Freeway Short-Term Travel Time Prediction Based on LightGBM Algorithm

Abstract

Abstract In order to realize the prediction of freeway travel time, a short-term travel time prediction model based on LightGBM (Light Gradient Boosting Machine) is proposed under the influence of weather factors, time period factors, and traffic factors. These factors are called as the features used for increase prediction accuracy. The travel time of a single vehicle is determined by license plate recognition data of two adjacent video monitors in Shaoxing section of Shanghai-Hangzhou-Ningbo Freeway, and a better travel time data set is constructed by data preprocessing. The feature data are determined by Pearson correlation. Based on the analysis of main optimization parameters in LightGBM, the short-term average travel time is predicted, the MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) obtained by experiments are satisfying, indicating that LightGBM model has high accuracy and good fit. Finally, through comparison with KNN (K-Nearest Neighbor) model and GBDT (Gradient Boosting Decision Tree) model, the prediction accuracy and training speed both show that LightGBM has good advantages in predicting short-term freeway travel time.