XGBoost: a tree-based approach for traffic volume prediction

The growth in the transportation sector has led to an enormous increase in the number of vehicles that ply our roads daily. Even though this advancement has provided numerous transportation modes, it has resulted in serious transportation issues including road congestion. Hence, estimating the number of vehicles on a road will enable traffic managers to take appropriate decisions to curb congestion. In this paper, we propose to use an extreme gradient boosting (XGBoost) algorithm to efficiently and accurately predict the hourly traffic volume. We investigate the effectiveness of the proposed method for different scenarios including how well it performs during extreme weather conditions and holidays. We further investigate the effect of ridge and LASSO regularization on the performance of XGBoost. We then propose a new approach for setting the LASSO regularization parameter in terms of the number of observations and predictors. The performance and computational efficiency of the proposed approach is evaluated on data collected from Interstate-94, Minnesota and the results are compared with existing methods. The results show that the proposed method provides a good balance between performance and computational efficiency.