Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Abstract

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach

“…The risk of fatal accident is increased for young novice drivers and decreased for old novice drivers. This is consistent with Xiao’s study [ 4 ]. They found that increasing age reduces the risk of fatal accidents for novice drivers.…”

“…By analyzing the eye movement data of novice and experienced drivers when driving on different types of roads, Underwood et al [ 14 ] found that compared with novice drivers, experienced drivers showed higher sensitivity on the whole. Xiao et al [ 4 ] used the bivariate random-effects probit model to analyze the influencing factors of accident severity for novice and experienced drivers, and they found that the key factors of fatal injury suffered by novice drivers and experienced drivers are different.…”

“…At present, many scholars have conducted research on accident analysis of drivers with different driving experience. Xiao et al [ 4 ] found that there is correlation between novice and experienced drivers for influencing factors of accident severity, but the correlation is not strong. Al Garawi’s study [ 5 ] of novice female drivers with different ages found no significant difference in accident rates among very young females, intermediate females, and older females.…”

Lateral stability regulation of intelligent electric vehicle based on model predictive control

“…Thanks to the evolution of the electronic and information technology, many advanced techniques can be applied to the intelligent vehicles, such as autonomous emergency braking and electronic stabilization programs (ESP), to enhance the safety issue. Thus, the vehicle quality can be improved and safety issue can be guaranteed, and then the traffic accident can be reduced (Liu et al, 2020;Xiao et al, 2021;Xu et al, 2020;Lyckegaard et al, 2015). Electric vehicle (EV), which has lower carbon emission, is an important industry in the world.…”