Traffic flow prediction in urban area using inverse approach of chaos theory

Nur Hamiza Adenan

Abstract

Traffic congestions problem could affect everyday life especially in urban area. In order to solve the issue, an excellent traffic flow prediction needs to be developed for a better traffic management. Hence, this study was conducted in order to predict traffic flow by using the data of total volume of vehicles per hour at two main roads located in urban areas namely Selangor and Kuala Lumpur, Malaysia by using application of chaos theory. Phase space reconstruction was used to determine the chaotic behaviour of the total volume of vehicles per hour data. The reconstruction of phase space involves a single variable of the total volume of vehicles per hour data to m-dimensional phase space. Meanwhile, the inverse approach as well as local linear approximation method was used to develop prediction model of the traffic flow time series data. This study found that (i) the time series data were chaotic behaviour based on the phase space plot and (ii) inverse approach can provide prediction on the traffic flow time series data besides give excellent prediction with the value of correlation coefficient more than 0.7500. Hence, inverse approach of chaos theory can develop to prediction model towards the traffic flow in urban area; thus may help the local authorities to provide good traffic management. ? 2021 by authors, all rights reserved.

References

(2011). Population Distribution and Basic Demographic Characteristic Report 2010, Retrieved from www.scopus.com

Abarbanel, H. D. I. (1996). Analysis of Observed Chaotic Data, Retrieved from www.scopus.com

Adewumi, A., Kagamba, J., & Alochukwu, A. (2016). Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks, Retrieved from www.scopus.com

Ahmad, A. (2020). Pembinaan lengkungan peralihan berbentuk C yang memuaskan data interpolasi hermite G 2. J Sci Math Lett, 8(2), 2600-8718. Retrieved from www.scopus.com

Albostan, A., & Önöz, B. (2015). Implementation of chaotic analysis on river discharge time series. Energy Power Eng, 7(3), 81-92. Retrieved from www.scopus.com

Farmer, J. D., & Sidorowich, J. J. (1987). Predicting chaotic time series. Physical Review Letters, 59(8), 845-848. doi:10.1103/PhysRevLett.59.845

Hamid, N., & Noorani, M. (2012). On prediction of subang, selangor daily rainfall data: An application of local approximation method. J Sains Dan Mat, 4(2), 49-57. Retrieved from www.scopus.com

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. Retrieved from www.scopus.com

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) doi:10.1007/s12544-015-0170-8

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. doi:10.1007/s11071-016-2677-5

Mashuri, A., Adenan, N. H., & Hamid, N. Z. A. (2019). Determining the chaotic dynamics of hydrological data in flood-prone area. Civil Engineering and Architecture, 7(6), 71-76. doi:10.13189/cea.2019.071408

Pascale, A., & Nicoli, M. (2011). Adaptive bayesian network for traffic flow prediction. Paper presented at the IEEE Workshop on Statistical Signal Processing Proceedings, 177-180. doi:10.1109/SSP.2011.5967651 Retrieved from www.scopus.com

Ruslan, A. B., & Hamid, N. Z. A. (2019). Application of improved chaotic method in determining number of k-nearest neighbor for CO data series. International Journal of Engineering and Advanced Technology, 8(6 Special Issue 3), 10-14. doi:10.35940/ijeat.F1003.0986S319

Shang, Q., Lin, C., Yang, Z., Bing, Q., & Zhou, X. (2016). Short-term traffic flow prediction model using particle swarm optimization-based combined kernel function-least squares support vector machine combined with chaos theory. Advances in Mechanical Engineering, 8(8) doi:10.1177/1687814016664654

Sivakumar, B., & Jayawardena, A. W. (2002). An investigation of the presence of low-dimensional chaotic behaviour in the sediment transport phenomenon. [Recherche sur la présence d'un chaos déterministe de faible dimension dans le phénomène de transport sédimentaire] Hydrological Sciences Journal, 47(3), 405-416. doi:10.1080/02626660209492943

Sivakumar, B., Liong, S. -., Liaw, C. -., & Phoon, K. -. (1999). Singapore rainfall behavior: Chaotic? Journal of Hydrologic Engineering, 4(1), 38-48. doi:10.1061/(ASCE)1084-0699(1999)4:1(38)

Sugihara, G., & May, R. M. (1990). Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344(6268), 734-741. doi:10.1038/344734a0

Tijani, A., Yusuf, S. D., Ibrahim, U., Loko, A. Z., & Mundi, A. A. (2020). Evaluation of real time rain-rate on downlink satellite signal attenuation in abuja, nigeria. EDUCATUM Journal of Science, Mathematics and Technology, 7(1), 29-38. Retrieved from www.scopus.com

Vlad, S. (2010). Investigation of chaotic behavior in euro-leu exchange rate. Journal of Applied Computer Science and Mathematics, 8(4), 67-71. Retrieved from www.scopus.com

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. doi:10.1080/0144164042000195072

Xie, J., & Choi, Y. -. (2017). Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data. International Journal of Distributed Sensor Networks, 13(11) doi:10.1177/1550147717745009

Xue, J. -., & Shi, Z. -. (2008). Short-time traffic flow prediction using chaos time series theory. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/ Journal of Transportation Systems Engineering and Information Technology, 8(5), 68-72. Retrieved from www.scopus.com

Yi, H., Heejin, J., & Bae, S. (2017). Deep neural networks for traffic flow prediction. Paper presented at the 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017, 328-331. doi:10.1109/BIGCOMP.2017.7881687 Retrieved from www.scopus.com

Zaim, W. N. A. B. W. M., & Hamid, N. Z. A. (2017). Forecasting ozone pollutant (o3) in universiti pendidikan sultan idris, tanjung malim, perak, malaysia based on monsoon using chaotic approach. [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. doi:10.17576/jsm-2017-4612-30