Open Access Open Access  Restricted Access Subscription Access

AI Calculations for Traffic Stream Expectation

Sowmya B

Abstract


People need traffic stream the board and examination to all the more likely oversee and course their ordinary processes, while transportation directors need it to plan street foundation fix occupations fittingly. To beat this issue, traffic expert has contrived an assortment of time-series speed estimating techniques, by utilizing traditional examination strategies and AI calculation. Traffic the board frameworks significance is to work out the element of traffic stream precisely. The proposed strategy centers around coordinating AI calculations, hereditary qualities, and picture handling to prepare a brain network model. The suggested method incorporates instructing of brain network model on anticipating of traffic stream on hourly premise. The result gives the better data of the traffic measurements.


Full Text:

PDF

References


Dimitrakopoulos, G., & Demestichas,

P. (2010). Intelligent transportation systems. IEEE Vehicular Technology Magazine, 5(1), 77-84.

Zhang, Y., Liu, Y. (2009). Comparison of parametric and nonparametric techniques for non- peak traffic forecasting. World Academy of Science, Engineering and Technology, 51(27), 8-14.

Kumar, S. V., & Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 7(3), 1-9.

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),

-672.

Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques (No. 722).

www.google.com

Liu, Z. (2010). Chaotic time series analysis. Mathematical Problems in Engineering. 1–31, 2010.

Li, R., & Lu, H. (2009, May).

Combined neural network approach for short-term urban freeway traffic flow prediction. In International symposium on neural networks (pp. 1017-1025). Springer, Berlin, Heidelberg..

Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1-17.

Wang, F. Y. (2010). Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 11(3), 630- 638.


Refbacks

  • There are currently no refbacks.