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Article

Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach

College of Engineering, University of Georgia, Athens, GA 30602, USA
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Author to whom correspondence should be addressed.
Infrastructures 2020, 5(8), 62; https://doi.org/10.3390/infrastructures5080062
Received: 16 June 2020 / Revised: 5 July 2020 / Accepted: 20 July 2020 / Published: 24 July 2020
(This article belongs to the Special Issue Innovate, Research, and Maintain Transportation Infrastructure)
Generating meaningful inferences from crash data is vital to improving highway safety. Classic statistical methods are fundamental to crash data analysis and often regarded for their interpretability. However, given the complexity of crash mechanisms and associated heterogeneity, classic statistical methods, which lack versatility, might not be sufficient for granular crash analysis because of the high dimensional features involved in crash-related data. In contrast, machine learning approaches, which are more flexible in structure and capable of harnessing richer data sources available today, emerges as a suitable alternative. With the aid of new methods for model interpretation, the complex machine learning models, previously considered enigmatic, can be properly interpreted. In this study, two modern machine learning techniques, Linear Discriminate Analysis and eXtreme Gradient Boosting, were explored to classify three major types of multi-vehicle crashes (i.e., rear-end, same-direction sideswipe, and angle) occurred on Interstate 285 in Georgia. The study demonstrated the utility and versatility of modern machine learning methods in the context of crash analysis, particularly in understanding the potential features underlying different crash patterns on freeways. View Full-Text
Keywords: crash analysis; freeways; machine learning; decision trees; gradient boosting; discriminant analysis; features crash analysis; freeways; machine learning; decision trees; gradient boosting; discriminant analysis; features
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MDPI and ACS Style

Morris, C.; J. Yang, J. Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach. Infrastructures 2020, 5, 62. https://doi.org/10.3390/infrastructures5080062

AMA Style

Morris C, J. Yang J. Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach. Infrastructures. 2020; 5(8):62. https://doi.org/10.3390/infrastructures5080062

Chicago/Turabian Style

Morris, Clint, and Jidong J. Yang. 2020. "Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach" Infrastructures 5, no. 8: 62. https://doi.org/10.3390/infrastructures5080062

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