learning and GCN-based taxi demand prediction for a traffic road network. Sensors, 20(13), 3776.
[71] Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019, July). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 922-929).
[72] He, K., Huang, Y., Chen, X., Zhou, Z., & Yu, S. (2019, December). Graph attention spatial-temporal network for deep learning based mobile traffic prediction. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
[73] Cheng, X., Zhang, R., Zhou, J., & Xu, W. (2018, July). Deeptransport: Learning spatial-temporal dependency for traffic condition forecasting. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
[74] Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926.
[75] Tian, Y., Zhang, K., Li, J., Lin, X., & Yang, B. (2018). LSTM-based traffic flow prediction with missing data. Neurocomputing, 318, 297-305.
[76] Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314.
[77] Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366.
[78] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
[79] Kalchbrenner, N., Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., & Kavukcuoglu, K. (2017, July). Video pixel networks. In International Conference on Machine Learning (pp. 1771-1779). PMLR.
[80] Lotter, W., Kreiman, G., & Cox, D. (2016). Deep predictive coding networks for video prediction and unsupervised learning. arXiv preprint arXiv:1605.08104.
[81] Wang, Y., Long, M., Wang, J., Gao, Z., & Yu, P. S. (2017). Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. Advances in neural information processing systems, 30.
[82] Funahashi, K. I., & Nakamura, Y. (1993). Approximation of dynamical systems by continuous time recurrent neural networks. Neural networks, 6(6), 801-806.
[83] Fu, R., Zhang, Z., & Li, L. (2016, November). Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth academic annual conference of Chinese association of automation (YAC) (pp. 324-328). IEEE.
[84] Mackenzie, J., Roddick, J. F., & Zito, R. (2018). An evaluation of HTM and LSTM for short-term arterial traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 20(5), 1847-1857.
[85] Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1-17.
[86] Zhao, J., Gao, Y., Yang, Z., Li, J., Feng, Y., Qin, Z., & Bai, Z. (2019). Truck traffic speed prediction under non-recurrent congestion: Based on optimized deep learning algorithms and GPS data. IEEE Access, 7, 9116-9127.
[87] Lee, S., Ngoduy, D., & Keyvan-Ekbatani, M. (2019). Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways. Transportation research part C: emerging technologies, 106, 360-377.
[88] Dabiri, S., & Heaslip, K. (2019). Developing a Twitter-based traffic event detection model using deep learning architectures. Expert systems with applications, 118, 425-439.
[89] Luong, M. T. (2015). Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025.
[90] Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649). Ieee.
[91] Dai, G., Tang, J., & Luo, W. (2023). Short-term traffic flow prediction: an ensemble machine learning approach. Alexandria Engineering Journal, 74, 467-480.
[92] Li, L., Qu, X., Zhang, J., Li, H., & Ran, B. (2018). Travel time prediction for highway network based on the ensemble empirical mode decomposition and random vector functional link network. Applied Soft Computing, 73, 921-932.
[93] Wu, Y., Tan, H., Qin, L., Ran, B., & Jiang, Z. (2018). A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166-180.
[94] 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: Emerging Technologies, 13(3), 211-234.
[95] Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926.
[96] Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2019). T-GCN: A temporal graph convolutional network for traffic prediction. IEEE transactions on intelligent transportation systems, 21(9), 3848-3858.
[97] Guo, J., Song, C., & Wang, H. (2019, November). A multi-step traffic speed forecasting model based on graph convolutional LSTM. In 2019 Chinese Automation Congress (CAC) (pp. 2466-2471). IEEE.
[98] Luo, X., Li, D., Yang, Y., & Zhang, S. (2019). Spatiotemporal traffic flow prediction with KNN and LSTM. Journal of Advanced Transportation, 2019(1), 4145353.
[99] Dai, G., Ma, C., & Xu, X. (2019). Short-term traffic flow prediction method for urban road sections based on space–time analysis and GRU. IEEE Access, 7, 143025-143035.
[100] Cortes, C. (1995). Support-Vector Networks. Machine Learning.
[101] Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. Advances in neural information processing systems, 9.
[102] Schölkopf, B., Burges, C. J., & Smola, A. J. (Eds.). (1999). Advances in kernel methods: support vector learning. MIT press.
[103] Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997, October). Predicting time series with support vector machines. In International conference on artificial neural networks (pp. 999-1004). Berlin, Heidelberg: Springer Berlin Heidelberg.
[104] Yang, W., Yang, D., Zhao, Y., & Gong, J. (2010, January). Traffic flow prediction based on wavelet transform and radial basis function network. In 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM) (Vol. 2, pp. 969-972). IEEE.
[105] Mercer, J. (1909). Xvi. functions of positive and negative type, and their connection the theory of integral equations. Philosophical transactions of the royal society of London. Series A, containing papers of a mathematical or physical character, 209(441-458), 415-446.
[106] Bertsekas, D. P. (1997). Nonlinear programming. Journal of the Operational Research Society, 48(3), 334-334.
[107] Fletcher, R. (2000). Practical methods of optimization. John Wiley & Sons.
[108] Herbrich, R. (2001). Learning kernel classifiers: theory and algorithms. MIT press.
[109] Scholkopf, B., & Smola, A. J. (2018). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
[110] Pérez-Cruz, F., Camps-Valls, G., Soria-Olivas, E., Pérez-Ruixo, J. J., Figueiras-Vidal, A. R., & Artés-Rodríguez, A. (2002, August). Multi-dimensional function approximation and regression estimation. In International conference on artificial neural networks (pp. 757-762). Berlin, Heidelberg: Springer Berlin Heidelberg.
[111] Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Berlin, Heidelberg: Springer Berlin Heidelberg.
[112] Ngoduy, D., & Liu, R. (2007). Multiclass first-order simulation model to explain non-linear traffic phenomena. Physica A: Statistical Mechanics and its Applications, 385(2), 667-682.
References
[1] Abbas, N., Sharafeddine, S., Hajj, H., & Dawy, Z. (2020). Price-aware traffic splitting in D2D HetNets with cost-energy-QoE tradeoffs. Computer Networks, 172, 107169.
[2] Zhang, J., Wang, F. Y., Wang, K., Lin, W. H., Xu, X., & Chen, C. (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624-1639.
[3] Genders, W., & Razavi, S. (2019). Asynchronous n-step Q-learning adaptive traffic signal control. Journal of Intelligent Transportation Systems, 23(4), 319-331.
[4] Husein, D. M., Rizk, S., & Naim, H. Y. (2020). Differential effects of sucrase-isomaltase mutants on its trafficking and function in irritable bowel syndrome: similarities to congenital sucrase-isomaltase deficiency. Nutrients, 13(1), 9.
[5] Smith, B. L., Williams, B. M., & Oswald, R. K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 10(4), 303-321.
[6] Rahman, S. A., Mourad, A., & El Barachi, M. (2019). An infrastructure-assisted crowdsensing approach for on-demand traffic condition estimation. IEEE Access, 7, 163323-163340.
[7] Jiang, J., Chen, Q., Xue, J., Wang, H., & Chen, Z. (2020). A novel method about the representation and discrimination of traffic state. Sensors, 20(18), 5039.
[8] Chen, Z., Lu, Z., Chen, Q., Zhong, H., Zhang, Y., Xue, J., & Wu, C. (2022). Spatial–temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism. Information Sciences, 611, 522-539.
[9] Dai, L., Qin, W., Xu, H., Chen, T., & Qian, C. (2014, October). Urban traffic flow prediction: A MapReduce based parallel multivariate linear regression approach. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 2823-2827). IEEE.
[10] Song, Y., & Lu, J. (2018). RNN-based traffic flow prediction for dynamic reversible lane control decision. In Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018) (pp. 323-330).
[11] Zhang, D., & Kabuka, M. R. (2018). Combining weather condition data to predict traffic flow: a GRU‐based deep learning approach. IET Intelligent Transport Systems, 12(7), 578-585.
[12] Cao, S., & Liu, W. (2019). Lstm network based traffic flow prediction for cellular networks. In Simulation Tools and Techniques: 11th International Conference, SIMUtools 2019, Chengdu, China, July 8–10, 2019, Proceedings 11 (pp. 643-653). Springer International Publishing.
[13] Yang, D., Li, S., Peng, Z., Wang, P., Wang, J., & Yang, H. (2019). MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion. IEICE TRANSACTIONS on Information and Systems, 102(8), 1526-1536.
[14] Chen, Y., Zou, X., Li, K., Li, K., Yang, X., & Chen, C. (2021). Multiple local 3D CNNs for region-based prediction in smart cities. Information Sciences, 542, 476-491.
[15] Chen, Z., Zhao, B., Wang, Y., Duan, Z., & Zhao, X. (2020). Multitask learning and GCN-based taxi demand prediction for a traffic road network. Sensors, 20(13), 3776.
[16] Ma, D., Song, X., & Li, P. (2020). Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter-and intra-day traffic patterns. IEEE Transactions on Intelligent Transportation Systems, 22(5), 2627-2636.
[17] Ma, T., Antoniou, C., & Toledo, T. (2020). Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transportation Research Part C: Emerging Technologies, 111, 352-372.
[18] Du, S., Li, T., Gong, X., & Horng, S. J. (2020). A hybrid method for traffic flow forecasting using multimodal deep learning. International journal of computational intelligence systems, 13(1), 85-97.
[19] Ma, C., Dai, G., & Zhou, J. (2021). Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5615-5624.
[20] Zheng, H., Lin, F., Feng, X., & Chen, Y. (2020). A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 22(11), 6910-6920.
[21] Dong, S., Xia, Y., & Peng, T. (2021). Network abnormal traffic detection model based on semi-supervised deep reinforcement learning. IEEE Transactions on Network and Service Management, 18(4), 4197-4212.
[22] Van Hasselt, H., Guez, A., & Silver, D. (2016, March). Deep reinforcement learning with double q-learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 30, No. 1).
[23] Choi, H., Kim, M., Lee, G., & Kim, W. (2019). Unsupervised learning approach for network intrusion detection system using autoencoders. The Journal of Supercomputing, 75, 5597-5621.
[24] Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295.
[25] Hardegen, C., Pfülb, B., Rieger, S., & Gepperth, A. (2020). Predicting network flow characteristics using deep learning and real-world network traffic. IEEE Transactions on Network and Service Management, 17(4), 2662-2676.
[26] Yan, F., Qiu, J., & Tian, J. (2022). An iterative learning identification strategy for nonlinear macroscopic traffic flow model. Physica A: Statistical Mechanics and its Applications, 604, 127901.
[27] Wang, Z., Chu, R., Zhang, M., Wang, X., & Luan, S. (2020). An improved selective ensemble learning method for highway traffic flow state identification. IEEE Access, 8, 212623-212634.
[28] Billot, R., Faouzi, N. E. E., Sau, J., & De Vuyst, F. (2010). Integrating the impact of rain into traffic management: online traffic state estimation using sequential Monte Carlo techniques. Transportation research record, 2169(1), 141-149.
[29] Khan, S. M., Dey, K. C., & Chowdhury, M. (2017). Real-time traffic state estimation with connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1687-1699.
[30] Wang, Z., Chu, R., Wu, W., Li, Q., Cai, Z., Cao, N., & Gu, M. (2018). Identification and optimization models for a freight-integrated transportation corridor with line importance and freight communication capability. Ieee Access, 7, 11114-11126.
[31] Tian, G., Ren, Y., Feng, Y., Zhou, M., Zhang, H., & Tan, J. (2018). Modeling and planning for dual-objective selective disassembly using AND/OR graph and discrete artificial bee colony. IEEE Transactions on Industrial Informatics, 15(4), 2456-2468.
[32] Li, L., He, S., Zhang, J., & Ran, B. (2016). Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information. Journal of Advanced Transportation, 50(8), 2029-2040.
[33] An, J., Fu, L., Hu, M., Chen, W., & Zhan, J. (2019). A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information. Ieee Access, 7, 20708-20722.
[34] Mou, L., Zhao, P., Xie, H., & Chen, Y. (2019). T-LSTM: A long short-term memory neural network enhanced by temporal information for traffic flow prediction. Ieee Access, 7, 98053-98060.
[35] Chen, X., Cai, X., Liang, J., & Liu, Q. (2018). Ensemble learning multiple LSSVR with improved harmony search algorithm for short-term traffic flow forecasting. Ieee Access, 6, 9347-9357.
[36] Ngoduy, D., & Liu, R. (2007). Multiclass first-order simulation model to explain non-linear traffic phenomena. Physica A: Statistical Mechanics and its Applications, 385(2), 667-682.
[37] Acosta, C. D., Bürger, R., & Mejía, C. E. (2015). Efficient parameter estimation in a macroscopic traffic flow model by discrete mollification. Transportmetrica A: Transport Science, 11(8), 702-715.
[38] Thonhofer, E., & Jakubek, S. (2018). Investigation of stochastic variation of parameters for a macroscopic traffic model. Journal of Intelligent Transportation Systems, 22(6), 547-564.
[39] Zammit, L. C., Fabri, S. G., & Scerri, K. (2019). Real-time parametric modeling and estimation of urban traffic junctions. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4579-4589.
[40] Zammit, L. C., Fabri, S. G., & Scerri, K. (2017, July). Online state and parameter estimation for a 4-arm traffic junction. In 2017 25th Mediterranean Conference on Control and Automation (MED) (pp. 737-742). IEEE.
[41] Ma, T., Antoniou, C., & Toledo, T. (2020). Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transportation Research Part C: Emerging Technologies, 111, 352-372.
[42] Lu, S., Zhang, Q., Chen, G., & Seng, D. (2021). A combined method for short-term traffic flow prediction based on recurrent neural network. Alexandria Engineering Journal, 60(1), 87-94.
[43] Aljuaydi, F., Wiwatanapataphee, B., & Wu, Y. H. (2023). Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Alexandria engineering journal, 65, 151-162.
[44] Shekhar, S., & Williams, B. M. (2007). Adaptive seasonal time series models for forecasting short-term traffic flow. Transportation Research Record, 2024(1), 116-125.
[45] Guo, J., Huang, W., & Williams, B. M. (2014). Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies, 43, 50-64.
[46] Zhou, B., He, D., & Sun, Z. (2006). Traffic modeling and prediction using ARIMA/GARCH model. In Modeling and Simulation Tools for Emerging Telecommunication Networks: Needs, Trends, Challenges and Solutions (pp. 101-121). Springer US.
[47] Xian, Z. B., & Qiang, L. (2002). Arma-based traffic prediction and overload detection of network [j]. Journal of Computer Research and Development, 12.
[48] 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, 129(6), 664-672.
[49] Thomas, T., Weijermars, W., & Van Berkum, E. (2009). Predictions of urban volumes in single time series. IEEE Transactions on Intelligent Transportation Systems, 11(1), 71-80.
[50] Ma, C., Dai, G., & Zhou, J. (2021). Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5615-5624.
[51] Gu, Y., Lu, W., Xu, X., Qin, L., Shao, Z., & Zhang, H. (2019). An improved Bayesian combination model for short-term traffic prediction with deep learning. IEEE Transactions on Intelligent Transportation Systems, 21(3), 1332-1342.
[52] Zheng, J., & Ni, L. M. (2013, June). Time-dependent trajectory regression on road networks via multi-task learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 27, No. 1, pp. 1048-1055).
[53] Deng, D., Shahabi, C., Demiryurek, U., Zhu, L., Yu, R., & Liu, Y. (2016, August). Latent space model for road networks to predict time-varying traffic. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1525-1534).
[54] Liu, L., Zhen, J., Li, G., Zhan, G., He, Z., Du, B., & Lin, L. (2020). Dynamic spatial-temporal representation learning for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 22(11), 7169-7183.
[55] Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. New York, NY: Springer New York.
[56] Zhang, J., Huang, T., Wang, S., & Liu, Y. J. (2019). Future Internet: trends and challenges. Frontiers of Information Technology & Electronic Engineering, 20(9), 1185-1194.
[57] Yao, H., Mai, T., Jiang, C., Kuang, L., & Guo, S. (2019). AI routers & network mind: A hybrid machine learning paradigm for packet routing. IEEE computational intelligence magazine, 14(4), 21-30.
[58] Rusek, K., Suárez-Varela, J., Mestres, A., Barlet-Ros, P., & Cabellos-Aparicio, A. (2019, April). Unveiling the potential of graph neural networks for network modeling and optimization in SDN. In Proceedings of the 2019 ACM Symposium on SDN Research (pp. 140-151).
[59] Zhuang, Z., Wang, J., Qi, Q., Sun, H., & Liao, J. (2019). Toward greater intelligence in route planning: A graph-aware deep learning approach. IEEE Systems Journal, 14(2), 1658-1669.
[60] Zhang, J., Zheng, Y., & Qi, D. (2017, February). Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1).
[61] Zhang, J., Zheng, Y., Qi, D., Li, R., & Yi, X. (2016, October). DNN-based prediction model for spatio-temporal data. In Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 1-4).
[62] Xu, Z., Wang, Y., Long, M., Wang, J., & KLiss, M. (2018, July). PredCNN: Predictive Learning with Cascade Convolutions. In IJCAI (pp. 2940-2947).
[63] Zhang, J., Zheng, Y., Sun, J., & Qi, D. (2019). Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Transactions on Knowledge and Data Engineering, 32(3), 468-478.
[64] Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143.
[65] Zhao, Z., Chen, W., Wu, X., Chen, P. C., & Liu, J. (2017). LSTM network: a deep learning approach for short‐term traffic forecast. IET intelligent transport systems, 11(2), 68-75.
[66] Gu, Y., Lu, W., Qin, L., Li, M., & Shao, Z. (2019). Short-term prediction of lane-level traffic speeds: A fusion deep learning model. Transportation research part C: emerging technologies, 106, 1-16.
[67] Bao, X., Jiang, D., Yang, X., & Wang, H. (2021). An improved deep belief network for traffic prediction considering weather factors. Alexandria Engineering Journal, 60(1), 413-420.
[68] Qiao, W., Haghani, A., & Hamedi, M. (2012). Short-term travel time prediction considering the effects of weather. Transportation research record, 2308(1), 61-72.
[69] Yu, F., Wei, D., Zhang, S., & Shao, Y. (2019, September). 3D CNN-based accurate prediction for large-scale traffic flow. In 2019 4th International Conference on Intelligent Transportation Engineering (ICITE) (pp. 99-103). IEEE.
[70] Chen, Z., Zhao, B., Wang, Y., Duan, Z., & Zhao, X. (2020). Multitask learning and GCN-based taxi demand prediction for a traffic road network. Sensors, 20(13), 3776.
[71] Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019, July). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 922-929).
[72] He, K., Huang, Y., Chen, X., Zhou, Z., & Yu, S. (2019, December). Graph attention spatial-temporal network for deep learning based mobile traffic prediction. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
[73] Cheng, X., Zhang, R., Zhou, J., & Xu, W. (2018, July). Deeptransport: Learning spatial-temporal dependency for traffic condition forecasting. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
[74] Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926.
[75] Tian, Y., Zhang, K., Li, J., Lin, X., & Yang, B. (2018). LSTM-based traffic flow prediction with missing data. Neurocomputing, 318, 297-305.
[76] Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314.
[77] Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366.
[78] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
[79] Kalchbrenner, N., Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., & Kavukcuoglu, K. (2017, July). Video pixel networks. In International Conference on Machine Learning (pp. 1771-1779). PMLR.
[80] Lotter, W., Kreiman, G., & Cox, D. (2016). Deep predictive coding networks for video prediction and unsupervised learning. arXiv preprint arXiv:1605.08104.
[81] Wang, Y., Long, M., Wang, J., Gao, Z., & Yu, P. S. (2017). Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. Advances in neural information processing systems, 30.
[82] Funahashi, K. I., & Nakamura, Y. (1993). Approximation of dynamical systems by continuous time recurrent neural networks. Neural networks, 6(6), 801-806.
[83] Fu, R., Zhang, Z., & Li, L. (2016, November). Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth academic annual conference of Chinese association of automation (YAC) (pp. 324-328). IEEE.
[84] Mackenzie, J., Roddick, J. F., & Zito, R. (2018). An evaluation of HTM and LSTM for short-term arterial traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 20(5), 1847-1857.
[85] Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1-17.
[86] Zhao, J., Gao, Y., Yang, Z., Li, J., Feng, Y., Qin, Z., & Bai, Z. (2019). Truck traffic speed prediction under non-recurrent congestion: Based on optimized deep learning algorithms and GPS data. IEEE Access, 7, 9116-9127.
[87] Lee, S., Ngoduy, D., & Keyvan-Ekbatani, M. (2019). Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways. Transportation research part C: emerging technologies, 106, 360-377.
[88] Dabiri, S., & Heaslip, K. (2019). Developing a Twitter-based traffic event detection model using deep learning architectures. Expert systems with applications, 118, 425-439.
[89] Luong, M. T. (2015). Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025.
[90] Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649). Ieee.
[91] Dai, G., Tang, J., & Luo, W. (2023). Short-term traffic flow prediction: an ensemble machine learning approach. Alexandria Engineering Journal, 74, 467-480.
[92] Li, L., Qu, X., Zhang, J., Li, H., & Ran, B. (2018). Travel time prediction for highway network based on the ensemble empirical mode decomposition and random vector functional link network. Applied Soft Computing, 73, 921-932.
[93] Wu, Y., Tan, H., Qin, L., Ran, B., & Jiang, Z. (2018). A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166-180.
[94] 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: Emerging Technologies, 13(3), 211-234.
[95] Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926.
[96] Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2019). T-GCN: A temporal graph convolutional network for traffic prediction. IEEE transactions on intelligent transportation systems, 21(9), 3848-3858.
[97] Guo, J., Song, C., & Wang, H. (2019, November). A multi-step traffic speed forecasting model based on graph convolutional LSTM. In 2019 Chinese Automation Congress (CAC) (pp. 2466-2471). IEEE.
[98] Luo, X., Li, D., Yang, Y., & Zhang, S. (2019). Spatiotemporal traffic flow prediction with KNN and LSTM. Journal of Advanced Transportation, 2019(1), 4145353.
[99] Dai, G., Ma, C., & Xu, X. (2019). Short-term traffic flow prediction method for urban road sections based on space–time analysis and GRU. IEEE Access, 7, 143025-143035.
[100] Cortes, C. (1995). Support-Vector Networks. Machine Learning.
[101] Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. Advances in neural information processing systems, 9.
[102] Schölkopf, B., Burges, C. J., & Smola, A. J. (Eds.). (1999). Advances in kernel methods: support vector learning. MIT press.
[103] Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997, October). Predicting time series with support vector machines. In International conference on artificial neural networks (pp. 999-1004). Berlin, Heidelberg: Springer Berlin Heidelberg.
[104] Yang, W., Yang, D., Zhao, Y., & Gong, J. (2010, January). Traffic flow prediction based on wavelet transform and radial basis function network. In 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM) (Vol. 2, pp. 969-972). IEEE.
[105] Mercer, J. (1909). Xvi. functions of positive and negative type, and their connection the theory of integral equations. Philosophical transactions of the royal society of London. Series A, containing papers of a mathematical or physical character, 209(441-458), 415-446.
[106] Bertsekas, D. P. (1997). Nonlinear programming. Journal of the Operational Research Society, 48(3), 334-334.
[107] Fletcher, R. (2000). Practical methods of optimization. John Wiley & Sons.
[108] Herbrich, R. (2001). Learning kernel classifiers: theory and algorithms. MIT press.
[109] Scholkopf, B., & Smola, A. J. (2018). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
[110] Pérez-Cruz, F., Camps-Valls, G., Soria-Olivas, E., Pérez-Ruixo, J. J., Figueiras-Vidal, A. R., & Artés-Rodríguez, A. (2002, August). Multi-dimensional function approximation and regression estimation. In International conference on artificial neural networks (pp. 757-762). Berlin, Heidelberg: Springer Berlin Heidelberg.
[111] Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Berlin, Heidelberg: Springer Berlin Heidelberg.
[112] Ngoduy, D., & Liu, R. (2007). Multiclass first-order simulation model to explain non-linear traffic phenomena. Physica A: Statistical Mechanics and its Applications, 385(2), 667-682.