Speeding and impaired driving in fatal crashes-Results from in-depth investigations

Speeding behavior and associated factors in Buenos Aires, Argentina

OBJECTIVE

Excessive speed is a major risk factor for serious injuries and death. However, speeding remains a pervasive problem around the world. This study aimed to investigate the factors associated with speeding behavior in the city of Buenos Aires, Argentina.

METHODS

A sample of vehicles (n = 34,967) from ten locations in the city was observed in two waves during 2021. Measurements were made at different times and days of the week. Observation sites were free of intersections, traffic lights, speed bumps and cameras, allowing drivers to speed freely. Data on speed, drivers and vehicle types were collected. Factors associated with speeding were identified through logistic regression analyses.

RESULTS

15.3% of vehicles were observed to be speeding. Roads with posted speed limits of 40 km/h showed higher speeding compared to 60 km/h roads. 77% of vehicles traveled above 30 km/h on local roads, and 30% above 50 km/h on avenues. Motorcycles, both commercial and private, showed a higher percentage of speeding compared to all other vehicles. Speeding was lower among women, among adults over 60 years of age, and among those using cell phones.

CONCLUSION

It is crucial to strengthen strategies for increased compliance with speed limits. Actions targeting motorcyclists must be a priority.

Do crashed drivers need more drug testing? A retrospective analysis of blood samples from hospitalised post-crash drivers in New Zealand

Driving under the influence of alcohol and other drugs is a prominent safety concern in New Zealand and across the world. While alcohol testing is routinely performed for drivers involved in hospitalisation crashes, testing for other drugs is often not undertaken. The present study refers to 530 traffic crashes that occurred from October 2019 to January 2020 on New Zealand roads. The blood samples from 550 drivers who were injured in a crash and were admitted to a hospital (66% of all drivers involved in these crashes), previously tested for drugs and/or alcohol, were retested for a wider range of drugs. Alcohol above the applicable limit was found to be present in 38% of hospitalised drivers, while other drugs of interest were found in 47% of hospitalised drivers. Binary logistic regression was used to predict the presence of drugs of interest for a crashed driver using previous offence data. A driver having at least one prior drink and drug driving offence is 61% more likely to be positive for a drug of interest when involved in a crash. Similarly, a driver having at least one prior non-traffic drug offence is 4.7 times more likely to be positive for at least a drug of interest when involved in a crash. While the presence of a drug or drugs cannot be presumed to have played a role in the occurrence of the crash, this study has provided a unique and comprehensive picture of the presence of various drugs present in New Zealand drivers' blood. It is recommended to consider standardising drug testing on all blood specimens taken in relation to a serious injury or fatal crash. This procedure is not only of interest for information purposes but may importantly inform appropriate charging decisions.

Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

The Five "I" Framework of crash investigation: Linking investigation practices to safety reform

Crash investigation is vital to the advancement of road safety, and it is a foundation for determining effective countermeasures and interventions for true road safety reform. However, the way in which road crash investigations are conducted substantially influences the quality of understanding and the effectiveness of responses. In crash investigations, we traditionally focus first on the question: "What caused this crash?" when it would be more efficient to ask immediately: "What could have prevented this crash?" or better yet, "What are all the ways this crash could have been prevented?" In this paper, we first explore a few common road crash investigation approaches where prosecution, retribution, or compensation are primary. We then examine investigation approaches where prevention is primary, especially investigations aimed at determining every point on the timeline preceding the crash where an intervention would have prevented the outcome, as illustrated by the Swiss Cheese Model. We draw from examples from the aviation industry, the occupational health and safety field, the U.S. National Transportation Safety Board (NTSB), and others, to identify strengths and weaknesses. We bring together good practices from several investigative approaches through the lens of diverse experiences in transportation safety and root-cause analysis to present a practical and proactive framework for road crash investigation. The Five "I" Framework provides guiding characteristics for prevention-focused safety investigations for road crashes: Immediate, In-Depth, Impartial, Independent, and Injury Prevention. The Five "I" Framework is a practical guide for investigations to move beyond crash causation to crash prevention, aligning with the Safe Systems Approach, Vision Zero, and the public health perspective. Rather than focusing primarily on on any single factor such as vehicle defect or driver error, it leads investigations to an array of countermeasures that involve collective action and systems change, and thus, to more effective road safety reform. Nevertheless, as a practical framework, it is the start (not the end) of discussions on how we can continue to move towards more multidisciplinary, collaborative, innovative, and ethical prevention efforts.

Characteristics of crash-involved drink and drug car drivers and motorcyclists

OBJECTIVE

Drug driving is an issue of growing concern among Australian jurisdictions, including South Australia. In order to have an appropriate response to drug driving in regard to policy and enforcement, it is important to have sound knowledge about the patterns of drug use among motorists and the associated risks of this behavior.

METHOD

To this end, this study examined the characteristics of 1,277 hospitalized road users in South Australia in the years 2014 to 2017, with reference to whether or not they tested positive for alcohol or other drugs (the three drugs tested for according to the South Australian Road Traffic Act (1961): THC, methamphetamine, MDMA). This examination used a database combining hospital data, police-reported crash data, licensing information, and the result of alcohol and drug tests conducted by Forensic Science SA.

RESULTS

It was found that more motorists were positive for one or more of the three illicit drugs than for alcohol, reversing long established trends. Comparisons were made between alcohol and drug free motorists, alcohol positive motorists, drug positive motorists, and motorists positive for both drugs and alcohol, with drivers of cars and motorcyclists analyzed separately where possible. Findings include: that those combining drugs and alcohol only comprise a small proportion of hospitalized motorists; that drug drivers tend to be younger than other hospitalized motorists; that drivers are more likely to test positive to alcohol or methamphetamine, while motorcyclists are more likely to test positive to THC; motorists testing positive to drugs and/or alcohol tend to exhibit a range of other risky behaviors; and motorists testing positive to drugs and/or alcohol tend to sustain more severe injuries in the event of a crash.

CONCLUSIONS

Preventing drink and drug driving is important not only for reducing crash numbers but also injury severity in the event of a crash. Substance use differs by road user type and age, which has potential implications for enforcement practices.

Analysis of driver-injury severity: a comparison between speeding and non-speeding driving crash accounting for temporal and unobserved effects

This paper aims to investigate the differences between temporal stability of factors influencing driver-injury severities in crashes involving speeding and non-speeding driving using a six-year (2012-2017) crash data in Thailand. With two possible driver injury severity outcomes (no/minor and severe/fatal), random parameter binary logit models, that allow for heterogeneity in means and variances, were estimated to fully account for unobserved heterogeneities (i.e., allow crash-level factors to vary across crashes and to influence random parameter distribution). While most factors were unstable over time, speeding crash models result showed that stable factors decreasing probability of severe/fatal injury were restraint, van, passenger car, pickup truck, running-off-road on straight and hitting guardrail and mounting traffic island; whereas stable factor increasing probability of severe/fatal injury were central/eastern/southern regions. In non-speeding driving crash model, stable factors decreasing probability of severe/fatal injury were restraint, truck, and running-off-road on straight and hitting guardrail; whereas stable factors increasing probability of severe and fatal injury were under influence of alcohol and van. The findings of this research could potentially be utilized to improve highway safety and facilitate the development of more effective crash injury mitigation policies. Practical-related recommendation based on the results is also provided.

Evaluating the effect of drunk driving on fatal injuries among vulnerable road users in Taiwan: a population-based study

Background

Most studies have focused on injuries sustained by intoxicated drivers themselves, but few have examined the effect of drunk driving on injury outcomes among VRUs (vulnerable road users) in developing countries. This study aims to evaluate the effect of drunk driving on fatal injuries among VRUs (pedestrians, cyclists, or motorcyclists).

Methods

The data were extracted from the National Taiwan Traffic Crash Dataset from January 1, 2011, to December 31, 2019. Crashes involving one motorized vehicle and one VRU were considered. This study examines the effect of drunk driving by estimating multivariate logistic regression models of fatal injuries among VRUs after controlling for other variables.

Results

Among 1,416,168 casualties, the fatality rate of VRUs involved in drunk driving was higher than that of general road users (2.1% vs. 0.6%). Drunk driving was a significant risk factor for fatal injuries among VRUs. Other risk factors for fatal injuries among VRUs included VRU age ≥ 65 years (adjusted odds ratio [AOR]: 5.24, 95% confidence interval [CI]: 5.53–6.07), a nighttime accident (AOR: 4.52, 95% CI: 4.22–4.84), and being hit by a heavy-duty vehicle (AOR: 2.83, 95% CI: 2.26–3.55). Subgroup analyses revealed a linear relationship between driver blood alcohol concentration (BAC) and the risk of fatal injury among motorcyclists. Motorcyclists exhibited the highest fatality rate when they had a BAC ≤ 0.03% (AOR: 3.54, 95% CI: 3.08–4.08).

Conclusion

Drunk driving was associated with a higher risk of fatality for all VRUs. The risk of fatal injury among motorcyclists was linearly related to the BAC of the drunk drivers. Injuries were more severe for intoxicated motorcyclists, even those with BAC ≤ 0.03%, which is within the legal limit.

Estimate of the magnitude of risky and protective behaviors associated with road traffic injuries in capitals participating in the Life in Traffic Project of Brazil

BACKGROUND

Brazil occupies the fifth position in the ranking of the highest mortality rates due to RTI in the world. With the objective of promoting traffic safety and consequently reducing deaths, Brazil created the Life in Traffic Project (LTP). The main goal of LTP is reducing 50% of RTI deaths, by promoting interventions to tackle risk factors, such as driving under the influence of alcohol and excessive and/or inappropriate speed. Thus, the aim of this study was to estimate the magnitude of risky and protective factors for RTI in capitals participating in the LTP in Brazil. We estimated these factors according to sociodemographic (age group, sex, education, race and, type of road user).

METHODS

A total of 5,922 car drivers and motorcyclists from 14 Brazilian capitals participating in the LTP were interviewed. Data collection was carried out in sobriety checkpoints at night and consisted of the administration of an interview and a breathalyzer test. Risky and protective behaviors associated with RTI were investigated. Covariates of the study were: age, sex, education, race and, type of road user. Poisson multiple regression analysis was used to assess the relationship between variables of interest.

RESULTS

The prevalence of individuals with positive blood alcohol concentration (BAC) was 6.3% and who reported driving after drinking alcohol in the last 30 days was 9.1%. The others risky behaviors reported were: driving at excessive speed on roads of 50 km/h, using a cell phone for calls while driving, using a cell phone to send or read calls while driving, running a red light. Use of seatbelts and helmets showed prevalence above 96,0% Use of seatbelts showed prevalence of 98.6% among car drivers, and helmet use was described by 96.6% of motorcycle drivers. Most risky behaviors were more prevalent in younger age groups (except BAC measurement higher in older participants), in males (except for cell phone use), in participants with higher education level and without a driver's license.

CONCLUSION

Excessive speed and driving under the influence of alcohol, defined as priorities within the LTP, need more consistent interventions, as they still have considerable prevalence in the cities investigated. The factors described such as cell phone usage and passing red traffic lights should also need to be prioritized as a focus on promoting traffic safety.

Capturing long-memory properties in road fatality rate series by an autoregressive fractionally integrated moving average model with generalized autoregressive conditional heteroscedasticity: A case study of Florida, the United States, 1975-2018

INTRODUCTION

Time series models play an important role in monitoring and understanding the serial dynamics of road crash exposures, risks, outcomes, and safety performance indicators. The time-series methods applied in previous studies on crash time series analysis assume that the serial dependency decays rapidly or even exponentially. However, this assumption is violated in most cases because of the existence of long-memory properties in the crash time series data. Ignoring the long-memory dependency could result in biased understanding of the dynamics of road traffic crashes.

METHOD

To fill this research gap, this study proposes an autoregressive fractionally integrated moving average model with generalized autoregressive conditional heteroscedasticity (ARFIMA-GARCH) to capture and accommodate the long-memory decencies in the road fatality rate time series. To further investigate how the factors influencing the fatality risks play a role in the long-memory dependence, the effects of exogenous variables are examined in this study. The analysis is conducted based on the road crash fatality data in Florida, USA over 44 years. Results' Conclusions: The case analysis confirmed the existence of long-memory property in the crash fatality time series data by both the joint tests of Augmented Dickey-Fuller and the Phillips-Perron, and the integer order of differencing (≤0.5) in the proposed models. The model results reveal that gasoline price and alcohol consumption per capita is positively associated with road fatality risks, whereas unemployment rate and rural/urban road mileage are negatively related to the road fatality risks.

PRACTICAL APPLICATIONS

The significant influential factors are also found to account for the long-memory serial correlations between road traffic fatalities to some extent.

Road Safety Policy in Addis Ababa: A Vision Zero Perspective

In this article, the Addis Ababa city road safety policies are examined and analysed based on the Vision Zero approach to road safety work. Three major policy documents are explored and assessed in terms of how they compare with Vision Zero policy in Sweden, concerning how road safety problems are conceptualised, the responsibility ascriptions promoted, the nature of goal setting concerning road safety objectives, and the specific road safety interventions promoted. It is concluded that there is a big difference between the Swedish Vision Zero approach to road safety work and the Addis Ababa road safety approach in terms of how road safety problems are framed and how responsibility ascriptions are made. In Addis Ababa, policy documents primarily frame road safety problems as individual road user problems and, hence, the responsibility for traffic safety is mainly left to the individual road users. The responsibility extended to other system components such as the vehicles, road design, and the operation of the traffic is growing but still very limited. It is argued that in order to find and secure long-term solutions for traffic safety in the city, a paradigm shift is needed, both regarding what are perceived to be the main causes of road safety problems in the city and who should be responsible for ensuring that road fatalities and injuries are prevented.

A systematic review of factors associated with illegal drug driving

Drug driving is a serious problem worldwide that can increase the risk of road crashes. This systematic review seeks to identify factors associated with drug driving (i.e., driving after consuming drugs other than alcohol) to highlight gaps in existing knowledge and inform the design of more effective countermeasures. A search of the literature was conducted for the period January 1, 2005 to July 31, 2021 using six different databases. The search protocol followed PRISMA guidelines and was registered in PROSPERO (#CRD42021234616). Studies that met inclusion criteria compared drug drivers with either non-drug drivers, alcohol-only drivers or drug drivers from an earlier time period, to identify factors specifically associated with drug driving, rather than common to all drivers. Two hundred and nineteen publications met the inclusion criteria and were included within the review. Based on the findings, a logic model was developed that presents the factors associated with drug driving. Various sociodemographic, psychosocial and legal factors emerged as the main factors associated with illegal drug driving. At the sociodemographic and psychological levels, drug drivers were more likely to be single, young males who often drive after using cannabis and who score high on sensation-seeking and impulsivity scales. The key social factor found to be associated with drug driving was peer acceptance/disapproval of the behaviour. At the legal level, the review suggested that the effectiveness of current enforcement approaches to drug driving vary among jurisdictions around the world due to differences in the level of perceived certainty of apprehension and the chances of punishment avoidance. Future research into the anticipated and actual rewards for drug driving is needed to inform the development of more effective countermeasures.

Analysis of Driver’s Socioeconomic Characteristics Relating to Speeding Behavior and Crash Involvement: A Case Study in Lahore

Speeding is one of the risky behaviors which results in accident involvement causing fatalities and severe injuries. This paper aimed to identify the significant socio-economic characteristics of drivers concerning their speeding behavior and crash involvement. A questionnaire was designed consisting of driver’s demographic features, involvement in an accident, penalty on speed violations, and statements on speeding behavior in terms of exceeding the speed limits by 10 km/h or more on roads with different speed limits of 60, 80, 100, and 120 km/h per standard operating speeds on various road types in Pakistan. This survey was conducted in Lahore city and a total of 551 usable samples were obtained. A latent variable of drivers’ speeding behavior was introduced; factor loadings were estimated, and an observed variable of drivers’ crash experience was defined as the drivers’ crash involvement. Ordered regression analysis using the probit function was conducted on speeding behavior and crash involvement. The ordinal analysis revealed that the drivers’ age, gender, marital status, employment, vehicle engine size, type of vehicle they drive, and driving frequency per day are good predictors of speeding behavior. Similarly, male drivers’ age, vehicle engine size, and type of vehicle they drive were significant predictors of their likelihood to be involved in an accident. The young, single, and male drivers and drivers of cars with an engine capacity above 1.5 L were more likely to speed and be involved in crashes. These findings provide a clear understanding of a specific group of drivers who have a higher probability of speeding and crash involvement. There is a need to focus on specific demographic factors in the formulation of traffic safety policies and managing speedy drivers’ behaviors.

Young adults in motor vehicle collisions in Norway: user safety errors observed in majority of cases with severe or fatal injuries

PURPOSE

We performed a multidisciplinary investigation of young adults involved in motor vehicle collisions (MVCs) to elucidate injury mechanisms and the role of passive safety equipment such as seat belts and airbags.

METHODS

MVCs resulting in death or serious injuries to the driver or passengers aged 16-24 years in southeastern Norway during 2013-2016 were investigated upon informed consent. We assessed the crash scene, the motor vehicle (MV) interior and exterior, and analyzed data from medical records, forensic autopsies and reports from police and civil road authorities.

RESULTS

This study included 229 young adult occupants involved in 212 MVCs. The Maximum Abbreviated Injury Scale (MAIS) score was ≥2 in 111 occupants, of which 22 were fatalities. In 59% (65/111) of the cases with MAIS score ≥2 injuries, safety errors and occupant protection inadequacies were considered to have contributed to the injury outcome. Common errors were seatbelt non-use and misuse, carrying insecure luggage, and the seat back being too reclined. MAIS score ≥2 head/neck injuries were observed in side impacts despite correct seatbelt use, related to older MVs lacking side airbag curtains. The independent risk factors for MAIS score ≥2 injuries included not using a seatbelt, driving under the influence of alcohol or drugs, nighttime driving, side impacts, heavy collision partner, and MV deformation.

CONCLUSION

User safety errors (not using a seatbelt, seatbelt misuse, excessive seat-back reclining, and insecure cargo) and a lack of occupant protection in older MVs resulted in young adults sustaining severe or fatal injuries in MVCs.

Alcohol and Alcohol Combined with Texting: Evaluation of Driving Impairment Effects in a Closed-Course Section

OBJECTIVE

Examine the driving impairment effects of alcohol alone and of alcohol combined with texting.

METHODS

Fifteen drivers (nine male, six female; mean age: 31.1 ± 6.9 years, range: 23 to 43 years) with similar drinking habit (i.e., social drinkers) completed a lap in a closed-course section in six different situations: (I) sober; (II) sober and while texting; (III) 30 minutes after ingesting a moderate dose of ethanol (0.50 g/kg); (IV) 30 minutes after drinking and while texting; (V) 60 minutes after drinking, (VI) 60 minutes after drinking and while texting. Driving performance was analyzed by means of maximum and mean speed, braking time and braking distance; and ability to control the car (i.e., evaluating if the drivers hit a traffic cone or exceeded the boundaries of the course). P values of < 0.05 were considered significant.

RESULTS

Pre and post-alcohol consumption results show a significant increase concerning the drivers' mean and maximum speed after drinking (p < 3.2x10^-8). However, neither alcohol nor texting had significant effects on braking parameters (p > 0.05). Traffic cones were knocked down only in texting experiments. In addition, when using the cell phone drivers tended to reduce the speed, and to accelerate abruptly right after they finish texting.

CONCLUSION

Our findings strengthen the hypothesis that even moderate alcohol doses may significantly impair the driving performance. Additionally, alcohol and texting have complementary effects on driving impairment, and their combination represents a significant risk factor for crashes.

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.