The traffic flow forecasting is a very important aspect of traffic predication and congestion, it alleviates the increasing congestion problems that cause drivers to save a longer traveling time and economical loses, thus, it is one of the severe problems in big city areas. In tunnels the forecasting may help scheduling the ventilation fans. This way, the cost might be decreased while the air quality increased. The aspect of traffic prediction is that it may give the drivers to plan their travelling time and traveling the path, as they have the predictive data information. In this paper, the survey of different types used for traffic forecasting, the data for these techniques, the output provided by them and analysis insights is provided.

Traffic flow, traffic predication, information, forecasting

[1] Fricker, J. D., and S. K. Saha. Traffic Volume Forecasting Methods for Rural State Highways publication FHWA/IN/JHRP-86/20.Joint Highway Research Project, Indiana Department of Transportation and Purdue University, West Lafayette, Indiana, 1986.
[2] M. M. Hamed, H. R. Al-Masaeid, and Z. M. B. Said, “Short-term prediction of traffic volume in urban areas,” J. Transp. Eng. J. Transp. Eng., vol. 121, no. 3, pp. 249–254, 1995.
[3] D. Reinke, “Urban travel demand forecasting,” Transp. Res. Circular, vol. E-168, pp. 86–92, Nov. 2012.
[4] L. Li, X. Su, Y. Zhang, Y. Lin, and Z. Li, “Trend modeling for traffic time series analysis: An integrated study,” IEEE Trans. Its., vol. 16, no. 6, pp. 3430–3439, Dec. 2015.
[5] Mathieu Ntakiyemunga and Hang Zhang. Traffic volume forcasting model using elasticity method and exponential smooth model for national road of Rwanda Asian Journal of Applied Sciences (ISSN: 2321 – 0893) Masters Candidate at Wuhan University of Technology.
[6] J. Chiou, Y. Zhang, W. Chen, and C. Chang, “A functional data approach to missing value imputation and outlier detection for traffic flow data,” Transp. B, Transp. Dyn., vol. 2, no. 2, pp. 106–129, Feb. 2014.
[7] Yan-hong Tang* and Bao Xi “Dynamic forecasting of traffic volume based on Quantificational dynamics: A nearness perspective School of Management, Harbin Institute of Technology, 150001, Harbin, China. Accepted 21 January, 2010.
[8] Yuanchang Xie, Kaiguang Zhao, Ying Sun, and Dawei Chen,” Gaussian Processes for Short-Term Traffic Volume Forecasting”2010.
[9] Smith BL, Demetsky MJ (1994). Short-term traffic flow prediction: Neural network approach. Transport. Res. Rec. (1453): 98-104.
[10] Smith BL, Demetsky MJ (1997). Traffic flow forecasting: Comparison of modeling approaches. J. Transport. Eng. 123(4): 261-266.
[11] Kartikeya Jha1, Nishita Sinha2, Shriniwas S. Arkatkar3,* and Ashoke K. Sarkar4,”A comparative study on application of time series analysis for traffic forecasting in India: prospects and limitations”, CURRENT SCIENCE, VOL. 110, NO. 3, 10 FEBRUARY 2016.
[12] Haibo Chen and Susan Grant-Muller. Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 9 (5): 319 – 336, 2001.
[13] Srinivasa Ravi Chandra Chilakamarri Venkata. SPATIO-TEMPORAL ANALYSES FOR PREDICTION OF TRAFFIC FLOW, SPEED AND OCCUPANCY ON I-4. PhD thesis, University of Central Florida, 2009.
[14] R. Chrobok, J. Wahle, and M. Schreckenberg. Traffic forecast using simulations of large scale networks. In Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE, pages 434 –439, 2001.
[15] S D CLARK, M S Dougherty, and H R KIRBY. The use of neural networks and time series models for Short term traffic forecasting: A comparative study. Pages 151–62, 1993.
[16] F.M. Sander C.P.IJ. Van Hinsbergen, J.W.C. van Lint. Short term traffic prediction models. ITS World Congress, Beijing, China, 2007.
[17] G. A. Davis, N. L. Nihan, M. M. Hamed, and L. N. Jacobson. Adaptive forecasting of freeway traffic congestion. Transportation Research Record, 1287:29–33, 1990.
[18] Gary A. Davis and Nancy L. Nihan. Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering, 117 (2):178–188, 1991.
[19] Iwao Okutani and Yorgos J. Stephanedes. Dynamic prediction of traffic volume through kalman filtering theory.
Transportation Research Part B: Methodological, 18(1):1 – 11, 1984.
[20] K. Lindveld J. Whittaker, S. Garside. Tracking and predicting a network traffic process. International Journal of Forecasting, pages 51–61, 1997.
[21] Guoqiang Yu, Jianming Hu, Changshui Zhang, Like Zhuang, and Jingyan Song. Short-term traffic flow Forecasting based on markov chain model. In Intelligent Vehicles Symposium, 2003. Proceedings. IEEE, pages 208– 212, June 2003.
[22] Kartikeya Jha, Nishita Sinha, Shriniwas Shrikant Arkatkar, Ashoke Kumar Sarkar, Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India - 3330312 ,” modeling growth trend and forecasting techniques for vehicular population in India” Received 4 January 2013.