Relationship between Short Term Traffic Flow Chaos Fractal Properties and the Necessary Data for Prediction

Abstract:

Analyze the traffic flow in multi-scale time window of a freeway by using the nonlinear analysis method such as Correlation dimension , recursive map and so on, we find that chaos and fractal still exist in wide observation scales. Traffic flow correlation dimension reduces when the length of time window increases, in the observation scale of minutes. However, traffic flow correlation dimension reduces when the length of time window reduces, in the observation scale of seconds, instead of fractal property disappearance as predicted before. We present that, from the view of prediction, the recording point which is 10 times of the correlation dimension is an essential length of the data to predict. The simple model we present, which includes speed difference between vehicles and observation scales of traffic flow, can explain some of the reasons of the traffic flow chaos.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 1256-1261
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.1256
• Online since: August 2013
• Authors: Gang Yin, Yu Feng Chen
• Keywords: Component, Correlation Dimension, Recursive Map, Traffic Flow

References

[1] Wang Zhengwu, Huang Zhongxiang, and Kuang Aiwu, Short term traffic flow chaos identification and forecasting precision analysis, Changsha Institute of transport, 2004, p.73–76.

[2] Feng Weidong, Chen Jian, He Guoguang, Liu bao, Traffic flow in fractal research, High technology letters, 2003, p.59–65.

[3] DENNIS B, Desharnais RA, Cushing JM, Henson SM, Costantino RF, Can noise induce chaos, OIKOS, 2003, p.329–339.

DOI: 10.1034/j.1600-0706.2003.12387.x

[4] WOLF, A., Swift, J., Swiney, H., Vastano, J, Determining Lyapunov exponents from a time series, Physica D, 1985 , p.285–317.

DOI: 10.1016/0167-2789(85)90011-9

[5] H. KANTZ, A robust method to estimate the maximal Lyapunov exponent, Physics Letters A, 1994, p.77–87.

DOI: 10.1016/0375-9601(94)90991-1

[6] Yang Zhengling, Lin Kongyuan, Power system load records discussion on causes of chaos, Automation of electric power systems, 2002 , p.18–23.

[7] Gao Wei, Lu Baichuan, and Yun Tianli, Short-term traffic flow forecasting based on spatiotemporal characteristics and RBF neural network, Journal of transport information and safety, 2011, p.16–19.

[8] Wu C H, Ho J M, and Lee D T, Travel-time prediction with support vector regression, IEEE Transactions on Intelligent Transportation Systems, 2004, p.276–281.

DOI: 10.1109/tits.2004.837813

[9] Shan Huang, and Adel W Sadek, A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting, Transportation Research Part C, 2009, p.510–525.

DOI: 10.1016/j.trc.2009.04.006

[10] Bidisha Ghosh, and Biswajit Basu, Member, IEEE, and Margaret O' Mahony. Multivariate short-term traffic flow forecasting using time-series analysis, IEEE Transactions on Intelligent Transportation Systems, 2009, p.246–254.

DOI: 10.1109/tits.2009.2021448

[11] Ding Shicheng, Multivariate analysis method and its application, " Changchun: Jilin people, s publishing house, (1981).

[12] Yao Zhisheng, and Shao Chunfu, Road traffic state prediction research, Journal of Harbin Institute of Technology, 2009, p.247–249.

[13] Wang Jin, and Shi Qixin, Short-term traffic flow prediction model in China, public safety, 2005, p.92–98.

[14] Shi Zongxin, and Zheng Weizhong, The road network traffic flow forecasting methods, Journal of traffic and transportation engineering, 2004, p.68–71.

[15] Wen Dong, Influence Modeling of Complex Stochastic Processes, Cambridge, MA, USA: Massachusetts Institute of Technology, (2006).

[16] Dong Honghui, Sun Xiaoliang, and Jia Limin, The multi-modal traffic flow prediction model, Journal of Jilin University: Science edition, 2011, p.645–649.

[17] Yang Chao, and Wang Zhiwei, The optimal GA WNN application in traffic flow forecasting, Computer engineering, 2011, p.149–151.