The road traffic causes worst condition and severe side effects. These affects can be reduced in case density of traffic can be predicted in advance. The number of vehicles is growing as the population growth so the traffic management systems are required that handles traffic. Today traffic becomes very big issues in the world that leads to increased accidents and pollution. Towards this aspect intelligent transportation system is worked upon by many researchers. This work analysed a previous work that has been done towards the intelligent transportation system. The merits and demerits of various techniques also highlighted through this approach. Literature survey is presented interactively in the comparative form for best possible approach selection for future enhancement. Parametric comparison includes metrics classification accuracy, error rate , true positive rate , false positive rate and sensitivity.

Intelligent transportation system, traffic, metric

[1] K. Zheng, H. Meng, P. Chatzimisios, L. Lei, and X. Shen, “An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7920–7928, 2015.
[2] W. Min and L. Wynter, “Real-time road traffic prediction with spatio-temporal correlations,” Transp. Res. Part C, vol. 19, no. 4, pp. 606–616, 2011.
[3] A. V. Chernov, M. A. Butakova, V. D. Vereskun, and O. O. Kartashov, “Situation awareness service based on mobile platforms for multilevel intelligent control system in railway transport,” 24th Telecommun. Forum, TELFOR 2016, pp. 1–4, 2017.
[4] W. Guo, D. Brennan, K. Pavkova, and P. T. Blythe, “Department of Industrial and Reformatory Schools : report of the Secretary for the year 1883,” 1885.
[5] I. Kabashkin, “Reliable v2x communications for safety-critical intelligent transport systems,” Proc. - 2017 Adv. Wirel. Opt. Commun. RTUWO 2017, vol. 2017-January, pp. 251–255, 2017.
[6] Z. Kljaiü, E. Briški, H. Vojvodiü, and N. Amin, “Benefits of Utilisation of GPS Error Mitigation Models for Intelligent Transport Systems,” pp. 1121–1125, 2018.
[7] I. Malygin, V. Komashinsky, and V. V. Tsyganov, “International experience and multimodal intelligent transportation system of Russia,” Proc. 2017 10th Int. Conf. Manag. Large-Scale Syst. Dev. MLSD 2017, 2017.
[8] A. Paier, “The end-to-end intelligent transport system (ITS) concept in the context of the european cooperative ITS corridor,” 2015 IEEE MTT-S Int. Conf. Microwaves Intell. Mobility, ICMIM 2015, 2015.
[9] A. Papola, F. Tinessa, V. Marzano, and A. Mautone, “Quantitative overview of efficiency and effectiveness of public transport in Italy: The importance of using ITS,” 5th IEEE Int. Conf. Model. Technol. Intell. Transp. Syst. MT-ITS 2017 - Proc., pp. 895–900, 2017.
[10] Y. A. Seliverstov, I. G. Malygin, V. I. Komashinskiy, A. A. Tarantsev, N. V. Shatalova, and V. A. Petrova, “The St. Petersburg transport system simulation before opening new subway stations,” Proc. 2017 20th IEEE Int. Conf. Soft Comput. Meas. SCM 2017, pp. 284–287, 2017.
[11] K. Siddiqi, A. D. Raza, and S. S. Muhammad, “Visible light communication for V2V intelligent transport system,” Proc. 2016 Int. Conf. Broadband Commun. Next Gener. Networks Multimed. Appl. CoBCom 2016, pp. 2–5, 2016.
[12] X. Wang and S. Yan, “Design and implementation of intelligent public transport system based on GIS,” 2011 Int. Conf. Electr. Inf. Control Eng. ICEICE 2011 - Proc., pp. 4868–4871, 2011.
[13] S. V. Kumar and L. Vanajakshi, “Short-term traffic flow prediction using seasonal ARIMA model with limited input data,” Eur. Transp. Res. Rev., vol. 7, no. 3, pp. 1–9, 2015.
[14] A. D. REZWAN-AL-ISLAM KHAN, BJORN LANDFELDT, “Predicting Travel Times in Dense and Highly Varying Road Traffic Networks using STARIMA Models .,” IEEE, no. February, 2012.
[15] F. Chen, P. Deng, J. Wan, D. Zhang, A. V Vasilakos, and X. Rong, “Data Mining for the Internet of Things : Literature Review and Challenges,” IEEE, vol. 2015, no. i, 2015.
[16] T. Jena and J. R. Mohanty, “GA-Based Customer-Conscious Resource Allocation and Task sScheduling in Multi-cloud Computing,” Arab. J. Sci. Eng., 2017.
[17] F. Guo, R. Krishnan, and J. W. Polak, “Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method,” IEEE ACCESS, pp. 1–6, 2015.
[18] S. D. Thepade, S. Vasaikar, N. Bhavsar, R. More, and A. Bhatkhande, “Vehicle traffic density estimation using bayes, rule, tree family data mining classifiers applied on background subtracted traffic images.,” IEEE ACCESS, pp. 87–92, 2016.
[19] G. Zhu, K. Song, and P. Zhang, “A Travel Time Prediction Method for Urban Road Traffic Sensors Data,” 2015 Int. Conf. Identification, Information, Knowl. Internet Things, pp. 29–32, 2015.
[20] J. He, W. Shen, P. Divakaruni, L. Wynter, and R. Lawrence, “Improving Traffic Prediction with Tweet Semantics,” IEEE ACCESSIEEE ACCESS, pp. 1387–1393, 2016.