Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups

Abstract

Access to non-biased and accurate models capable of predicting driver injury severity of collision events is vital for determining what safety measures should be implemented at intersections. Inadequate models can underestimate the potential for collision events to result in driver fatalities or injuries, which can lead to improperly assessing the safety criteria of an intersection. This study investigates how injury severity differs between drivers of various ages and gender groups using cost-sensitive data-mining models. Previous research efforts have used machine learning methods for predicting injury severity; however, these studies did not consider the consequences (cost) of incorrect predictions. This paper addresses this shortfall by considering the monetary cost of incorrect injury severity predictions when developing C4.5, instance-based (IB), and random forest (RF) machine-learning models. One model of each method was developed for four distinct cohorts of drivers (i.e., younger males, younger females, older males, and older females). Each model considered a selection of driver, vehicular, road/traffic, environmental, and crash parameters for determining if they significantly influenced driver injury severity. A five-year period of two-vehicle crash data collected at signalized intersections in the metropolitan area of Miami, Florida was used in the models. Results indicated that cost-sensitive learning classifiers were superior to regular classifiers at accurately predicting injuries and fatalities of crashes. Among cost-sensitive models, RF outperformed C4.5 and IB models in predicting driver injury severity for four groups of drivers. The models displayed substantial differences in injury severity determinants across the age/gender cohorts.

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

“…Among the non-traditional methods with two or multiple answer variables, there are the Artificial Neuronal Network (ANN) [26] and Classification And Regression Trees (CARTs) [27,28]. Similarly, other advanced tools have been combined, such as Machine Learning (ML) methods, including Conditional Inference Trees and Forest [29], Decision Trees (DT) and Decision rules (DR) [30][31][32], Random Forest (RF) and Boosted Regression Trees (BRTs) [33], RF and OPM models [34], CART (as a variable selection model) and Support Vector Machine (SVM) (as a predictive model) [35], RF for variable selection and ANN for prediction [36], and comparison ML methods and performance studies [37,38].…”

“…Mafi et al [37] studied driver injuries in two passenger car collisions in signalized intersections in Miami, Florida, with driver, environmental, roadway, and vehicle characteristics, as well as crash identification variables. Within the data mining models selected, RF was superior to C4.5 and IB when studying prediction capability and cost.…”

A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

“…By contrast, machine learning models are non-parametric tools good at handling outliers and missing values. They are simultaneously able to hand-picked the supreme significant explanatory variables to classify the dependent variable [24]. Furthermore, these two model types have diverse properties: statistical models provide good theoretical interpretability with explicit mathematics construction; whereas machine learning techniques employ a “black box” tactic to forecast crash severity and often lack a reasonable explanation of the model.…”

Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

“…This makes machine learning approaches particularly useful for traffic safety data analysis. The machine learning technologies applied in the past research include: Random forest [22,23], SVM (Support Vector Machine) [24,25], neural networks [26,27], and XGBoost [28].…”