Title: Factors affecting crash risk within the car-sharing market
Authors: Kristina Sutiene; Monika Uselyte
Addresses: Department of Mathematical Modeling, Kaunas University of Technology, Kaunas, LT-51368, Lithuania ' Department of Mathematical Modeling, Kaunas University of Technology, Kaunas, LT-51368, Lithuania
Abstract: As the sharing economy becomes increasingly more popular, crash risk assessment has become important not only for insurance companies, but also for companies engaged in the car-sharing business. As such, linear regression and machine learning methods, such as regression trees and random forests, were used to model crash risk based on the observations retrieved from car-sharing systems. The evidence shows that the average daily trip duration, the month of the crash event, and the car brand have the greatest impact on crash rates, while holiday, working day or weekend; peak hour; and gender of the driver hold no valuable information for predicting crash risk. After a proper assessment of the risk indicators that have the greatest impact on the occurrence of crashes, companies might be able to enter into personalised car-sharing pricing by developing usage-based or pay-as-you-drive insurance products.
Keywords: car-sharing; risk assessment; personalised pricing; insurance; usage-based insurance; crash risk; context-sensitive data; regression model; random forest; regression tree; prediction; machine learning; insurtech.
DOI: 10.1504/IJRAM.2021.126436
International Journal of Risk Assessment and Management, 2021 Vol.24 No.2/3/4, pp.236 - 251
Received: 20 Jul 2020
Accepted: 26 Jul 2021
Published online: 26 Oct 2022 *