Modeling Mandatory Lane Changing Using Bayes Classifier and Decision Trees

Abstract

A lane changing assistance system that advises drivers of safe gaps for making mandatory lane changes at lane drops is developed. Bayes classifier and decision-tree methods were applied to model lane changes. Detailed vehicle trajectory data from the Next Generation Simulation (NGSIM) data set were used for model development (U.S. Highway 101) and testing (Interstate 80). The model predicts driver decisions on whether to merge or not as a function of certain input variables. The best results were obtained when both Bayes and decision-tree classifiers were combined into a single classifier using a majority voting principle. The prediction accuracy was 94.3% for nonmerge events and 79.3% for merge events. In a lane change assistance system, the accuracy of nonmerge events is more critical than merge events. Misclassifying a nonmerge event as a merge event could result in a traffic crash, whereas misclassifying a merge event as a nonmerge event would only result in a lost opportunity to merge. Sensitivity analysis performed by assigning higher misclassification cost for nonmerge events resulted in even higher accuracy for nonmerge events but lower accuracy for merge events.