A literature review of machine learning algorithms for crash injury severity prediction.

INTRODUCTION

Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase of injury severity of its interveners when involved in a road crash. Determining such factors is essential to help decision making in road safety management, improving road safety, and reducing the severity of future crashes.

METHOD

This paper presents a recent literature review of the methods that have been applied to road crash injury severity modeling. It includes 56 studies from 2001 to 2021 that consider more than 20 different statistical or machine learning techniques.

RESULTS

Random Forest was the algorithm with the best results, achieving the best performance in 70% of the times that it was applied and in 29% of all studies. Support Vector Machine and Decision Tree achieved the best performance in 53% and 31% of the times and in 16% and 14% of all studies, respectively. Bayesian Networks and K-Nearest Neighbors achieved the best performance in 67% and 40% of the times that were used but only achieved the best performance in 4% and 7% of all the studies analyzed, respectively.

CONCLUSIONS

At this point, Random Forest revealed to be a good approach for road traffic crash injury severity prediction followed by Support Vector Machine, Decision Tree, and K-Nearest Neighbor. However, there is still a lot of room in this area to explore other techniques that can best suit this purpose as not only the model's performance should be considered but also causality issues, unobserved heterogeneity, and temporal instability. Practical Applications: This review enables researchers to understand the recent techniques applied in the analysis of injury severity modeling, and the ones that achieved the best performance results. Based on the reviewed studies, challenges and future research directions are presented.