Motorcycle Safety Investigation in Kentucky Using Machine and Deep Learning Techniques

This study analyzes the factors affecting motorcycle crash severity in the state of Kentucky while applying machine learning method (i.e., random forest) and deep learning model (i.e., combined principal component-neural network model). Severe motorcycle crashes were the main severity level outcome analyzed in this study and are those crashes resulting in either serious motorcycle injury or fatality. To the authors’ knowledge, these models have been very rarely implemented to analyze motorcycle crashes, especially when it comes to severe motorcycle crashes. Recent five-year motorcycle crash data (2015 to 2019) from Kentucky were used. The random forest classifier was applied to rank each feature’s importance in influencing serious injury or fatal motorcycle crashes. The random forest classifier indicated that collision time, crash location, driver age, helmet use, and vehicle type colliding with the motorcycle were the key features affecting severe motorcycle crashes while yielding 91% prediction accuracy. By testing multiple numbers of principal components, 800 principal components were found to decrease overfitting while still retaining high prediction accuracy. Thus, 800 principal components were used for fitting the neural network model. The neural network showed that driver-related (i.e., age), crash-related (i.e., crash location, collision time, and manner of motorcycle collision), and roadway-related factors (i.e., roadway surface condition) could successfully predict severe motorcycle crashes with 94.2% prediction accuracy. An advanced occlusion-based interpretation of the neural network model also produced a list of features most highly correlated with the model prediction performance. The neural network model result was largely consistent with that of random forest. Nevertheless, deep learning techniques (e.g., the combined principal component-neural network model) could better predict severe motorcycle crashes with higher accuracy compared to machine learning techniques (e.g., random forest). Overall, the study results demonstrated that both machine learning (random forest) and deep learning (combined principal component-neural network model) techniques can be used successfully in identifying those key features contributing to severe motorcycle crashes.