Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study

Abstract

Objective: Crash occurrence prediction has been of major importance in proactively improving traffic safety and reducing potential inconveniences to road users. Conventional statistical crash prediction models frequently suffer from severe data quality issues and require a significant amount of historical data. On the other hand, even though machine learning (ML) based algorithms have proven to be powerful in predicting future outcomes in different fields of applications, they likely fail to provide satisfactory results unless a tuning parameter approach is conducted. The main objective of this article is to develop real-time crash prediction models that will potentially be employed within traffic management systems.Methods: In this study, two highly optimized data-driven models for crash occurrence prediction have been designed based on the popular machine learning techniques, Support Vector Machine (SVM) and deep neural network Multilayer Perceptron (MLP). To ensure that the proposed algorithms produce robust and stable performance, the optimal scheme for models' construction has been thoroughly examined and discussed. Additionally, the further boost of models' performance requires the systemic assessment of crash strongest precursors within the driver-vehicle-environment triptych. Therefore, three categories of features, including driver input responses, vehicle kinematics and weather conditions, were measured during the execution of various driving tasks performed on a desktop driving simulator. Moreover, since crash events typically occur in rare instances tending to be underrepresented in the dataset, an imbalance-aware strategy to overcome the issue was adopted using the Synthetic Minority Oversampling TEchnique (SMOTE).Results: The results show that MLP exhibited the best performing prediction results, most particularly, in clear, overcast and snow conditions, in which MLP recall values were above 94%. Higher F1-score values were achieved in overcast and rain weather by MLP and snow conditions by SVM; whereas over 90% of G-mean levels were obtained under fog and rain conditions for MLP and snow condition for SVM.Conclusion: The findings provide new insights into crash events forecasting and may be used to promote enforcement efforts related to designing crash avoidance/warning systems that enhance the effectiveness of the system's application based on driver input and vehicle kinematics under various weather conditions.

Keywords: Crash prediction; SMOTE; driving simulator; machine learning; multilayer perceptron; support vector machine.