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Traffic Prediction Using Multifaceted Techniques: A Survey

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Abstract

Road transportation is the largest and complex nonlinear entity of the traffic management system. Accurate prediction of traffic-related information is necessary for an effective functioning of Intelligent Transportation System (ITS). It is still a challenge for the departments of transportation to choose an appropriate prediction technique for the ITS applications. That is, a user must be able to utilize the disseminated information effectively by the forecasting models. This paper provides a detailed survey of the latest forecasting technologies and contributes to understand the key concept behind the prediction approaches. To provide guidelines to the decision-maker, this paper reviews multifaceted techniques developed by various authors for traffic prediction. We start classifying each technique into four categories namely, Machine Learning (ML), Computational Intelligence (CI), Deep Learning (DL), and hybrid algorithms. Many have conducted survey using model-driven or data-driven methods. We are the first to explore the area of traffic prediction based on the advances in multifaceted techniques proposing algorithmic approaches for key traffic characteristics in the forecasting process. The role of dependent factors in the prediction are analyzed thoroughly. We have analyzed each algorithm chronologically based on various traffic traits. The approaches are summarized based on the rational usage and performance of each technique. The analysis led to several research queries, and the appropriate  responses are provided based on our detail survey. Finally, it is confirmed that currently, CI-MLs and DL hybrid techniques outperforms the rest in the field of traffic prediction. Ultimately suggested open challenges and future direction to explore the capability of DL and hybrid techniques further in the field of traffic prediction. 

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References

  1. Lin, Y., Wang, P., & Ma, M. (2017). Intelligent transportation system (ITS): Concept, challenge and opportunity. In 2017 IEEE 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and IEEE international conference on intelligent data and security (IDS) (pp. 167–172). IEEE.

  2. Joseph, A. D., Beresford, A. R., Bacon, J., Cottingham, D. N., Davies, J. J., Jones, B. D., et al. (2006). Intelligent transportation systems. IEEE Pervasive Computing, 5(4), 63–67.

    Google Scholar 

  3. Blythe, P., Rackliff, T., Holland, R., & Mageean, J. (2000). ITS applications in public transport: Improving the service to the transport system. Journal of Advanced Transportation, 34(3), 325–345.

    Google Scholar 

  4. Moral-Muñoz, J. A., Cobo, M. J., Chiclana, F., Collop, A., & Herrera-Viedma, E. (2015). Analyzing highly cited papers inintelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 17(4), 993–1001.

    Google Scholar 

  5. Dimitrakopoulos, G., & Demestichas, P. (2010). Intelligent transportation systems. IEEE Vehicular Technology Magazine, 5(1), 77–84.

    Google Scholar 

  6. Figueiredo, L., Jesus, I., Machado, J. T., Ferreira, J. R., & De Carvalho, J. M. (2001). Towards the development of intelligent transportation systems. In ITSC 2001. 2001 IEEE intelligent transportation systems. Proceedings (Cat. No. 01TH8585) (pp. 1206–1211). IEEE.

  7. Zhai, H., & Fang, Y. (2005). Medium access control protocols in mobile ad hoc networks: Problems and solutions. In Handbook on theoretical and algorithmic aspects of sensor, ad hoc wireless and peer-to-peer networks (pp. 231–250). CRC Press.

  8. Claudel, C. G., Chamoin, T., & Bayen, A. M. (2013). Solutions to estimation problems for scalar Hamilton-Jacobi equations using linear programming. IEEE Transactions on Control Systems Technology, 22(1), 273–280.

    Google Scholar 

  9. Taleb, T., Sakhaee, E., Jamalipour, A., Hashimoto, K., Kato, N., & Nemoto, Y. (2007). A stable routing protocol to support ITS services in VANET networks. IEEE Transactions on Vehicular Technology, 56(6), 3337–3347.

    Google Scholar 

  10. Yin, H., Wong, S., Xu, J., & Wong, C. K. (2002). Urban traffic flow prediction using a fuzzy-neural approach. Transportation Research Part C: Emerging Technologies, 10(2), 85–98.

    Google Scholar 

  11. Hamed, M. M., Al-Masaeid, H. R., & Said, Z. M. B. (1995). Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering, 121(3), 249–254.

    Google Scholar 

  12. Ozkurt, C., & Camci, F. (2009). Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Mathematical and Computational Applications, 14(3), 187–196.

    Google Scholar 

  13. Wang, J., Gu, Q., Wu, J., Liu, G., & Xiong, Z. (2016). Traffic speed prediction and congestion source exploration: A deep learning method. In 2016 IEEE 16th international conference on data mining (ICDM) (pp. 499–508). IEEE.

  14. Stathopoulos, A., & Karlaftis, M. G. (2003). A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C: Emerging Technologies, 11(2), 121–135.

    Google Scholar 

  15. Verhoef, E. T. (1999). Time, speeds, flows and densities in static models of road traffic congestion and congestion pricing. Regional Science and Urban Economics, 29(3), 341–369.

    Google Scholar 

  16. Sayed, T., Abdelwahab, W., & Navin, F. (1995). Identifying accident-prone locations using fuzzy pattern recognition. Journal of Transportation Engineering, 121(4), 352–358.

    Google Scholar 

  17. Dıaz, E. M. (2002). Theory of planned behavior and pedestrians’ intentions to violate traffic regulations. Transportation Research Part F: Traffic Psychology and Behaviour, 5(3), 169–175.

    Google Scholar 

  18. Nanthawichit, C., Nakatsuji, T., & Suzuki, H. (2003). Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway. Transportation Research Record, 1855(1), 49–59.

    Google Scholar 

  19. Yasdi, R. (1999). Prediction of road traffic using a neural network approach. Neural Computing and Applications, 8(2), 135–142.

    Google Scholar 

  20. Zhou, H., Wei, W., Shimada, K., Mabu, S., & Hirasawa, K. (2008). Time related association rules mining with attributes accumulation mechanism and its application to traffic prediction. In 2008 IEEE congress on evolutionary computation (IEEE World congress on computational intelligence) (pp. 305–11). IEEE.

  21. Khotanzad, A., & Sadek, N. (2003). Multi-scale high-speed network traffic prediction using combination of neural networks. In Proceedings of the sd, 2003. (Vol. 2, pp. 1071–1075). IEEE.

  22. Chabaa, S., Zeroual, A., & Antari, J. (2010). Identification and prediction of internet traffic using artificial neural networks. Journal of Intelligent Learning Systems and Applications, 2(03), 147.

    Google Scholar 

  23. Xiao, H., Sun, H., Ran, B., & Oh, Y. (2003). Fuzzy-neural network traffic prediction framework with wavelet Decomposition. Transportation Research Record, 1836(1), 16–20.

    Google Scholar 

  24. Chen, Y., Yang, B., & Meng, Q. (2012). Small-time scale network traffic prediction based on flexible neural tree. Applied Soft Computing, 12(1), 274–279.

    Google Scholar 

  25. Tatomir, B., & Rothkrantz, L. (2006). Hierarchical routing in traffic using swarm-intelligence. In 2006 IEEE intelligent transportation systems conference (pp. 230–235). IEEE.

  26. Dasgupta, D., Yu, S., & Nino, F. (2011). Recent advances in artificial immune systems: Models and applications. Applied Soft Computing, 11(2), 1574–1587.

    Google Scholar 

  27. Lu, B., & Huang, M. (2009). Traffic flow prediction based on wavelet analysis, genetic algorithm and artificial neural network. In 2009 International conference on information engineering and computer science (pp. 1–4). IEEE.

  28. Chan, K. Y., Dillon, T. S., Singh, J., & Chang, E. (2011). Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm. IEEE Transactions on Intelligent Transportation Systems, 13(2), 644–654.

    Google Scholar 

  29. Ma, Y., Chowdhury, M., Sadek, A., & Jeihani, M. (2012). Integrated traffic and communication performance evaluation of an intelligent vehicle infrastructure integration (VII) system for online travel-time prediction. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1369–1382.

    Google Scholar 

  30. Xu, D. W., Wang, Y. D., Jia, L. M., Qin, Y., & Dong, H. H. (2017). Real-time road traffic state prediction based on ARIMA and Kalman filter. Frontiers of Information Technology & Electronic Engineering, 18(2), 287–302.

    Google Scholar 

  31. Cao, Y., He, H., & Man, H. (2012). SOMKE: Kernel density estimation over data streams by sequences of self-organizing maps. IEEE Transactions on Neural Networks and Learning Systems, 23(8), 1254–1268.

    Google Scholar 

  32. Borkar, P., & Malik, L. G. (2013). Acoustic Signal based Traffic Density State Estimation using SVM. International Journal of Image, Graphics & Signal Processing, 5(8), 37–44.

  33. Qian, R., Yue, Y., Coenen, F., & Zhang, B. (2016). Traffic sign recognition with convolutional neural network based on max pooling positions. In 2016 12th International conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD) (pp. 578–582). IEEE.

  34. Rzeszótko, J., & Nguyen, S. H. (2012). Machine learning for traffic prediction. Fundamenta Informaticae, 119(3–4), 407–420.

    MathSciNet  Google Scholar 

  35. Ayodele, T. O. (2010). Types of machine learning algorithms. In New advances in machine learning. IntechOpen.

  36. Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 5(4), 276–281.

    Google Scholar 

  37. Alarcon-Aquino, V., & Barria, J. A. (2006). Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(2), 208–220.

    Google Scholar 

  38. Castro-Neto, M., Jeong, Y. S., Jeong, M. K., & Han, L. D. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36(3), 6164–6173.

    Google Scholar 

  39. Dia, H. (2001). An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research, 131(2), 253–261.

    MATH  Google Scholar 

  40. Zhang, J., Chen, C., Xiang, Y., Zhou, W., & Xiang, Y. (2012). Internet traffic classification by aggregating correlated naive bayes predictions. IEEE Transactions on Information Forensics and Security, 8(1), 5–15.

    Google Scholar 

  41. Williams, N., Zander, S., & Armitage, G. (2006). A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Computer Communication Review, 36(5), 5–16.

    Google Scholar 

  42. Smith, B. L., & Demetsky, M. J. (1994). Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches. In Proceedings of IEEE international conference on systems, man and cybernetics (Vol. 2, pp. 1706–1709). IEEE.

  43. Zhong, M., Sharma, S., & Lingras, P. (2005). Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models. Journal of Computing in Civil Engineering, 19(1), 94–103.

    Google Scholar 

  44. Zhang, L., Liu, Q., Yang, W., Wei, N., & Dong, D. (2013). An improved k-nearest neighbor model for short-term traffic flow prediction. Procedia-Social and Behavioral Sciences, 96, 653–662.

    Google Scholar 

  45. Yu, B., Song, X., Guan, F., Yang, Z., & Yao, B. (2016). k-Nearest neighbor model for multiple-time-step prediction of short-term traffic condition. Journal of Transportation Engineering, 142(6), 04016018.

    Google Scholar 

  46. Auld, T., Moore, A. W., & Gull, S. F. (2007). Bayesian neural networks for internet traffic classification. IEEE Transactions on Neural Networks, 18(1), 223–239.

    Google Scholar 

  47. Castillo, E., Menéndez, J. M., & Sánchez-Cambronero, S. (2008). Traffic estimation and optimal counting location without path enumeration using Bayesian networks. Computer-Aided Civil and Infrastructure Engineering, 23(3), 189–207.

    Google Scholar 

  48. Farid, D. M., Zhang, L., Rahman, C. M., Hossain, M. A., & Strachan, R. (2014). Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Systems with Applications, 41(4), 1937–1946.

    Google Scholar 

  49. Mirchandani, P., & Head, L. (2001). A real-time traffic signal control system: Architecture, algorithms, and analysis. Transportation Research Part C: Emerging Technologies, 9(6), 415–432.

    Google Scholar 

  50. Singh, K. P., Gupta, S., & Rai, P. (2013). Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmospheric Environment, 80, 426–437.

    Google Scholar 

  51. Nath, R. P. D., Lee, H. J., Chowdhury, N. K., & Chang, J. W. (2010). Modified K-means clustering for travel time prediction based on historical traffic data. In International conference on knowledge-based and intelligent information and engineering systems (pp. 511–521). Springer, Berlin.

  52. Sharma, B., Katiyar, V. K., & Gupta, A. K. (2014). Fuzzy logic model for the prediction of traffic volume in week days. International Journal of Computer Applications, 107(17), 1–6.

    Google Scholar 

  53. Ba, A. N. N., Pogoutse, A., Provart, N., & Moses, A. M. (2009). NLStradamus: A simple Hidden Markov Model for nuclear localization signal prediction. BMC Bioinformatics, 10(1), 202.

    Google Scholar 

  54. Mermoud, G., Vasseur, J. P., & Dasgupta, S. (2014). U.S. patent application No. 13/955,648.

  55. Yi, H., Jung, H., & Bae, S. (2017). Deep Neural Networks for traffic flow prediction. In 2017 IEEE international conference on big data and smart computing (BigComp) (pp. 328–331). IEEE.

  56. Lin, S. Y. (2015). Reinforcement learning-based prediction approach for distributed dynamic data-driven application systems. Information Technology and Management, 16(4), 313–326.

    Google Scholar 

  57. Muda, Z., Yassin, W., Sulaiman, M. N., & Udzir, N. I. (2011). Intrusion detection based on K-Means clustering and Naïve Bayes classification. In 2011 7th international conference on information technology in Asia (pp. 1–6). IEEE.

  58. Tang, J., Liu, F., Zou, Y., Zhang, W., & Wang, Y. (2017). An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2340–2350.

    Google Scholar 

  59. Tyagi, V., Kalyanaraman, S., & Krishnapuram, R. (2012). Vehicular traffic density state estimation based on cumulative road acoustics. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1156–1166.

    Google Scholar 

  60. Abdi, J., Moshiri, B., Abdulhai, B., & Sedigh, A. K. (2012). Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm. Engineering Applications of Artificial Intelligence, 25(5), 1022–1042.

    Google Scholar 

  61. George, S., Santra, A. K. (2020). Deep learning techniques for traffic flow prediction in intelligent transportation system: A survey. Test Engineering Management, 82, Jan/Feb, 9773–9789.

  62. Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2014). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873.

    Google Scholar 

  63. Huang, W., Song, G., Hong, H., & Xie, K. (2014). Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems, 15(5), 2191–2201.

    Google Scholar 

  64. Quek, C., Pasquier, M., & Lim, B. B. S. (2006). POP-TRAFFIC: A novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transactions on Intelligent Transportation Systems, 7(2), 133–146.

    Google Scholar 

  65. Chan, K. Y., Dillon, T. S., & Chang, E. (2012). An intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems. IEEE Transactions on Industrial Electronics, 60(10), 4714–4725.

    Google Scholar 

  66. Hu, W., Yan, L., Liu, K., & Wang, H. (2016). A short-term traffic flow forecasting method based on the hybrid PSO-SVR. Neural Processing Letters, 43(1), 155–172.

    Google Scholar 

  67. Li, M. W., Hong, W. C., & Kang, H. G. (2013). Urban traffic flow forecasting using Gauss–SVR with cat mapping, cloud model and PSO hybrid algorithm. Neurocomputing, 99, 230–240.

    Google Scholar 

  68. 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.

    Google Scholar 

  69. Duan, P., Mao, G., Liang, W., & Zhang, D. (2018). A Unified spatio-temporal model for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3212–3223.

  70. Miao, Y., & Wei, L. (2018). Urban road network traffic volume prediction based on road section speed. In 2018 Prognostics and system health management conference (PHM-Chongqing) (pp. 1320–1324). IEEE.

  71. Chu, K. C., Saigal, R., & Saitou, K. (2018). Real-time traffic prediction and probing strategy for Lagrangian traffic data. IEEE Transactions on Intelligent Transportation Systems, 20(2), 497–506.

    Google Scholar 

  72. Tang, M., Li, Z., & Tian, G. (2019). A data-driven-based wavelet support vector approach for passenger flow forecasting of the metropolitan hub. IEEE Access, 7, 7176–7183.

    Google Scholar 

  73. Hou, Q., Leng, J., Ma, G., Liu, W., & Cheng, Y. (2019). An adaptive hybrid model for short-term urban traffic flow prediction. Physica A: Statistical Mechanics and its Applications, 527, 121065.

    Google Scholar 

  74. Mu, R., & Yamamoto, T. (2019). Analysis of traffic flow with micro-cars with respect to safety and environmental impact. Transportation Research Part A: Policy and Practice, 124, 217–241.

    Google Scholar 

  75. Sun, J., Liu, H., & Ma, Z. (2019). Modelling and simulation of highly mixed traffic flow on two-lane two-way urban streets. Simulation Modelling Practice and Theory, 95, 16–35.

    Google Scholar 

  76. Ghiasi, A., Li, X., & Ma, J. (2019). A mixed traffic speed harmonization model with connected autonomous vehicles. Transportation Research Part C: Emerging Technologies, 104, 210–233.

    Google Scholar 

  77. Yu, R., Zheng, Y., Abdel-Aty, M., & Gao, Z. (2019). Exploring crash mechanisms with microscopic traffic flow variables: A hybrid approach with latent class logit and path analysis models. Accident Analysis and Prevention, 125, 70–78.

    Google Scholar 

  78. Cheng, A., Jiang, X., Li, Y., Zhang, C., & Zhu, H. (2017). Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A: Statistical Mechanics and its Applications, 466, 422–434.

    Google Scholar 

  79. Guo, J., Xie, Z., Qin, Y., Jia, L., & Wang, Y. (2019). Short-term abnormal passenger flow prediction based on the fusion of SVR and LSTM. IEEE Access, 7, 42946–42955.

    Google Scholar 

  80. Guo, J., Liu, Z., Huang, W., Wei, Y., & Cao, J. (2017). Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals. IET Intelligent Transport Systems, 12(2), 143–150.

    Google Scholar 

  81. Ryu, U., Wang, J., Kim, T., Kwak, S., & Juhyok, U. (2018). Construction of traffic state vector using mutual information for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 96, 55–71.

    Google Scholar 

  82. Djenouri, Y., Belhadi, A., Lin, J. C. W., & Cano, A. (2019). Adapted K-nearest neighbors for detecting anomalies on spatio-temporal traffic flow. IEEE Access, 7, 10015–10027.

    Google Scholar 

  83. Yang, H. F., Dillon, T. S., & Chen, Y. P. P. (2016). Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2371–2381.

    Google Scholar 

  84. Zhang, L., Alharbe, N. R., Luo, G., Yao, Z., & Li, Y. (2018). A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction. Tsinghua Science and Technology, 23(4), 479–492.

    Google Scholar 

  85. Zhang, Y., & Huang, G. (2018). Traffic flow prediction model based on deep belief network and genetic algorithm. IET Intelligent Transport Systems, 12(6), 533–541.

    Google Scholar 

  86. Yang, H. F., Dillon, T. S., Chang, E., & Chen, Y. P. (2018). Optimized configuration of exponential smoothing and extreme learning machine for traffic flow forecasting. IEEE Transactions on Industrial Informatics, 15(1), 23–34.

    Google Scholar 

  87. 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.

  88. Zang, D., Fang, Y., Wei, Z., Tang, K., & Cheng, J. (2019). Traffic flow data prediction using residual deconvolution based deep generative network. IEEE Access, 7, 71311–71322.

  89. Deng, S., Jia, S., & Chen, J. (2019). Exploring spatial–temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data. Applied Soft Computing, 78, 712–721.

    Google Scholar 

  90. Xiao, J., Xiao, Z., Wang, D., Bai, J., Havyarimana, V., & Zeng, F. (2019). Short-term traffic volume prediction by ensemble learning in concept drifting environments. Knowledge-Based Systems, 164, 213–225.

    Google Scholar 

  91. Qu, L., Li, W., Li, W., Ma, D., & Wang, Y. (2019). Daily long-term traffic flow forecasting based on a deep neural network. Expert Systems with Applications, 121, 304–312.

    Google Scholar 

  92. Duan, Z., Yang, Y., Zhang, K., Ni, Y., & Bajgain, S. (2018). Improved deep hybrid networks for urban traffic flow prediction using trajectory data. IEEE Access, 6, 31820–31827.

    Google Scholar 

  93. Ma, D., Sheng, B., Jin, S., Ma, X., & Gao, P. (2018). Short-term traffic flow forecasting by selecting appropriate predictions based on pattern matching. IEEE Access, 6, 75629–75638.

    Google Scholar 

  94. Zhan, H., Gomes, G., Li, X. S., Madduri, K., Sim, A., & Wu, K. (2018). Consensus ensemble system for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 19(12), 3903–3914.

    Google Scholar 

  95. Tian, Y., Zhang, K., Li, J., Lin, X., & Yang, B. (2018). LSTM-based traffic flow prediction with missing data. Neurocomputing, 318, 297–305.

    Google Scholar 

  96. 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.

    Google Scholar 

  97. Diao, Z., Zhang, D., Wang, X., Xie, K., He, S., Lu, X., et al. (2018). A hybrid model for short-term traffic volume prediction in massive transportation systems. IEEE Transactions on Intelligent Transportation Systems, 20(3), 935–946.

    Google Scholar 

  98. Lin, Y., Dai, X., Li, L., & Wang, F. Y. (2018). Pattern sensitive prediction of traffic flow based on generative adversarial framework. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2395–2400.

    Google Scholar 

  99. Du, B., Peng, H., Wang, S., Bhuiyan, M. Z. A., Wang, L., Gong, Q., Liu, L., & Li, J. (2019). Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Transactions on Intelligent Transportation Systems, 21(3), 972–985.

  100. Guo, S., Lin, Y., Li, S., Chen, Z., & Wan, H. (2019). Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Transactions on Intelligent Transportation Systems, 20, 3913–3926.

    Google Scholar 

  101. Zheng, Z., Yang, Y., Liu, J., Dai, H. N., & Zhang, Y. (2019). Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Transactions on Intelligent Transportation Systems, 20, 3927–3939.

    Google Scholar 

  102. Yang, B., Sun, S., Li, J., Lin, X., & Tian, Y. (2019). Traffic flow prediction using LSTM with feature enhancement. Neurocomputing, 332, 320–327.

    Google Scholar 

  103. Lin, Y., Zhang, J. W., & Liu, H. (2019). Deep learning based short-term air traffic flow prediction considering temporal–spatial correlation. Aerospace Science and Technology, 93, 105113.

    Google Scholar 

  104. Kong, F., Li, J., Jiang, B., & Song, H. (2019). Short-term traffic flow prediction in smart multimedia system for internet of vehicles based on deep belief network. Future Generation Computer Systems, 93, 460–472.

    Google Scholar 

  105. Zhao, L., Zhou, Y., Lu, H., & Fujita, H. (2019). Parallel computing method of deep belief networks and its application to traffic flow prediction. Knowledge-Based Systems, 163, 972–987.

    Google Scholar 

  106. Tang, J., Chen, X., Hu, Z., Zong, F., Han, C., & Li, L. (2019). Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A: Statistical Mechanics and its Applications, 534, 120642.

    Google Scholar 

  107. Liao, J., Tang, J., Zeng, W., & Zhao, X. (2018). Efficient and accurate traffic flow prediction via incremental tensor completion. IEEE Access, 6, 36897–36905.

    Google Scholar 

  108. Feng, X., Ling, X., Zheng, H., Chen, Z., & Xu, Y. (2018). Adaptive multi-kernel SVM with spatial-temporal correlation for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2001–2013.

    Google Scholar 

  109. Li, L., Qin, L., Qu, X., Zhang, J., Wang, Y., & Ran, B. (2019). Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowledge-Based Systems, 172, 1–14.

    Google Scholar 

  110. Chen, W., An, J., Li, R., Fu, L., Xie, G., Bhuiyan, M. Z. A., et al. (2018). A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features. Future Generation Computer Systems, 89, 78–88.

    Google Scholar 

  111. 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.

    Google Scholar 

  112. Chen, W., An, J., Li, R., & Xie, G. (2019). Tensor-train fuzzy deep computation model for citywide traffic flow prediction. IEEE Access, 7, 120581–120593.

    Google Scholar 

  113. Zhan, X., Zheng, Y., Yi, X., & Ukkusuri, S. V. (2016). Citywide traffic volume estimation using trajectory data. IEEE Transactions on Knowledge and Data Engineering, 29(2), 272–285.

    Google Scholar 

  114. Chen, C., Liu, X., Qiu, T., & Sangaiah, A. K. (2017). A short-term traffic prediction model in the vehicular cyber–physical systems. Future Generation Computer Systems, 105, 894–903.

    Google Scholar 

  115. Rajabzadeh, Y., Rezaie, A. H., & Amindavar, H. (2017). Short-term traffic flow prediction using time-varying Vasicek model. Transportation Research Part C: Emerging Technologies, 74, 168–181.

    Google Scholar 

  116. Liebig, T., Piatkowski, N., Bockermann, C., & Morik, K. (2017). Dynamic route planning with real-time traffic predictions. Information Systems, 64, 258–265.

    Google Scholar 

  117. Louati, A., Darmoul, S., Elkosantini, S., & ben Said, L. (2018). An artificial immune network to control interrupted flow at a signalized intersection. Information Sciences, 433, 70–95.

    MathSciNet  MATH  Google Scholar 

  118. Chung, J., & Sohn, K. (2017). Image-based learning to measure traffic density using a deep convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1670–1675.

    Google Scholar 

  119. Qureshi, K. N., Abdullah, A. H., & Altameem, A. (2017). Road aware geographical routing protocol coupled with distance, direction and traffic density metrics for urban vehicular ad hoc networks. Wireless Personal Communications, 92(3), 1251–1270.

    Google Scholar 

  120. Zhang, F., Du, Y., Liu, W., & Li, P. (2018). Model predictive power control for cooperative vehicle safety systems. IEEE Access, 6, 4797–4810.

    Google Scholar 

  121. Tayara, H., Soo, K. G., & Chong, K. T. (2017). Vehicle det ection and counting in high-resolution aerial images using convolutional regression neural network. IEEE Access, 6, 2220–2230.

    Google Scholar 

  122. Hu, H., Gao, Z., Sheng, Y., Zhang, C., & Zheng, R. (2019). Traffic density recognition based on image global texture feature. International Journal of Intelligent Transportation Systems Research, 17, 1–10.

    Google Scholar 

  123. Rempe, F., Franeck, P., Fastenrath, U., & Bogenberger, K. (2017). A phase-based smoothing method for accurate traffic speed estimation with floating car data. Transportation Research Part C: Emerging Technologies, 85, 644–663.

    Google Scholar 

  124. Bae, B., Kim, H., Lim, H., Liu, Y., Han, L. D., & Freeze, P. B. (2018). Missing data imputation for traffic flow speed using spatio-temporal cokriging. Transportation Research Part C: Emerging Technologies, 88, 124–139.

    Google Scholar 

  125. Liu, S., Zhang, W., Wu, X., Feng, S., Pei, X., & Yao, D. (2018). A simulation system and speed guidance algorithms for intersection traffic control using connected vehicle technology. Tsinghua Science and Technology, 24(2), 160–170.

    Google Scholar 

  126. Gao, Z., Yang, X., Zhang, J., Lu, H., Xu, R., & Diao, W. (2019). Redundancy-reducing and holiday speed prediction based on highway traffic speed data. IEEE Access., 4(7), 31535–31546.

    Google Scholar 

  127. Jiang, B., & Fei, Y. (2016). Vehicle speed prediction by two-level data driven models in vehicular networks. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1793–1801.

    Google Scholar 

  128. Le, T. V., Oentaryo, R., Liu, S., & Lau, H. C. (2016). Local Gaussian processes for efficient fine-grained traffic speed prediction. IEEE Transactions on Big Data, 3(2), 194–207.

    Google Scholar 

  129. Yao, B., Chen, C., Cao, Q., Jin, L., Zhang, M., Zhu, H., et al. (2017). Short-term traffic speed prediction for an urban corridor. Computer-Aided Civil and Infrastructure Engineering, 32(2), 154–169.

    Google Scholar 

  130. Li, Y., Chen, M., Lu, X., & Zhao, W. (2018). Research on optimized GA-SVM vehicle speed prediction model based on driver-vehicle-road-traffic system. Science China Technological Sciences, 61(5), 782–790.

    Google Scholar 

  131. Niu, K., Zhang, H., Zhou, T., Cheng, C., & Wang, C. (2019). A novel spatio-temporal model for city-scale traffic speed prediction. IEEE Access, 7, 30050–30057.

    Google Scholar 

  132. Zhao, J., Gao, Y., Yang, Z., Li, J., Feng, Y., Qin, Z., et al. (2019). truck traffic speed prediction under non-recurrent congestion: based on optimized deep learning algorithms and GPS data. IEEE Access, 7, 9116–9127.

    Google Scholar 

  133. Zhu, M., Hassanalieragh, M., Chen, Z., Fahad, A., Shen, K., & Soyata, T. (2018). Energy-aware sensing in data-intensive field systems using supercapacitor energy buffer. IEEE Sensors Journal, 18(8), 3372–3383.

    Google Scholar 

  134. Chavhan, S., & Venkataram, P. (2018). Commuters’ traffic pattern and prediction analysis in a metropolitan area. Journal on Vehicle Routing Algorithms, 1(1), 33–46.

    Google Scholar 

  135. Murça, M. C. R., & Hansman, R. J. (2018). Identification, characterization, and prediction of traffic flow patterns in multi-airport systems. IEEE Transactions on Intelligent Transportation Systems, 20(5), 1683–1696.

    Google Scholar 

  136. 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.

    Google Scholar 

  137. Liu, Q., Cai, Y., Jiang, H., Lu, J., & Chen, L. (2018). Traffic state prediction using ISOMAP manifold learning. Physica A: Statistical Mechanics and its Applications, 506, 532–541.

    Google Scholar 

  138. Chen, X., Zhang, S., Li, L., & Li, L. (2018). Adaptive rolling smoothing with heterogeneous data for traffic state estimation and prediction. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1247–1258.

    Google Scholar 

  139. Su, Z., Liu, Q., Lu, J., Cai, Y., Jiang, H., & Wahab, L. (2018). Short-time traffic state forecasting using adaptive neighborhood selection based on expansion strategy. IEEE Access, 6, 48210–48223.

    Google Scholar 

  140. Yu, H., Ji, N., Ren, Y., & Yang, C. (2019). A special event-based k-nearest neighbor model for short-term traffic state prediction. IEEE Access, 7, 81717–81729.

    Google Scholar 

  141. Chen, M., Yu, X., & Liu, Y. (2018). PCNN: Deep convolutional networks for short-term traffic congestion prediction. IEEE Transactions on Intelligent Transportation Systems, 19(11), 3550–3559.

    Google Scholar 

  142. Tseng, F. H., Hsueh, J. H., Tseng, C. W., Yang, Y. T., Chao, H. C., & Chou, L. D. (2018). Congestion prediction with big data for real-time highway traffic. IEEE Access, 6, 57311–57323.

    Google Scholar 

  143. Wen, F., Zhang, G., Sun, L., Wang, X., & Xu, X. (2019). A hybrid temporal association rules mining method for traffic congestion prediction. Computers & Industrial Engineering, 130, 779–787.

    Google Scholar 

  144. Xing, Y., Ban, X., Liu, X., & Shen, Q. (2019). Large-scale traffic congestion prediction based on the symmetric extreme learning machine cluster fast learning method. Symmetry, 11(6), 730.

    Google Scholar 

  145. Bouyahia, Z., Haddad, H., Jabeur, N., & Yasar, A. (2019). A two-stage road traffic congestion prediction and resource dispatching toward a self-organizing traffic control system. Personal and Ubiquitous Computing, 23, 1–12.

    Google Scholar 

  146. Zhang, S., Yao, Y., Hu, J., Zhao, Y., Li, S., & Hu, J. (2019). Deep autoencoder neural networks for short-term traffic congestion prediction of transportation networks. Sensors, 19(10), 2229.

    Google Scholar 

  147. Fan, S. K. S., Su, C. J., Nien, H. T., Tsai, P. F., & Cheng, C. Y. (2018). Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Computing, 22(17), 5707–5718.

    Google Scholar 

  148. Philip, A. M., Ramadurai, G., & Vanajakshi, L. (2018). Urban arterial travel time prediction using support vector regression. Transportation in Developing Economies, 4(1), 7.

    Google Scholar 

  149. Jenelius, E., & Koutsopoulos, H. N. (2017). Urban network travel time prediction based on a probabilistic principal component analysis model of probe data. IEEE Transactions on Intelligent Transportation Systems, 19(2), 436–445.

    Google Scholar 

  150. Chen, C. H. (2018). An arrival time prediction method for bus system. IEEE Internet of Things Journal, 5(5), 4231–4232.

    Google Scholar 

  151. Ran, X., Shan, Z., Fang, Y., & Lin, C. (2018). Travel time prediction by providing constraints on a convolutional neural network. IEEE Access, 6, 59336–59349.

    Google Scholar 

  152. Petersen, N. C., Rodrigues, F., & Pereira, F. C. (2019). Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Systems with Applications, 120, 426–435.

    Google Scholar 

  153. Yang, S. R., Su, Y. J., Chang, Y. Y., & Hung, H. N. (2019). Short-term traffic prediction for edge computing-enhanced autonomous and connected cars. IEEE Transactions on Vehicular Technology, 68(4), 3140–3153.

    Google Scholar 

  154. Yang, K., Yu, R., Wang, X., Quddus, M., & Xue, L. (2018). How to determine an optimal threshold to classify real-time crash-prone traffic conditions? Accident Analysis and Prevention, 117, 250–261.

    Google Scholar 

  155. Zhang, Z., He, Q., Gao, J., & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C: Emerging Technologies, 86, 580–596.

    Google Scholar 

  156. Wang, X., Jiang, R., Li, L., Lin, Y., Zheng, X., & Wang, F. Y. (2017). Capturing car-following behaviors by deep learning. IEEE Transactions on Intelligent Transportation Systems, 19(3), 910–920.

    Google Scholar 

  157. Vij, D., & Aggarwal, N. (2018). Smartphone based traffic state detection using acoustic analysis and crowdsourcing. Applied Acoustics, 138, 80–91.

    Google Scholar 

  158. George, S., & Santra A. K., (2020) An improved long short‐term memory networks with Takagi‐Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation. Computational Intelligence. https://doi.org/10.1111/coin.12291.

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  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Shiju George & Ajit Kumar Santra

  2. Amal Jyothi College of Engineering, Kottayam, Kerala, India

    Shiju George

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George, S., Santra, A.K. Traffic Prediction Using Multifaceted Techniques: A Survey. Wireless Pers Commun 115, 1047–1106 (2020). https://doi.org/10.1007/s11277-020-07612-8

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