Lane changing assistance systems advise drivers on safe gaps for making mandatory lane changes at lane drops. In this study, such a system was developed using a Bayes classifier and a decision tree to model lane changes. Detailed vehicle trajectory data from the Next Generation Simulation (NGSIM) dataset were used for model development (US Highway 101) and testing (Interstate 80). The model predicted driver decisions regarding whether or not to merge as a function of certain input variables. The best results were obtained when both the Bayes and decision tree classifiers were combined into a single classifier using a majority voting principle. Predictive accuracy was 94.3% for non-merge events and 79.3% for merge events. In a lane change assistance system, the accuracy of non-merge events is more critical than accuracy for merge events. Misclassifying a non-merge event as a merge event could result in a crash, while misclassifying a merge event as a non-merge event would only result in a lost opportunity to merge. Sensitivity analysis performed by assigning a higher misclassification cost for non-merge events resulted in even higher accuracy for non-merge events, but lower accuracy for merge events. (Author/publisher)
Abstract