Decision-Tree-based Ensemble Learning Models for Long-Term Traffic Intensity Forecasting and Analysis of Congestion Treatment Strategies

Abstract

Traffic intensity forecasting is a key factor in analyzing traffic patterns and making recommendations to overcome congestion. It can also prove helpful to the Intelligent Transportation System (ITS) application. In this work, we have made a detailed comparative evaluation of various ML regression algorithms aimed at solving the problem of long-term traffic intensity prediction. A lot of work focuses mainly on traffic flow prediction. However, work on traffic intensity prediction has not been done sufficiently. For this problem, ensemble learning methods like Random Forest Regression that use the outputs of individual trees (Decision Trees) proved to be more successful and efficient rather than the single models approach. This work also dictates the study of various features that may be used to express the traffic data and the various strategies that can be employed to make decisions on whether a solution to overcome traffic congestion is needed.