Complex Network Short-Term Traffic Forecasting 
Based on Lasso-NN Model

Abstract

Abstract:

Abstract： Traditional traffic forecasting models are transforming from single section historical data processing to multi-section and multi-timing historical data processing. However, when considering the influence between each section, the fickle traffic condition tends to complicate the forecasting model. Therefore, this paper introduced the Lasso method used in multivariable linear regression and utilized its excellent ability of variable selection. It selected partial high correction sections from a complex multi-section road network. Combined with the non-linear neural network, a new LassoNN model was proposed. The result shows that the LassoNN model has an overall lower error rate. In the intersection region of the road network, the error rate is less than 9.2% and in the nonintersection region, it is less than 6.7%.

Key words: short-term traffic forecasting, least absolute shrinkage and selection operator (Lasso), variable selection, neural network

CLC Number:

U 491

JIANG Shizheng,XU Rong,CHEN Qimei. Complex Network Short-Term Traffic Forecasting
Based on Lasso-NN Model[J]. Journal of Shanghai Jiaotong University, 2015, 49(02): 281-286.

References

［1］王进, 史其信. 短时交通流预测模型综述［J］. ITS通讯, 2005, 7(1): 1013.

WANG Jin, SHI Qixin. Survey on shortterm traffic flow forecasting modelling［J］. ITS Communication, 2005, 7(1): 1013.

［2］Thomas T, Weijermars W, Berkum E V. Predictions of urban volumes in single time series［J］. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(1): 7180.

［3］Castillo E, Nogal M, Menéndez J M, et al. Stochastic demand dynamic traffic models using generalized betaGaussian Bayesian networks［J］. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 565581.

［4］Adeli H. Neural networks in civil engineering: 19892000［J］. ComputerAided Civil and Infrastructure Engineering, 2001, 16(2): 126142.

［5］Vlahogianni E I, Karlaftis M G, Golias J C. Shortterm traffic forecasting: Where we are and where we’re going［J］. Transportation Research Part C: Emerging Technologies, 2014, 43(1): 319.

［6］Tibshirani R. Regression shrinkage and selection via the lasso［J］. Journal of the Royal Statistical Society. Series B(Methodological), 1996, 58(1): 267288.

［7］Efron B, Hastie T, Johnstone I, et al. Least angle regression［J］. The Annals of statistics, 2004, 32(2): 407499.

［8］Zou H, Hastie T, Tibshirani R. Sparse principal component analysis［J］. Journal of Computational and Graphical Statistics, 2006, 15(2): 265286.

［9］Trendafilov N T, Jolliffe I T. DALASS: Variable selection in discriminant analysis via the LASSO［J］. Computational statistics & Data Analysis, 2007, 51(8): 37183736.

［10］Kamarianakis Y, Shen W, Wynter L. Realtime road traffic forecasting using regimeswitching spacetime models and adaptive LASSO［J］. Applied Stochastic Models in Business and Industry, 2012, 28(4): 297315.

［11］Sun S, Huang R, Gao Y. Networkscale traffic modeling and forecasting with graphical lasso and neural networks［J］. Journal of Transportation Engineering, 2012, 138(11): 13581367.

［12］张文龙, 于洪浩. 一种星形神经网络的混沌同步［J］. 上海交通大学学报, 2013, 47(2)： 220225.

ZHANG Wenlong, YU Honghao. Chaotic synchronization of a type of starshaped neural network［J］. Journal of Shanghai Jiaotong University, 2013, 47(2)： 220225.

［13］Zou H, Hastie T. Regularization and variable selection via the elastic net［J］. Journal of the Royal Statistical Society: Series B(Statistical Methodology), 2005, 67(2): 301320.

［14］California department of transportation. The freeway performance measurement system(PeMS) ［DB/OL］.(20130606)［20140410］. http://pems. dot.ca.gov/

[1]	ZENG Guozhi, WEI Ziqing, YUE Bao, DING Yunxiao, ZHENG Chunyuan, ZHAI Xiaoqiang. Energy Consumption Prediction of Office Buildings Based on CNN-RNN Combined Model [J]. Journal of Shanghai Jiao Tong University, 2022, 56(9): 1256-1261.
[2]	WU Shuchen, QI Zongfeng, LI Jianxun. Intelligent Global Sensitivity Analysis Based on Deep Learning [J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 840-849.
[3]	QUAN Daying, CHEN Yun, TANG Zeyu, LI Shitong, WANG Xiaofeng, JIN Xiaoping. Radar Signal Recognition Based on Dual Channel Convolutional Neural Network [J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 877-885.
[4]	QIN Pengfei (秦鹏飞), WANG Xiaoliang∗ (王晓亮). Construction on Aerodynamic Surrogate Model of Stratospheric Airship [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(6): 768-779.
[5]	SU Hong1 (苏红), WU Bozhao2 (吴博钊), MAO Xuchu1∗ (茅旭初). Non-Line-of-Sight Multipath Detection Method for BDS/GPS Fusion System Based on Deep Learning [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(6): 844-854.
[6]	LÜ Qibing (吕其兵), LIU Tianyuan (刘天元), ZHANG Rong (张荣), JIANG Yanan (江亚南), XIAO Lei (肖雷), BAO Jingsong∗ (鲍劲松). Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 602-613.
[7]	LIU Min (刘敏), YI Ming (易鸣), WU Minghu∗ (武明虎), WANG Juan (王娟), HE Yu (何宇). Breast Pathological Image Classification Based on VGG16 Feature Concatenation [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 473-484.
[8]	JIANG Zhiguo (蒋志国), CHANG Qing∗ (常青). USSL Net: Focusing on Structural Similarity with Light U-Structure for Stroke Lesion Segmentation [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 485-497.
[9]	LU Guannan1 (卢冠男), WANG Mengling1∗ (王梦灵), FOX Tamara2, JIANG Peng3 (蒋鹏), JIANG Fusong3 (蒋伏松). Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 498-504.
[10]	QIN Yichao, HUANG Limin, WANG Xiao, MA Xuewen, DUAN Wenyang, HAO Wei. Feasibility of Wave Measurement by Using a Sailing Buoy and the Artificial Neural Network Technique [J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 498-505.
[11]	ZHAO Yong, SU Dan. Rogue Wave Prediction Based on Four Combined Long Short-Term Memory Neural Network Models [J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 516-522.
[12]	YUAN Xiaoqi (袁筱祺), ZHU Lelan (朱乐兰), XU Qiongfan(徐琼凡), GAO Wei (高玮). Risk Prediction Model of Gallbladder Disease in Shanghai Middle-Aged and Elderly People Based on Neural Networks [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(2): 153-159.
[13]	DING Ming, MENG Shuai, WANG Shuheng, XIA Xi. Neural-Network-Based Adaptive Feedback Linearization Control for 6-DOF Wave Compensation Platform [J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 165-172.
[14]	LIU Xiuli, XU Xiaoli. A Fault Diagnosis Method Based on Feature Pyramid CRNN Network [J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 182-190.
[15]	NIE Rui, WANG Hongru. Fault Diagnosis of UAV Formation Actuator Based on Neural Network Observer [J]. Air & Space Defense, 2022, 5(2): 32-41.