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%.