An Approach for Short Term Traffic Flow Forecasting Based on Genetic Neural Network

Abstract:

In this paper, genetic neural network is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time correlation and spatial correlation, we construct the short-term traffic flow forecasting model using back-propagation neural network that has the function of arbitrary nonlinear function approximation. In order to find proper initial values of the neural network weights and threshold quickly, a combination of neural network prediction method is presented. This method utilizes genetic algorithm to choose the initial weights and threshold, and uses L-M algorithm to train sample, which can enhance the global convergence rate. Trained network is used for short-term traffic flow prediction with mean square error as the forecast performance evaluation. The results show that the performance of genetic neural network is better than a separate BP neural network for short-term traffic flow prediction.

• Periodical: Advanced Materials Research (Volumes 671-674)
• Pages: 2866-2869
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.671-674.2866
• Online since: March 2013
• Authors: Lei Yang, Wei Dong Dai
• Keywords: Back-Propagation Network, Genetic Neural Network, Space Correlation, Time Correlation, Traffic Flow Prediction

References

[1] Hua Zhang, Xiaoye Wang, Yingyuan Xiao, Similarity Research for Urban Intersection Traffic Flow, JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, No. 1, pp.439-445, 2012. Dj.M. Maric, P.F. Meier and S.K. Estreicher: Mater. Sci. Forum Vol. 83-87 (1992).

DOI: 10.4156/jdcta.vol6.issue1.53

[2] Qingsong Song, Xibin Liu, Xiangmo Zhao, Short-term Traffic Flow and Hourly Electric Load Forecasting Algorithm based on Echo State Neural Networks, JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, No. 4, pp.166-172, (2012).

DOI: 10.4156/jdcta.vol6.issue4.20

[3] Yadav R. N. Kalra P. K. John J. Time series prediction with single multiplicative neuron model,. Applied Soft Computing. Vol. 7, No. 4, pp.1157-1163, (2007).

DOI: 10.1016/j.asoc.2006.01.003

[4] Vlahogianni Eleni I. Karlaftis Matthew G. Golias John C. Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume,. Transportation Research Part C: Emerging Technologies. Vol. 14, No. 5, pp.351-367, (2007).

DOI: 10.1016/j.trc.2006.09.002

[5] Fei-Yue Wang. Short-term traffic flow prediction based on ratio-median lengths of intervals two-factors high-order fuzzy time series,. IEEE International Conference on Vehicular Electronics and Safety, pp.1-7, (2007).

DOI: 10.1109/icves.2007.4456387

[6] Yuesheng Gu, Yanpei Liu, Jiucheng Xu, Improved Hybrid Intelligent Method for Urban Road Traffic Flow Forecasting based on Chaos-PSO Optimization, IJACT: International Journal of Advancements in Computing Technology, Vol. 3, No. 7, pp.282-290, (2011).

DOI: 10.4156/ijact.vol3.issue7.33

[7] WANG Xue-wu, TAN De-jian. The Application of Neural Network and Its Development,. Computer Engineering and Applications, Vol. 39, No. 3, pp.33-37, (2008).

[8] Yungho Leu, Tzu-I Chiu, An Effective Stock Portfolio Trading Strategy using Genetic Algorithms and Weighted Fuzzy Time Series, JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, No. 6, pp.333-344, (2012).

DOI: 10.1109/nasnit.2011.6111124

[9] REN Jianfeng, LI Shuhong, Zhang Yadong, A New Multi-Objective Robust Genetic Algorithm and Its Application, AISS: Advances in Information Sciences and Service Sciences, Vol. 4, No. 1, pp.177-184, (2012).

DOI: 10.4156/aiss.vol4.issue1.23

[10] Haijun Chen, Zhiyan Li, Jie Liu, Lifeng Wu, A Global Optimization Algorithm for Minimizing Mean Absolute Percentage Error of Nonlinear Forecasting Model, AISS: Advances in Information Sciences and Service Sciences, Vol. 4, No. 10, pp.253-260, (2012).

DOI: 10.4156/aiss.vol4.issue10.30