1 Affiliation : Department of Mathematics Hindustan Institute of Technology & Science Chennai-603 103, India
Email : firstname.lastname@example.org
2 Affiliation : Laboratory of Information processing, Faculty of Science Ben M’Sik, University Hassan II, B.P 7955, Sidi Othman, Casablanca, Morocco
Email : email@example.com
3 Affiliation : Department of Mathematics and Statistics,Faculty of Applied Science and Technology, Universiti Tun Hussein Onn Malaysia, Johar ,Malaysia
Email : firstname.lastname@example.org
Neutrosophic along with its environment development over the past decades. Neutrosophic environment is apply to various applications in logic,statstics,albebra, neural networks and several other fields. Neutrosophic sets has been presented to handle the indeterminacy in real-world decision-making problem. Real world problems have some kind of uncertainty in nature and one of the influential problem in environment. Neutrosophic environment results are apply to a new dimension in traffic control. Neutrosophic is the vital role on traffic flow control . It is deal with membership , non membership and also indeterminacy of the data as well. The advantage of the neutrosophic environment is to find the optimized result of the system choosing the best alternative.In this paper, traffic flow control is analyzed under neutrosophic environment using MATLAB.
Traffic flow , Neutrosophic environment , Neutrosophic network
 R. K. Oswald, B.L. Smith and B.M. Williams, “Parametric and nonparametric traffic volume forecasting,” in Transportation Research Board Annual Meeting, (Washington, DC, 2000).
 Stathopoulos and M.G. Karlaftis “Spectral and cross-spectral analysis of urban traffic flows,” in Intelligent Transportation Systems, (2001 IEEE Proceedings 2001), pp. 820 –825.
 R. Chrobok, J. Wahle and M. Schreckenberg, “Traffic forecast using simulations of large scale networks,” in Intelligent Transportation Systems, IEEE Proceedings pp. 434 –439, 2001
 A. Stathopoulos and M. G. Karlaftis, A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C: Emerging Technologies, 11(2), 121 – 135, 2003.
 H. Wang, F. Smarandache, Y. Q. Zhang, R. Sunderraman, Interval neutrosophic Sets and Logic: Theory and Applications in Computing (Hexis, Phoenix, 2005.
 F. Smarandache, A unifying field in logic. Neutrosophy: Neutrosophic probability, set, logic (American Research Press, Rehoboth, fourth edition, 2005
 Shiliang, Chnagshui and Guoqiang, A Bayesian Network Approach to Traffic Flow Forecasting. IEEE Transactions on Intelligent Transportation Systems 7(1), 2006.
 Chai, Michel, Bernard and Seng, POP-TRAFFIC: A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction. IEEE Transactions on Intelligent Transportation systems 7(2), 2006.
 Li, Lin and Liu, Type-2 fuzzy logic approach for short term traffic forecasting. Intelligent Transportation System, 153(1), 2006.
 L. Chen and P. Chen, “Ensemble learning approach for freeway short-term traffic flow prediction,” in IEEE 2007.
 Tong, Fan, Cui and Meng, “Fuzzy Neural Network model Applied in the Traffic Flow Prediction,” in IEEE 2007.
 M. C. Tan, S. C. Wong, J. M. Xu, Z. R. Guan, and P. Zhang, An Aggregation Approach to Short-Term Traffic Flow Prediction. IEEE Transactions On Intelligent Transportation Systems 10(1) 60-69, 2009.
 W. Ge and Yan, “Research on Traffic Flow Fuzzy Time Series Forecasting Algorithm of Single Intersection,” in Fourth International Conference on Innovative Computing, Information and Control, 2009.
 T. Zhang, L. Hu, Z. Liu, and Y. Zhang, “Nonparametric regression for the short-term traffic flow forecasting,” in 2010 International Conference on Mechanic Automation and Control Engineering (MACE), pp. 2850 –2853, 2010.
 H. Wang, F. Smarandache, Y. Zhang and R. Sunderraman, Single Valued Neutrosophic Sets. Multispace and Multisrtucture 4, 410-413, 2010.
 M. A. Tahaa, L. Ibrahimb, Traffic Simulation System Based on Fuzzy Logic. Procedia Computer Science 12, 356 – 360, 2012.
 J. Alam, M. K. Pandey and H. Ahmed, “Intelligent Traffic Light Control System for Isolated Intersection Using Fuzzy Logic,” Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013), 209-215
 B. Sharma, Gupta, Fuzzy Logic model for the prediction of traffic volume in week days. International Journal of computer Applications 107(17), 2014.
 E. Pasetiyo, O. Wahyunggoro and S. Sulistyo, Design and Simulation of Adaptive Traffic Light Controller Using Fuzzy Logic Control Sugeno Method. International Journal of Scientific and Research Publications 5(4), 2015.
 S. Prontri, P. Wuttidittachotti and S. Thajchayapong, “Traffic signal control using fuzzy logic,” in International conference on, Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology(ECTI-CON), 2015.
 J. Ye, Interval neutrosophic multiple attribute decision-making method with credibility information. International Journal of Fuzzy Systems 18(5), 914–923, 2016.
 S. Broumi, T. Mohamed, A. Bakali and F. Smarandache, Single Valued Neutrosophic Graphs. Journal of New Theory 10, 86-101, 2016.
 Nancy, and G. Harish, An Improved score function for ranking neutrosophic Sets and Its Application to decsion making process. International Journal for Uncertainty Quantification 6 (5), 377–385, 2016.
 M. Alodat and I. Addullah, “Surveillance Rapid Detection of Signs of Traffic Services in Real Time,” 4 th international conference on E-Commerce ICoEC, pp. 51-55,2017.
 S. Banerjee, S. Biswas and T. K. Roy, Intuitionistic Fuzzy Linear System, Advances in Fuzzy Mathematics. 12 (13), 475-487, 2017.
 J. Ye, Neutrosophic linear equations and application in traffic flow problems, Algorithms 10(4), 133, 2017.
 D. Nagarajan, M. Lathamaheswari, R. Sujatha and J. Kavikumar, Edge Detection on DIOM Image using Triangular Norms in Type-2 Fuzzy. International Journal of Advanced Computer Science and Applications 9(11), 462-475, 2018.
 M. Lathamaheswari, D. Nagarajan, A. Udayakumar and J. Kavikumar, Review on Type-2 Fuzzy in Biomedicine. Indian Journal of Public Health Research & Development 9(12), 322-326,2018. DOI Number:10.5958/0976-5506.2018.01855.7.
 S. Broumi, A. Bakali, T. Mohamed, F. Samarandache, L. H. Son, P. K. K. Kumar, New matrix algorithm for minimum spanning tree with undirected interval valued Neutrosophic Graph. Neutrosophic Operational Research 2, 54-69, 2018.
 M. Lathamaheswari, D. Nagarajan, J. Kavikumar and C. Phang. A Review on Type-2 Fuzzy Controller on Control System. Journal of Advanced Research in Dynamical and Control Systems 10(11), 430-435, 2018.
 S. Broumi S, A. Bakali, M. Talea, F. Smarandache, V. Ulucay, M. Sahin, A. Dey, M. Dhar, R. P. Tan, A. Bahnasse and S. Pramanik, Neutrosophic Sets: On overview, Project: New Trends in Neutrosophic Theory and Applications 2, pp.403-434, 2018.
 D. Nagarajan, M. Lathamaheswari, R. Sujatha and J. Kavikumar, A Type- 2 Fuzzy in Image Extraction for DICOM Image. International Journal of Advanced Computer Science and Applications 9(12), 351-362, 2018.
 S. Broumi, K. Ullah, A. Bakali, M. Talea, P. K. Singh, T. Mahmood, F. Samarandache, A. Bahnasse, S. K. Patro and A. D. Oliveira, Novel System and Method for Telephone Network Planing based on Neutrosophic Graph. Global Journal of Computer Science and Technology: E Network, Web & Security 18(2), 1-11, 2018.
 D. Nagarajan, M. Lathamaheswari, S. Broumi and J. Kavikumar, A new perspective on traffic control management using triangular interval type-2 fuzzy sets and interval neutrosophic sets. Operations Research perspectives, Article in Press. https://doi.org/10.1016/j.orp.2019.100099
 D. Nagarajan, T. Tamizhi, M. Lathamaheswari, and J. Kavikumar” Traffic control management using Gauss Jordan method under neutrosophic Environment” AIP Conference Proceedings 2112, 020060 (2019); https://doi.org/10.1063/1.5112245