Applying the heteroskedastic ordered probit model on injury severity for improved age and gender estimation.

OBJECTIVE: Driver characteristics have been linked to the frequency and severity of car crashes. Among these, age and gender have been shown to impact both the possibility and severity of a crash. Previous studies have used standard ordered probit (OP) models to analyze crash data, and some research has suggested heteroskedastic ordered probit (HETOP) could provide improved model fit. The objective of this paper is to evaluate potential improvements of the heteroskedastic ordered probit (HETOP) model compared to the standard ordered probit (OP) model in crash analysis, by examining the effect of gender across age on injury severity among drivers. This paper hypothesizes that the HETOP model can provide a better fit to crash data, by allowing heteroskedasticity in the distribution of injury severity across driver age and gender. METHODS: Data for 20,222 crashes were analyzed for North Carolina from 2016 to 2018, which represents the state with the highest number of fatalities per 100 million vehicle miles traveled amongst available crash data from the Highway Safety Information System. RESULTS: Darker lighting conditions, severe road surface conditions, and less severe weather were associated with increased injury severity. For driver demographics, the probability of severe injuries increased with age and for male drivers. Moreover, the variance of severity increased with age disproportionately within and across genders, and the HETOP was able to account for this. CONCLUSIONS: The results of the two applied approaches revealed that HETOP model outperformed the standard OP model when measuring the effects of age and gender together in injury severity analysis, due to the heteroskedasticity in injury severity within gender and age. The HETOP statistical method presented in this paper can be more broadly applied across other contexts and combinations of independent variables for improved model prediction and accuracy of causal variables in traffic safety.

User perceptions of automated Truck-Mounted attenuators: Implications on work zone safety.

OBJECTIVE: Automated Truck-Mounted Attenuators (ATMAs) have the potential to improve work zone safety by removing the human driver out of a vehicle that is positioned in work zones to absorb impact from errant vehicles. However, this automated technology is expensive and can be detrimental to safety and project success if operated incorrectly (e.g., operating limitations and procedures not followed). Therefore, it is important to understand users' perceptions of ATMAs and how training can improve appropriate adoption of this technology. The objective of this study was to evaluate how work zone workers perceive the usefulness of and the capabilities of automation in Truck-Mounted Attenuators. METHODS: A survey study was conducted with 13 Department of Transportation (DOT) workers in Colorado and California. Each of the DOT workers in this study had some previous experience with the ATMA, either in real-world applications and/or formal training. The survey collected information on participant job specifications, experience with the ATMA, training received, trust in the ATMA, usability of the HMIs, and operating capabilities of the automation. RESULTS: Workers reported an overall positive acceptance of this technology. This was supported by their expectation that it would reduce crash severity; that there was a reasonable workload associated with operating procedures for the automation; and by their overall trust in the automation's reliability. However, workers noted concerns regarding their trust in the automation under various contexts, such as poor visibility and denser traffic volumes. Further, trust in the technology was greatest among workers with higher levels of ATMA training and longer experience working with the ATMA. CONCLUSIONS: This research presents a novel perspective on user acceptance of ATMA technology. These findings can help jurisdictions achieve the safety improvements that investment and deployment of automation in work zones offers, by identifying the disconnect between operators and technology.