A Novel Traffic Flow prediction model based on the Improved Extreme Learning Machine

The changes of urban traffic flow is affected by many factors, and during predicting the traffic flow, it should consider each factor. The paper considers the characteristic between time and traffic flow, and introduces the time factor as a separated input parameter. Besides, some unexpected situations have large impacts on the predicting results, so, the unexpected factor is introduced as an improved factor to the input samples. Based on these, a novel prediction model is proposed, called at-ELM, which determines the hidden node number by using PCA, and reduces the impacts of the hidden node numbers on the predicting results. After performance tests and analysis, the novel model is a little improved in prediction accuracy and completely acceptable.