Traffic flow prediction is a key problem of realizing intelligent transportation technology. Forecasting traffic flow in time and accurately is the precondition to realize the dynamic traffic management. Short -term traffic flow prediction is an important part of traffic flow prediction. In this paper, the Traffic Flow Prediction Based on Combined Model (TFPBCM) based on traffic flow sequence partition and Extreme Learning Machine (ELM) is designed for the short time traffic flow forecasting. The algorithm divides the traffic flow into different patterns along a time dimension by K-means, and then models and forecasts for each pattern by ELM. The proposed algorithm is compared with Back Propagation (BP) and ELM. The combined model algorithm on modeling time is 1/10 of BP, but is 4 times ELM. Its MSE is 1/50 of BP and 1/20 of ELM. The combined model algorithm’s coefficient of detemination (R2 ) is close to 1, so the credibility of the model is higher than others.
CAO Wei. The city traffic flow prediction and cross analysis based on BP neural network[D]. [Master dissertation], Kunming University of Science and Technology, 2006.
SHANG Ning, QIN Minggui, WANG Yaqin, et al. A BP neural network method for short-term traffic flow forecasting on crossroads[J]. Computer Applications and Software, 2006, 23(2): 32-33, 57.
[7]
翟敏. 极限学习机的自适应网络结构选择方法研究[D]. [硕士论文], 西北大学, 2014.
ZHAI Min. Research on adaptive network structure selection method for extreme learning machine[D]. [Master dissertation], Northwest University, 2014.
[8]
甘露. 极限学习机的研究与应用[D]. [硕士论文], 西安电子科技大学, 2014.
GAN Lu. Research and application of extreme learning machine[D]. [Master dissertation], Xidian University, 2014.
[9]
王智慧. BP神经网络和ELM算法研究[D]. [硕士论文], 中国计量学院, 2012.
WANG Zhi-hui. Research on BP neural networks and ELM Algorithms[D]. [Master dissertation], China Jiliang University, 2012.
ZHANG Yi. Research of short-term traffic volume prediction based on kalman filtering[D]. [Master dissertation], Shenyang University of Technology, 2014.
LUO Xianglong. Short-term traffic flow prediction method based on EMD and artificial neural network[J]. Computer Engineering and Applications, 2010, 46(26): 212-214. doi: 10.3778/j.issn.1002-8331.2010.26.066.
ZHU Zhengyu. Short-term traffic flow forecasting model combining SVM and Kalman filtering[J]. Computer Science, 2013, 40(10): 248-251.
[14]
TCHRAKIAN T T, BASU B, and O’MAHONY M. Real-time traffic flow forecasting using spectral analysis[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 519-526.
[15]
CHEN Syuanyi and CHOU Weiyao. Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach[C]. 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, 2012: 1821-1826.
[16]
CHAN K Y, DILLON T S, SINGH J, et al. Neural- network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg- Marquardt algorithm[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 644-654.
[17]
XU Yanyan, KONG Qingjie, and LIU Yuncai. Short-term traffic volume prediction using classification and regression trees[C]. IEEE Intelligent Vehicles Symposium(IV), Gold Coast, 2013: 493-498.
[18]
WILLIAMS B M. Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process[D]. [Ph.D. dissertation], University of Virginia, 1999: 243-246.
