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.

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