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Type :research_report
Subject :QC Physics
Main Author :Nur Hamiza Adenan
Additional Authors :Nor Zila Abd Hamid
Norazman Arbin
Mohd Salmi Noorani
Title :Mathematical modelling based on chaos approach to predict traffic flow.
Place of Production :Tanjong Malim
Publisher :Fakulti Sains dan Matematik
Year of Publication :2018
Corporate Name :Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris
Traffic flow is a continuous phenomenon. The irregular patterns in the traffic flow data show the complexity of the system under a variety of internal and external factors of restrict and influence. As such, local mean prediction method (LMPM) has been employed in order to do prediction. Firstly, traffic flow data has been reconstructed to m-dimensional phase space. Next, the phase space is used to predict traffic flow using chaos approach. The traffic flow data reveals chaotic behaviour through analysis using Cao method. The prediction performance shows that the value of correlation coefficient (CC) is greater than 0.5 for all stations that have been analyzed. The value of CC is good and acceptable due to the complexity of the system. These findings are expected to assist in fulfilling the Road Safety Plan 2014-2020 by the Ministry of Transportation as well as local authorities in traffic management.

References

Adenan, N. H., Hamid, N. Z. A., Mohamed, Z., & Noorani, M. S. M. (2017). A pilot study of river flow prediction in urban area based on phase space reconstruction. In The 24th National Symposium on Mathematical Sciences (SKSM24) (Vol. 1870, p. 040011).

 

Frazier, C., Kockelman, K. M., & Kocklman, K. M. (2004). Chaos Theory and Transportation Systems : An Instructive Example. In 83rd Annual Meeting of the Transportation Research Board (Vol. 1897, pp. 9–17). Washington D.C.

 

Hongsuk, Y., HeeJin, J., & Sanghoon, B. (2017). Deep Neural Networks for traffic flow predictionHongsuk Yi, HeeJin Jung, & Sanghoon Bae. (2017). Deep Neural Networks for traffic flow prediction. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 328–331).

 

Kumar, K., Parida, M., & Katiyar, V. K. (2013). Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network. Procedia - Social and Behavioral Sciences, 104, 755–764.

 

Li, Y., Jiang, X., Zhu, H., He, X., Peeta, S., Zheng, T., & Li, Y. (2016). Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory. Nonlinear Dynamics, 85(1), 179–194.

 

Mahmoudabadi, A., & Andalibi, S. (2014). The Assessment of Applying Chaos Theory for Daily Traffic Estimation. In International Conference on Industrial Engineering and Operations Management (pp. 559–566). Bali, Indonesia.

 

Pascale, A., & Nicoli, M. (2011). Adaptive Bayesian network for traffic flow prediction. In 2011 IEEE Statistical Signal Processing Workshop (SSP) (pp. 177–180). IEEE.

 

Shang, P., Li, X., & Kamae, S. (2005). Chaotic analysis of traffic time series. Chaos, Solitons & Fractals, 25(1), 121–128.

 

Takens, F. (1981). Detecting strange attractor in turbulence. Lectures Note in Mathematics (Vol. 898). New York: Springer-Verlag.

 

Vlad, S. (2010). Investigation of chaotic behavior in Euro-Leu exchange rate. Journal of Applied Computer Science & Mathematics, 8(8), 4–8.

 

Vlahogianni, E. I., Golias, J. C., & Karlaftis, M. G. (2004). Short-term traffic forecasting: Overview of objectives and methods. Transport Reviews, 24(5), 533–557.

 

Wan Mohd Zaim, W. N. A. B., & Abd Hamid, N. Z. (2018). Peramalan Bahan Pencemar Ozon (O3) di Universiti Pendidikan Sultan Idris, Tanjung Malim Perak, Malaysia Mengikut Monsun dengan Menggunakan Pendekatan Kalut. Sains Malaysiana, 46(12), 2523–2528.

 

Xie, J., & Choi, Y.-K. (2017). Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data. International Journal of Distributed Sensor Networks, 13(11), 155014771774500.


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