Traffic-related Fatalities Research Articles

Factors Affecting Crash Severity on Two Major Intercity Roads in Western Sri Lanka: A Random Parameter Logit Approach

Although Sri Lanka has significant traffic-related fatalities and injuries, only a limited number of studies have focused on the road safety challenge. This study applies the random parameters logit model to police reported crash data to identify the factors contributing to the severity of crashes in Sri Lanka. We find that severe crashes are associated with roadways that are unlit, in rural areas and have traffic controls, dual purpose vehicles, heavy vehicles, head-on and hit pedestrian crashes, drivers who are unlicensed, and casualties who are older (65 years and older) and do not use safety equipment, while minor crashes are associated with rear-end crashes, and female or younger (aged 25 years or younger) casualties. Several engineering, enforcement, and education measures are recommended based on the risk factors identified.

Examining Factors Contributing to Motorcycle Collisions with Left-Turning Vehicles at Urban Intersection Locations

Motorcycle crashes account for a significant proportion of traffic-related fatalities on U.S. roadways. Compared with motor vehicles, motorcycles traveling straight ahead are more susceptible to collisions with left-turning vehicles at intersections (note – in a system where traffic travels on the right-hand side of the road). The limited knowledge of the causes and influences of this specific type of crash deters efforts to improve motorcycle safety and is partly influenced by two issues. First, significant variables are unknown; second, motorcycles comprise a small proportion of vehicles in the traffic stream. This study sought to understand the factors that may contribute to the disproportionate crash risk left-turning vehicles pose for motorcyclists while accounting for the imbalance of vehicle proportions. Data containing motorcycle-motor vehicle and motor vehicle-motor vehicle crashes involving left-turning motor vehicles at intersections in South Florida were collected from 2015 to 2017. The study applied logistic regression on a balanced dataset generated using the random oversampling technique. The proposed model improved the predictive accuracy and enabled the identification of factors contributing to motorcycle crashes with left-turning vehicles. A Bayesian network analysis was also applied to the balanced data to analyze the interrelationship of factors associated with motorcycle crashes with left-turning vehicles. Results indicated that the type of intersection and traffic control, time of day, age of drivers, sex of the motorcyclist, roadway type, and weather were significantly associated with motorcyclists’ susceptibility to collisions with left-turning vehicles. Recognizing these attributes could help devise engineering measures and policies for promoting motorcycle safety.

An evaluation of a Vision Zero traffic-calming intervention, an urban transportation safety policy

ABSTRACT Today over 40 major metropolitan areas in the United States have formally adopted a new “Vision Zero” road design framework to prioritize roadway safety and eliminate traffic-related fatalities. This policy framework diffused across much of Europe and, more recently, the United States. In 2019, the City of San Francisco, California, implemented a series of traffic-calming interventions designed to lower speeds on left-hand turns and thereby reduce severe pedestrian and bicyclist injuries. This study uses a quasi-experimental design to evaluate this innovation. Results show that these safety measures reduced speeds between 1.4 and 1.9 miles per hour and that these reductions were sustained for at least 1 year. A conservative cost-benefit analysis suggests that these interventions have a large, positive return on investment, and that they are justified for any intersection with 2.5 or more pedestrian- or cyclist-involved crashes over a 5-year window.

Can I Stop? Considering Opportunities to Influence Multidisciplinary Factors That Result in Crashes

In previous decades significant gains were made in reducing the number of road traffic related fatalities in Victoria and Australia. However, in absolute terms this reduction started plateauing despite substantial investment in road safety countermeasures. This trend is not well aligned with the goal to achieve zero fatalities by 2050. Through practical traffic management experience, a study tour of nine countries and a literature review, we have gained valuable insights into the changing dynamics of roads. We have identified opportunities to improve road safety by adapting to larger/heavier vehicles, denser traffic, changing travel patterns and evolving demographics. These opportunities include: vehicle-centred crash avoidance in addition to crash consequence mitigation strategies; reduced traffic complexity; reduced cognitive workload; and targeted dynamic information of inclement weather and traffic conditions. Improved road surface friction, braking, tyres and vehicle stability can reduce crashes and their impacts. This article aims at stimulating broader discussions among experts for the development of next-generation road safety strategies.

Predictors of older people’s intention to engage in cycling violation behaviour with an integrative model

In China, bicycles are a popular mode of transportation for senior citizens. A disproportionate number of traffic-related fatalities and injuries involve cyclists. The violation of cycling laws is a significant cause of cyclist crashes. Few studies have analyzed the cycling violation behaviour of seniors. Therefore, it is essential to examine the factors that influence older individuals’ intention to engage in cycling violation behaviours. In this study, the effects of social-demographic characteristics, the exogenous constructs in the health belief model (HBM), and the theory of planned behaviour (TPB) on senior cyclists’ violation intention were investigated using hierarchical regression analysis. Interviews were conducted with older cyclists in urban areas of Wuhan City, all above 60 years of age. The results showed that very little variance in behavioural intention could be explained by social-demographic factors. The TPB has a significantly greater capacity than the HBM to explain variance in behavioural intention. Perceived susceptibility, perceived benefit, cues to action, subjective norm and attitude significantly impacted behavioural intention, whereas perceived severity, perceived barrier and self-efficacy did not.

Public support of street projects to improve traffic safety in unincorporated Los Angeles County: Developing an action plan to eliminate traffic-related fatalities

ObjectiveTo better understand the roadway needs of unincorporated Los Angeles County (LAC), the present study examined public support for and potential barriers to implementing Vision Zero (VZ) in the region. MethodsAn 18-question survey on traffic safety and VZ planning was administered to 736 participants recruited at community meetings and events held in unincorporated LAC. ResultsThe survey response rate was 92% among those who were approached. The average age of the participants was 49 years, with more than two-thirds (69%; n = 506) identifying as female, 58% as Hispanic/Latino, 19% as White, 7% as Black, and 7% as Asian. The top three reported traffic safety concerns were: (1) speeding (85%), (2) people ignoring traffic laws while driving (63%), and (3) distracted driving (61%). Almost three-quarters (74%) believed people who needed traffic safety education the most are those who drive regularly, as compared to those who walk, bike, or ride a motorcycle. Most participants (92%) reported they would support efforts to implement VZ street projects, with 87% willing to increase their commute time (one-way). ConclusionsStudy findings on traffic safety attitudes and behaviors among residents of unincorporated LAC who attended a community meeting provided insights into ways in which the County of Los Angeles can more efficiently plan and implement VZ street projects. Practical applicationsPublic support and trade-offs in commute time by residents are often required to make VZ implementation a success. Local jurisdictions should be mindful of these government-public support dynamics and invest in community engagement to better prepare residents of unincorporated/rural areas for these VZ street projects and related roadway interventions.

Road Accidents in India: Looking Through the Behavioral Lens

While connecting people and their lives, roads also have a cost on them. It is clear that one of the major causes of death worldwide is traffic-related fatalities. In 2016, 1.35 million persons worldwide lost their lives in traffic accidents, 90% of which occurred in poorer nations (World Health Organization, 2018). One of the top causes of death for children between the ages of 5 and 29 is traffic-related injuries. More people lose their lives as a result of automobile accidents than from diarrheal illnesses or tuberculosis. The government has made a lot of efforts and implemented many mitigation techniques both locally and globally, but infrastructure, enforcement, and behavioural understanding of road safety are still lacking. The 50% reduction in the number of traffic accidents outlined in Sustainable Development Goal 3.6 is far from adequate. So, this study aims to provide an overview of road accidents at the global and national levels as well as behavioural attributions and changes in people's behaviour for road safety. Key words: Road accidents; behavioural intervention; road safety; traffic accidents; SDG2030

COVID-19 in Vietnam and its impact on road trauma: A national data review.

Despite significant improvement in the last decade, road trauma remains a significant contributor to deaths in Vietnam. The COVID-19 pandemic necessitated public health measures which had an unforeseen benefit on road trauma in high income countries. We investigate if this reduction was also seen in a low to middle income country, Vietnam. Our aim was to investigate how the COVID-19 pandemic, and the government policies to address it, impacted on road trauma fatalities in Vietnam. We also compared this impact with other government policies intended to road trauma in the preceding 14 years (2007 to 2020). COVID-19 data was extracted from the Vietnamese Ministry of Health (MoH) database. Road traffic deaths from 2007 to 2021 were derived from the Vietnamese General Statistical Office (GSO). We used Stata software 17 for statistical analysis. Poisson regression modelling, with the logarithm of the national population of Vietnam of each year included as an offset, was used to estimate trends in rates of road fatalities from the annual national mortality data for the period 2007-2021. The actual change in road traffic mortality in 2021 was compared with calculated figures to demonstrate the effect of COVID-19 on road trauma fatalities. We also compared this impact with other government road toll reduction policies for 14 years (2007 to 2020). Between 2007 and 2020 the number of annual road traffic deaths had more than halved from 15.3 to 7.0 per 100,000, an average reduction of 5.4% per annum. We estimated that the road traffic mortality rate declined by 12.1% (95% CI 8.9 to 15.3%) in 2021 relative to trend. The actual number of road trauma deaths fell by 16.4%. This reduction was largely seen from August to October 2021 when lockdown and social distancing were in force. In 2021, the road traffic death reduction in Vietnam was 3 times greater than the trend over the preceding 14 years. The public heath response to the COVID-19 pandemic in Vietnam was associated with a third of this reduction. It can thus be reasonably concluded that government policies to address the COVID-19 pandemic resulted in a 4.3% decrease of road traffic deaths in 2021. This has been observed in High-Income Countries but was demonstrated by us for the first time in a Low- and Middle-Income Country.

Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

Thailand has been on the World Health Organization (WHO)’s notorious deadliest road list for several years, currently ranking eighth on the list. Among all types of road fatalities, pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures, such as safety belts. Passenger overloading is illegal, but it is often overlooked. The country often uses police checkpoints to enforce traffic laws. However, there are few or no highway patrols to apprehend offending drivers. Therefore, in this study, we propose the use of existing closed-circuit television (CCTV) traffic cameras with deep learning techniques to classify overloaded public transport pickup trucks (PTPT) to help reduce accidents. As the said type of vehicle and its passenger occupancy characteristics are unique, a new model is deemed necessary. The contributions of this study are as follows: First, we used various state-of-the-art object detection YOLOv5 (You Only Look Once) models to obtain the optimum overcrowded model pretrained on our manually labeled dataset. Second, we made our custom dataset available. Upon investigation, we compared all the latest YOLOv5 models and discovered that the YOLOv5L yielded the optimal performance with a mean average precision (mAP) of 95.1% and an inference time of 33 frames per second (FPS) on a graphic processing unit (GPU). We aim to deploy the selected model on traffic control computers to alert the police of such passenger-overloading violations. The use of a chosen algorithm is feasible and is expected to help reduce traffic-related fatalities.

Analysis of pedestrian violations based on an integrative model: Evidence from college students in China

IntroductionWalking is a popular mode of transportation for college students. A disproportionate number of traffic-related fatalities and injuries involve pedestrians. The violation of traffic laws is a significant cause of traffic crashes. Few studies have analyzed the walking violation behavior of Chinese college students. Therefore, it is essential to examine the factors that influence college students’ intention to engage in walking violation behaviors. MethodsThree models were used to analyze college students’ walking violations. In Model 1, the theory of planned behavior (TPB) was analyzed; In Model 2, the health belief model (HBM) was analyzed; In Model 3, the integrative model that considered exogenous constructs in the TPB, exogenous constructs in the HBM, and descriptive norm was analyzed. ResultsThe HBM has a moderately larger power than the TPB in explaining behavioral intention's variance, and the integrative model has a slightly larger power than the HBM in explaining behavioral intention's variance. Attitude, subjective norm, perceived severity, perceived benefit, and descriptive norm have significant impacts on behavioral intention to violate traffic rules, while perceived behavioral control, perceived susceptibility, perceived barrier, and cues to action do not have significant impacts. ConclusionsThe findings of this study could be used to explain pedestrian violation behaviors among Chinese college students and encourage them to obey traffic rules.

The Association among Alcohol Consumption Patterns, Drink-Driving Behaviors, and the Harm from Alcohol-Related Road Traffic Injuries Due to the Drinking of Others in Thailand

Thailand has one of the highest rates of traffic-related fatalities and alcohol-related road traffic injuries globally. Previous studies focused on alcohol consumption and road traffic injuries. However, no existing studies investigate the association between drink-driving behaviors and road traffic injuries due to the drinking of others. This study aims to explore any potential associations among alcohol drinking patterns, drink-driving behaviors, and the harm from alcohol-related road traffic injuries due to the drinking of others. The Thai Tobacco and Alcohol Use Household National Survey data in 2017 (n = 80,797) were analyzed using multiple logistic regression. This study found that current drinkers and binge drinkers were more likely to suffer from road traffic injuries due to others' drink-driving behavior, i.e., 1.50 times (95% CI: 1.49-1.51) and 2.31 times (95% CI: 2.30-2.33), respectively, compared with non-drinkers. In addition, we found that drink-driving behavior was associated with harm from road traffic injuries due to others' drink-driving behavior by 2.12 times (95% CI: 2.10-2.14) compared with the non-drinker group. This study calls for effective measures to reduce drink-driving behaviors to prevent road traffic injuries due to the drinking of others.

The kids aren’t alright: The effects of medical marijuana market size on adolescents

We exploit shocks to US federal enforcement policy to assess how legal medical marijuana market size affects youth marijuana use and consequences for youth traffic-related fatalities. Using hand-collected data on state medical marijuana patient rates to develop a novel measure of market size, we find that legal market growth increases youth marijuana use. Likely mechanisms are lower prices and easier access. Youth die more frequently from alcohol-involved car accidents, suggesting complementarities for youths. The consequences of marijuana legalization for youth are not immediate, but depend on how supply-side regulations affect production and prices.

Skilled maneuvering: Evaluation of a young driver advanced training program.

BACKGROUNDYoung drivers (YDs) are disproportionately injured and killed in motor vehicle crashes throughout the United States. Nationally, YDs aged 16 to 20 years constituted nearly 9% of all traffic-related fatalities in 2018. A Nevada Advanced Driver Training (ADT) program for YDs aims to reduce YD traffic injuries and fatalities through four modules taught by professional drivers. The program modules include classroom-based didactic lessons and hands-on driving exercises intended to improve safe driving knowledge and behaviors. The overarching purpose of this study was to determine if the Nevada ADT program achieved its objectives for improving safe driving knowledge and behaviors based on program-provided data. A secondary purpose of this study was to provide recommendations to improve program efficiency, delivery, and evaluation. The findings of this study would serve as a basis to develop and evaluate future ADT interventions.METHODSThe exploratory mixed methods outcome evaluation used secondary data collected during three weekend events in December 2018 and March 2019. The study population consisted of high school students with a driver’s license or learner’s permit. Pretests/posttests and preevent questionnaires on student driving history were matched and linked via personal identifiers. The pretests/posttests measured changes in knowledge of safe driving behaviors. This study used descriptive statistics, dependent samples t test, Pearson’s r correlation coefficient, and χ2 (McNemar’s test) with significance set at p = 0.05, 95% confidence interval. Statistical analysis was conducted using IBM SPSS version 24 (Armonk, NY). Qualitative data analysis consisted of content and thematic analysis.RESULTSResponses from YD participants (N = 649) were provided for analysis. Aggregate YD participant knowledge of safe driving behaviors increased from a mean of 43.9% (pretest) to 74.9% (posttest).CONCLUSIONThe program achieved its intended outcomes of improving safe driving knowledge and behaviors among its target population.LEVEL OF EVIDENCEPrognostic/Epidemiologic, Level V.

Evaluation of Protected Left-Turn Phasing and Leading Pedestrian Intervals Effects on Pedestrian Safety

Pedestrian safety is an important public health issue for the United States, with pedestrian fatalities representing approximately 16% of all traffic-related fatalities in 2016. Nationwide, transportation agencies are increasing their efforts to implement engineering-based improvements that increase pedestrian safety. These agencies need statistically rigorous crash modification factors (CMFs) to demonstrate the safety effectiveness of such countermeasures, and to apply in benefit–cost analyses to justify their implementation. This study focused on developing CMFs for two countermeasures that show promise for improving pedestrian safety: protected or protected/permissive left-turn phasing, and leading pedestrian intervals (LPIs). Data were acquired from four North American cities that had installed one or both of the countermeasures of interest: Chicago, IL; New York City, NY; Charlotte, NC; and Toronto, ON. The empirical Bayes before–after study design was applied to estimate the change in expected crash frequency for crashes following treatment. The protected left-turn phasing evaluation showed a benefit in reducing vehicle–vehicle injury crashes, but did not produce statistically significant results for vehicle–pedestrian crashes. For those crashes a disaggregate analysis did reveal that this treatment could be especially beneficial where pedestrian volumes exceed 5,500 per day. The LPI evaluation showed a statistically significant reduction in vehicle–pedestrian crashes with an estimated CMF of 0.87.

Uber and traffic safety: Evidence from Brazilian cities

In this paper we estimate the causal effect of the introduction of Uber, an app designed to provide urban transportation services, on traffic-related fatalities and hospitalizations in Brazilian cities. Exploiting the staggered rollout of the company across cities, our research applies a difference-in-differences design and an event study specification using quarterly panel data from 2011 to 2016. Our results indicate that Uber’s introduction reduced by roughly 10% and 17%, respectively, traffic fatality and hospitalization rates in Brazilian cities. The results are robust to different specifications and falsification tests, and to the best of our knowledge, are the first estimates of this effect in a developing country.

Impact of lockdown and change in mobility patterns on road fatalities during COVID-19 pandemic

ABSTRACT The present study examines the impact of lockdown measures taken to control the spread of COVID-19 on road fatalities. The study is based on the data collected over the duration of six months for fifteen countries based on severity of COVID-19. Lockdown stringency and six daily mobility measures were selected to study the change in road mobility patterns due to lockdown measures. The fifteen countries were categorized into two clusters (C1 and C2) and generalized linear mixed model was considered for analysis. The model results revealed that stringent lockdown measures and high residential mobility reduced Crash Fatality Ratio (CFR) by 0.36% and 1.84%, respectively in cluster C2. Further, in cluster C1, travel restrictions on workplace and recreational activities decreased CFR up to 0.31% and 0.26%, respectively. Thus, overall model results specified that stringent law enforcement and appropriate compliance from the road users can effectively lessen traffic-related fatalities in unprecedented times.

Understanding Police Officers’ Usage of High-Visibility Safety Apparel: The Role of Safety Ethics and Professional Appearance

High-visibility safety apparel (HVSA) is personal protective clothing that provides visual conspicuity to reduce police officers’ risk of fatality or injury from road traffic accidents. Under the current U.S. Department of Transportation’s Federal Highway Administration regulations, police officers on or near Federal-Aid highways are mandated to wear HVSA to enhance their visibility. Although wearing HVSA can significantly prevent traffic-related fatalities among police officers, studies have shown that they do not consistently, or ever, wear it. This study identifies factors influencing HVSA noncompliance among officers by exploring their perceptions of its use and efficiency. Ninety-eight police officers completed a field survey in four cities in Yavapai County, Arizona; their responses were analyzed via predictive decision tree modeling. The results indicated that officers’ HVSA-wearing behavior can be predicted by safety ethics, professional appearance, occupation risk, functionality of HVSA, and safety education. Among these predictors, safety ethics and professional appearance were the most important factors in forecasting officers’ HVSA-wearing behavior. Our study contributes to enhancing knowledge about the psychological aspects of officers’ HVSA-wearing behavior and provides implications for improving their traffic safety policies and compliance in the law enforcement community.

Relationship Between Mobility and Pedestrian Traffic Safety in India

Pedestrians are the most vulnerable road users. In India and other developing countries, pedestrian fatalities constitute a significant proportion of traffic-related fatalities. As these countries’ economies grow and their populations become more mobile, there may be repercussions for pedestrians. While improved mobility is generally considered a positive outcome of economic growth, its impact on pedestrian traffic safety has not been studied in detail, especially at a region-wide level. Since pedestrian behavior and vehicle ownership characteristics in low-and middle-income countries are substantially different than in high-income countries, it is necessary to explore the relationship between mobility and pedestrian traffic safety in the context of a middle-income country. In this study, India serves as a case study to explore such a relationship. A time-series regression methodology and mobility indices were used to quantify regional mobility over time. The results suggest that improvements in mobility are detrimental to pedestrian traffic safety in India. This study should emphasize to decision-makers the importance of investing in safety features for pedestrians, whose needs have been neglected for decades, often in favor of motorized transport.

Alcohol and illicit substances associated with fatal crashes in Queensland: An examination of the 2011 to 2015 Coroner’s findings

ProblemThe problem of impaired driving is well documented in the literature but is heavily dependent upon self-report studies and/or databases that do not include in-depth information about the contributing origins of fatalities. AimThis study aimed to conduct an in-depth analysis of Coroner’s findings for all fatally injured drivers in the state of Queensland in order to explore the prevalence of alcohol and different types of illicit substances (including drug combinations) in fatal crash reports. MethodA total of 701 Coroner’s reports related to drivers or controllers of vehicles involved in traffic related fatalities for the period of 2011–2015 were analysed, revealing 306 controllers (43.6%) were detected with either alcohol or illegal drugs (e.g., methylamphetamine, Δ9-tetrahydrocannabinol, cocaine or MDMA) ResultsAlcohol was the most commonly detected substance identified with 223 cases (72.9% of the drug and alcohol sample). Illicit drug detections totalled 147 cases (48% of the drug and alcohol sample) with Δ9-tetrahydrocannabinol the most commonly detected illicit substance (109 cases; 35.6% of the drug and alcohol sample) followed by methylamphetamine (total of 63 cases; 20.6% of the drug and alcohol sample). An important theme to emerge was the prevalence of polysubstance use among fatally injured drivers, not just for alcohol and one drug type, but also multiple drug combinations. Fatality trends revealed a decrease in both non-substance and alcohol-related fatalities across the study period. However, road fatalities where an illicit substance was detected increased by approximately 57%. Males were overrepresented as a proportion of total fatalities (82.4%) and there were no significant sex or age differences regarding illicit substance related deaths. Drivers of passenger vehicles were most commonly identified in the data (66.2%), but motorcycle operators were disproportionately represented (28.1% of the total controller sample compared to 4% of vehicle registrations in Queensland) ConclusionThis case study analysis of fatal crashes not only confirms the ongoing problem of alcohol and driving, but also illuminates the emerging (and escalating) issue of illicit substances detected in fatally injured drivers.

Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.