An overview of Data Based Predictive Modeling Techniques used in Analysis of Vehicle Crash Severity ⋆ Gulshan Noorsumar 1[0000-0002-6718-4508] , Kjell G. Robbersmyr 1[0000-0001-9578-7325] , Svitlana Rogovchenko 1[0000-0001-8002-4974] , and Dmitry Vysochinskiy 1[0000-0002-0453-0012] University of Agder, Jon Lilletuns vei 9, Grimstad Norway gulshan.noorsumar@uia.no kjell.g.robbersmyr@uia.no svitlana.rogovchenko@uia.no dmitry.vysochinskiy@uia.no Abstract. Accident injury prediction is a crucial constituent to reduc- ing fatalities linked to vehicle crashes. The vehicle development process and road safety planning includes also the injury prediction for occu- pants and Vulnerable Road Users (VRUs) in a vehicle crash and the identification of the factors responsible for increased traffic collision in- juries. 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 ex- isting predictive models for estimating injury severity in a crash event to occupants and VRUs and the second section describes the applica- tions 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. Keywords: data-based prediction models · machine learning · vehicle crash · modeling and simulation · injury prediction · crashworthiness. 1 Introduction Road accidents have been one of the major causes of injuries and fatalities in the world. The 2015 European Commission report identifies frontal impact as the most common crash scenario leading to serious injuries, followed by side im- pact. This might be due to the different forces acting in impact scenarios along with the cage protecting the occupants in a collision [50], [38].(this sentence should be reformulated) The report also suggests further study of mechanisms ⋆ Supported by University of Agder.