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Developing New Hybrid Grey Wolf Optimization-based Artificial Neural Network for Predicting Road Crash Severity by Vittorio Astarita; Sina Shaffiee Haghshenas; Giuseppe Guido; Alessandro Vitale is a Engineering article available to read on EtoBox.

What is Developing New Hybrid Grey Wolf Optimization-based Artificial Neural Network for Predicting Road Crash Severity about?

With more cars on the road and an increasing travel rate, one of the main issues in transportation engineering is how to make roads safe. The evaluation of the level of road crash severity is a significant component of the road safety assessment in terms of financial and personal losses. One of the most advanced ways to control and manage traffic information in modern traffic management systems is through intelligent transportation systems (ITS). ITS plays a big part in improving road safety. On the other hand, machine learning techniques play an important role in increasing the level of ITS performance. Hence, this study aims to predict the level of crash severity through a classification approach. In this study, two machine learning techniques namely artificial neural network (ANN) and Grey Wolf optimization algorithm (GWO)–ANN were developed to predict the level of road crash severity, and several performance metrics were used to assess the predictive algorithms' predictive capability. For this purpose, some factors that affect the level of severity were considered, including light condition (LC), weekday (W), horizontal alignment (HA), road element (RE), type of crash (TC), veh

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Author

Vittorio Astarita; Sina Shaffiee Haghshenas; Giuseppe Guido; Alessandro Vitale

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)