Developing Crash Severity Model Handling Class Imbalance and Implementing Ordered Nature: Focusing on Elderly Drivers

Abstract

Along with the rapid demographic change, there has been increased attention to the risk of vehicle crashes relative to older drivers. Due to senior involvement and their physical vulnerability, it is crucial to develop models that accurately predict the severity of senior-involved crashes. However, the challenge is how to cope with an imbalanced severity class distribution and the ordered nature of crash severities, as these can complicate the classification of the severity of crashes. In that regard, this study investigates the influence of implementing ordinal nature and handling imbalanced class distribution on the prediction performance. Using vehicle crash data in Ohio, U.S., as an example, the eight machine learning classifiers (logistic and ordered logistic regressions and random forest and ordered random forest with or without handling imbalanced classes) are suggested and then compared with their respective performances. The analysis outcomes show that balancing strategy enhances performance in predicting severe crashes. In contrast, the effects of implementing ordinal nature vary across models. Specifically, the ordered random forest classifier without balancing appears to be superior in terms of overall prediction accuracy, and the ordered random forest with balancing outperforms others in predicting severer crashes.

Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach

“…The number of licensed elderly drivers in the United States, for example, climbed by 65% during the period of 1997 to 2018 [4]. Moreover, the injury and death risk among older drivers involved in automobile accidents continues to rise; indeed, the fatality rates for older drivers and their passengers are ranked higher in crash severity than any other road accident fatalities [5][6][7][8][9][10]. The Insurance Institute for Highway Safety [11] has found that drivers over the age of 80 have about a 0.658% chance of being killed in a car accident, which is almost four times greater than that for those between the ages of 30 and 39 (0.137%).…”

Assessing School Travel Safety in Scotland: An Empirical Analysis of Injury Severities for Accidents in the School Commute

“…The involvement of elderly drivers (older than 60 years old) is also associated with more severe injuries in accidents on urban roads. The vulnerability of elderly drivers and their higher risk exposure to severe injuries have been long recorded in previous studies [39,40]; despite the low proportion of elderly drivers that is shown in Table 1, their propensity to serious or fatal outcomes is confirmed when they are involved in accidents during school-related trips. Severe injuries were also more likely to be sustained in accidents that occurred under dark ambient conditions.…”

Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques

“…Given the risks associated with elderly driving, it is important to understand the factors that contribute to crash severity in this population. Previous research has identified several such factors, including driver-related factors, such as age, gender, medical history, and driving experience [15,16], as well as environmental factors, such as road conditions, traffic patterns, and weather conditions [17]. However, despite these efforts, the identification of the most influential factors remains an area of ongoing investigation.…”