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An Overview of Data Based Predictive Modeling Techniques Used in Analysis of Vehicle Crash Severity

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Intelligent Technologies and Applications (INTAP 2021)

Abstract

Accident injury prediction is a crucial constituent to reducing fatalities linked to vehicle crashes. The vehicle development process and road safety planning includes also the injury prediction for occupants and Vulnerable Road Users (VRUs) in a vehicle crash and the identification of the factors responsible for increased traffic collision injuries. This paper reviews the different data-based prediction techniques to modeling a vehicle crash event, crash frequency and crash severity. Machine learning (ML) is a research field which has gained impetus in the recent years and is widely used in different engineering applications; including injury prediction in vehicle collisions. The paper is divided into two major sections; the first section presents an overview of the existing predictive models for estimating injury severity in a crash event to occupants and VRUs and the second section describes the applications of data-based modeling techniques to predict crash frequency in different traffic scenarios. We also discuss possible future applications of data-based modeling techniques in this domain.

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Noorsumar, G., Robbersmyr, K.G., Rogovchenko, S., Vysochinskiy, D. (2022). An Overview of Data Based Predictive Modeling Techniques Used in Analysis of Vehicle Crash Severity. In: Sanfilippo, F., Granmo, OC., Yayilgan, S.Y., Bajwa, I.S. (eds) Intelligent Technologies and Applications. INTAP 2021. Communications in Computer and Information Science, vol 1616. Springer, Cham. https://doi.org/10.1007/978-3-031-10525-8_28

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