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A variety of statistical and machine learning methods are used to model crashfrequency on specific roadways with machine learning methods generally having ahigher prediction accuracy. Recently, heterogeneous ensemble methods (HEM),including stacking, have emerged as more accurate and robust intelligenttechniques and are often used to solve pattern recognition problems byproviding more reliable and accurate predictions. In this study, we apply oneof the key HEM methods, Stacking, to model crash frequency on five laneundivided segments (5T) of urban and suburban arterials. The predictionperformance of Stacking is compared with parametric statistical models (Poissonand negative binomial) and three state of the art machine learning techniques(Decision tree, random forest, and gradient boosting), each of which is termedas the base learner. By employing an optimal weight scheme to combineindividual base learners through stacking, the problem of biased predictions inindividual base-learners due to differences in specifications and predictionaccuracies is avoided. Data including crash, traffic, and roadway inventorywere collected and integrated from 2013 to 2017. The data are split intotraining, validation, and testing datasets. Estimation results of statisticalmodels reveal that besides other factors, crashes increase with density (numberper mile) of different types of driveways. Comparison of out-of-samplepredictions of various models confirms the superiority of Stacking over thealternative methods considered. From a practical standpoint, stacking canenhance prediction accuracy (compared to using only one base learner with aparticular specification). When applied systemically, stacking can helpidentify more appropriate countermeasures.

Heterogeneous Ensemble Learning for Enhanced Crash Forecasts -- A Frequentest and Machine Learning based Stacking Framework