Risk factors for being the at-fault driver: A case-control study

Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data

Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors. A fusion deep learning approach combining a convolution neural network (CNN) and gated recurrent units (GRU) was developed to compare predictive performance with one econometric approach, two machine learning approaches, and another deep learning approach. The performance comparison was conducted for (1) at-fault crash driver frequency prediction tasks and (2) city-level crash risk prediction tasks. The proposed CNN-GRU achieved remarkable prediction accuracy and outperformed other approaches, while the other approaches also exhibited excellent performances. The results suggest that effective prediction approaches and appropriate traffic safety measures can be developed by considering both crash frequency and crash risk prediction tasks. In addition, the accumulated local effects (ALE) plot was utilized to investigate the contribution of each traffic enforcement activity on traffic safety in a scenario of multicollinearity among predictors. The ALE plot illustrated a complex nonlinear relationship between traffic enforcement predictors and the response variable. These findings can facilitate the development of traffic safety measures and serve as a good foundation for further investigations and utilization of traffic violation data.

Prevalence of behavioural risk factors for road-traffic injuries among the Iranian population: findings from STEPs 2016

BACKGROUND

To achieve Sustainable Development Goal 3.6 in Iran, we need to have a comprehensive understanding of the distribution of risky behaviours regarding road-traffic injuries at national and sub-national levels. Little is known about the road-use vulnerability patterns of road-traffic injuries in Iran. The aim of this study is to describe the prevalence of self-reported human risk factors in road-traffic injuries using the findings from a large-scale cross-sectional study based on the World Health Organization's stepwise approach to surveillance of non-communicable diseases (STEPs).

METHODS

A cross-sectional survey study in 2016 assessed the road-use pattern and prevalence of risky behaviours of people more than 18 years old. In this study, we planned to recruit 31 050 individuals as a representative sample at national and provincial levels. In practice, 30 541 individuals (3105 clusters) from urban and rural areas of Iran were selected. Basic socio-demographic data, major behavioural risk factors such as seatbelt and helmet non-compliance, drunk driving and occupant in a car with a drunk driver were assessed through baseline interviews gathered through an Android tablet-based questionnaire.

RESULTS

The overall prevalence of seatbelt and helmet compliance was 75.2% (95% confidence interval: 74.7-75.7) and 13.9% (13.4-14.5), respectively, at the national level. The prevalence of risk-taking behaviours such as drink driving was 0.5% (0.4-0.6) and for being an occupant in a car with a drunk driver was 3.5% (3.2-3.8). At the provincial level, the highest age-standardized prevalence of seatbelt compliance (89.6%) was almost 1.5 times higher than the lowest provincial prevalence (58.5%). In 63% of provinces, the lowest prevalence of seatbelt compliance was observed among people aged 18-24 years old.

CONCLUSIONS

In Iran, existing disease-prevention and health-promotion programmes should be expanded to target vulnerable subgroups that have more prevalent human risk factors for road-traffic injuries. Further research is required to investigate the context-specific proximal human risk factors and vulnerability patterns in Iran.

Modeling and comparing injury severity of at-fault and not at-fault drivers in crashes

This paper examines and compares the effect of selected variables on driver injury severity of, both, at-fault and not at-fault drivers. Data from the Highway Safety Information System (HSIS) for the state of North Carolina was used for analysis and modeling. A partial proportional odds model was developed to examine the effect of each variable on injury severity of at-fault driver and not at-fault driver, and, to examine how each variable affects these two drivers' injury severity differently. Road characteristics, weather condition, and geometric characteristics were observed to have a similar effect on injury severity in a crash to at-fault and not at-fault drivers. Age of the driver, physical condition, gender, vehicle type, and, the number and type of traffic rule violations were observed to play a significant role in the injury severity of not at-fault drivers when compared to at-fault drivers in the crash. Moreover, motorcyclists and drivers 70 years or older are observed to be the most vulnerable road users.