A real-time crash prediction fusion framework: An imbalance-aware strategy for collision avoidance systems

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2020-09, Vol.118, p.102708, Article 102708
• Main Authors: Elamrani Abou Elassad, Zouhair, Mousannif, Hajar, Al Moatassime, Hassan
• Format: Article
• Language: English
• Subjects: Driving behavior; Fusion framework; Machine learning; Real-time crash prediction; SMOTE
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2020.102708

•Design and validate a fusion framework for real-time crash prediction.•Information fusion strategy based on four distinct categories of features.•Diversity generation using four learners: BL, kNN, SVM and MLP.•Boosting and Bagging with Meta-Classifier for more robust outcomes.•An Imbalance-learning founded on SMOTE for crash events prediction. Real-time traffic crash prediction has been a major concern in the development of Collision Avoidance Systems (CASs) along with other intelligent and resilient transportation technologies. There has been a pronounced progress in the use of machine learning models for crash events assessment by the transportation safety research community in recent years. However, little attention has been paid so far to evaluating real-time crash occurrences within information fusion systems. The main aim of this paper is to design and validate an ensemble fusion framework founded on the use of various base classifiers that operate on fused features and a Meta classifier that learns from base classifiers’ results to acquire more performant crash predictions. A data-driven approach was adopted to investigate the potential of fusing four real-time and continuous categories of features namely physiological signals, driver maneuvering inputs, vehicle kinematics and weather covariates in order to systematically identify the crash strongest precursors through feature selection techniques. Moreover, a resampling-based scheme, including Bagging and Boosting, is conducted to generate diversity in learner combinations comprising Bayesian Learners (BL), k-Nearest Neighbors (kNN), Support Vector Machine (SVM) and Multilayer Perceptron (MLP). To ensure that the proposed framework provide powerful and stable decisions, an imbalance-learning strategy was adopted using the Synthetic Minority Oversampling TEchnique (SMOTE) to address the class imbalance problem as crash events usually occur in rare instances. The findings show that Boosting depicted the highest performance within the fusion scheme and can accomplish a maximum of 93.66% F1 score and 94.81% G-mean with Naïve Bayes, Bayesian Networks, k-NN and SVM with MLP as the Meta-classifier. To the best of our knowledge, this work presents the first attempt at establishing a fusing framework on the basis of data from the four aforementioned categories and fusion models while accounting for class imbalance. Overall, the method and findings provide new insights into crash prediction and can be harne