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The search for common and serious single causes of road crashes naturally leads to a concentration on the road user. This is supported by a legal framework in the search for the main cause and the suspect for this cause. In prevention, we havefor decades been more inclined to look for systematic improvements of all elements of the road transport system, and we direct the recommendations for actions towards system designers, organizations, products and services. In this paper the discussion about causation and prevention is broadened in the light of Vision Zero and its approach to prevention of serious and fatal injuries. We also discuss the Swedish judicial system and why the prevention approach has not been legislated or even generally accepted. Occupational health and safety legislation and road rules are compared, as well as how sustainability practices and reporting are tools to apply prevention where organizations have a natural sphere of influence that could mitigate deaths and serious injuries within value chains. It is recommended that we stop using the term causation as it is only directing actions in one direction. There is a risk that the focus on causation, in particular single causes, will deviate actions away from robust prevention countermeasures such as increased seat belt use, relevant speed limits, and well functioning roundabouts and median barriers. Furthermore, there is also a risk that important preventative actions from organizations are overlooked., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Problem: Electronic Stability Control (ESC) is believed to be among the most efficient vehicle safety interventions with reported effects around 50% for fatal single and rollover crashes. However, such estimates have used sample data, which have not controlled for the possibilities of self-selection, behavioral adaptation, increased access to the technology by less safe drivers, and the calculation of effects on very specific categories of crashes. Effects of ESC in the population can therefore be expected to be smaller than is currently believed., Method: National U.S. data for fatal crashes, driving exposure and other control factors, and market penetration of ESC over 1991-2021 were used to calculate whether the trends in fatalities over time in crash rates for singles, rollovers, and fatal crashes in general matched projections from estimates of effectiveness., Results: It was found that downward trends in the relevant crash types were generally present before ESC was introduced, and that the trends thereafter were weaker. Although some trends were consistent with effects of ESC, they were markedly smaller than the projected ones, and could be explained by other factors such as the number of vehicles per capita. At best, the effect for rollovers could be up to two-thirds of previous estimates, no effect was detected for singles, while for all fatal crashes results depended upon the type of analysis performed. These results conflict with conclusions in all published ESC crash sample studies, which have compared vehicles with and without ESC. This discrepancy can be explained by methodological errors in the previous studies using induced exposure methods and self-selected samples., Practical Applications: Traffic safety may not be as much improved by technological interventions as believed. Alternative approaches to traffic safety are needed, which do not rely on technology that interferes with driver behavior., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Unmeasured confounding can lead to biased interpretations of empirical findings. This paper aimed to assess the magnitude of suspected unmeasured confounding due to driving mileage and simulate the statistical power required to detect a discrepancy in the effect of polypharmacy on road traffic crashes (RTCs) among older adults., Methods: Based on Monte Carlo Simulation (MCS) approach, we estimated 1) the magnitude of confounding of driving mileage on the association of polypharmacy and RTCs and 2) the statistical power of to detect a discrepancy from no adjusted effect. A total of 1000 studies, each of 500000 observations, were simulated., Results: Under the assumption of a modest adjusted exposure-outcome odds ratio of 1.35, the magnitude of confounding bias by driving mileage was estimated to be 16% higher with a statistical power of 50%. Only an adjusted odds ratio of at least 1.60 would be associated with a statistical power of about 80% CONCLUSION: This applied probabilistic bias analysis showed that not adjusting for driving mileage as a confounder can lead to an overestimation of the effect of polypharmacy on RTCs in older adults. Even considering a large sample, small to moderate adjusted exposure effects were difficult to be detected., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Online media reports provide valuable information for road traffic injury prevention, but technical challenges concerning data acquisition and processing limit analysis and interpretation of such data. Integrating injury epidemiology theory and big data technology, we developed a data platform consisting of four layers (data acquisition, data processing, application and data storage) to automatically collect reports from online Chinese media concerning road traffic crashes every 24 h. We built a text classification model using 20,000 manually annotated news stories based on the Bidirectional Encoder Representations from Transformers (BERT) and then used natural language processing algorithms to extract data concerning 27 structured variables from the news sources. The accuracy of the BERT-based text classification model was 0.9271, with information extraction accuracy exceeding 80% for 22 variables. As of November 30, 2021, the data platform collected 244,650 eligible media reports covering all 333 prefecture-level divisions in China. These reports were from 37,073 websites or social media accounts, which were geographically located in all 31 provinces and over 98% of prefecture-level divisions. Data availability varied greatly from 0.9% to 100% across the 27 structured variables. Additionally, the platform identified 645,787 potentially relevant keywords when applying natural language processing techniques to the textual media reports. Platform data were highly correlated with road police data in province-based road traffic crash statistics (crashes, r s  = 0.799; non-fatal injuries, r s  = 0.802; deaths, r s  = 0.775). In particular, the platform offers valuable data (like crashes involving electric vehicles) that are not included in official road traffic crash statistics. The new automated data platform shows great potential for timely detection of emerging characteristics of road traffic crashes. Further research is needed to improve the platform and apply it to real-time monitoring and analysis of road traffic injuries., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Introduction: In 2007, the German legislature introduced a zero tolerance law (ZTL) for novice drivers to reduce the number of alcohol-related crashes. The purpose of our study was to evaluate the long-term effects of this law on current and former novice drivers., Method: Our approach was threefold: first, we used individual data of police records from 2003-2018 and conducted a cohort analysis to examine how the first cohort affected by the law responded in the long-term. Second, we analyzed the influence of the ZTL on alcohol-related traffic offenses by current novice drivers. Third, we conducted a survey to examine if the acceptance, knowledge, and behavior regarding the ZTL have changed compared to a decade ago., Results: The number of alcohol-related crashes was significantly lower in the first affected cohort than in earlier cohorts. Moreover, current novice drivers had lower levels of alcohol-related crashes and alcohol-related traffic offenses than did novice drivers before the ZTL became effective. The survey showed a high level of acceptance and knowledge in both current and first cohort and a decreased importance of drinking and driving., Conclusion: The ZTL is associated with a long-term increase of traffic safety in Germany. Former novice drivers appear to have retained learned behavior toward drinking and driving. Thus, the ZTL might have an impact on perceived norms resulting in less acceptance of drinking and driving. Changes in society, like lower alcohol consumption and decreased importance of passenger cars among young people, further accelerated these effects. Practical applications: ZTL for novice drivers are an effective way to improve traffic safety. It is associated with a positive effect on traffic safety even when drivers were no longer directly affected by the measure. These findings suggest that policies are an effective tool to improve traffic safety and help towards achieving Vision Zero., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase of injury severity of its interveners when involved in a road crash. Determining such factors is essential to help decision making in road safety management, improving road safety, and reducing the severity of future crashes., Method: This paper presents a recent literature review of the methods that have been applied to road crash injury severity modeling. It includes 56 studies from 2001 to 2021 that consider more than 20 different statistical or machine learning techniques., Results: Random Forest was the algorithm with the best results, achieving the best performance in 70% of the times that it was applied and in 29% of all studies. Support Vector Machine and Decision Tree achieved the best performance in 53% and 31% of the times and in 16% and 14% of all studies, respectively. Bayesian Networks and K-Nearest Neighbors achieved the best performance in 67% and 40% of the times that were used but only achieved the best performance in 4% and 7% of all the studies analyzed, respectively., Conclusions: At this point, Random Forest revealed to be a good approach for road traffic crash injury severity prediction followed by Support Vector Machine, Decision Tree, and K-Nearest Neighbor. However, there is still a lot of room in this area to explore other techniques that can best suit this purpose as not only the model's performance should be considered but also causality issues, unobserved heterogeneity, and temporal instability. Practical Applications: This review enables researchers to understand the recent techniques applied in the analysis of injury severity modeling, and the ones that achieved the best performance results. Based on the reviewed studies, challenges and future research directions are presented., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 National Safety Council and Elsevier Ltd. All rights reserved.)

In many road crashes the human body is exposed to high forces, commonly resulting in multiple injuries. This study of linked road crash data aimed to identify co-occurring injuries in multiple injured road users by using a novel application of a data mining technique commonly used in Market Basket Analysis. We expected that some injuries are statistically associated with each other and form Individual-Based Injury Patterns (IBIPs) and further that specific road users are associated with certain IBIPs. First, a new injury taxonomy was developed through a four-step process to allow the use of injury data recorded from either of the two major dictionaries used to document anatomical injury. Then data from the Swedish Traffic Accident Data Acquisition, which includes crash circumstances from the police and injury information from hospitals, was analysed for the years 2011 to 2017. The injury data was analysed using the Apriori algorithm to identify statistical association between injuries (IBIP). Each IBIP were then used as the outcome variable in logistic regression modelling to identify associations between specific road user types and IBIPs. A total of 48,544 individuals were included in the analysis of which 36,480 (75.1%) had a single injury category recorded and 12,064 (24.9%) were considered multiply injured. The data mining analysis identified 77 IBIPs in the multiply injured sample and 16 of these were associated with only one road user type. IBIPs and their relation to road user type are one step on the journey towards developing a tool to better understand and quantify injury severity and thereby improve the evidence-base supporting prioritisation of road safety countermeasures., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

We propose a novel linear model of pedestrian safety in urban areas with respect to road traffic crashes that considers a single independent variable of pedestrian path safety. This variable is estimated for a given urban area by sampling pedestrian paths from the population of such paths in that area and in turn estimating the mean safety of these paths. We argue that this independent variable directly models the factors contributing to pedestrian safety. This contrasts previous approaches, which, by considering multiple independent variables describing the environment, traffic and pedestrians themselves, indirectly model these factors. Using data about 15 UK cities, we demonstrate that the proposed model accurately estimates numbers of pedestrian casualties., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The current study tests an innovative public participation process for designing and implementing a tailored traffic enforcement program in minority localities. The quasi-experiment used two matched pairs of randomly selected Israeli Arab localities, where one locality in each pair was randomly assigned to the experimental group and the other to the control group. The intervention's main features were the public participation process and implementation by police of the traffic enforcement program designed during the process. Systematic field observations on 12,236 vehicles in the four localities found a meaningful and significant reduction in traffic violations in the experimental localities following the intervention, while a small increase in violations was observed in the control localities. The most meaningful decline, indicating improvement in drivers' behavior, was in non-use of seatbelts and small children in the front seat. The study suggests that a public participation process which identifies local road traffic problems and "dark" hot spots (places where offenses and risky behavior recur but might not be known to the police), followed by implementing tailored solutions for these problems, can reduce traffic violations. Future research should aim to separate out the independent effects of the two phases (the public participation process and tailored enforcement)., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

One of the major problems in the world today is the rate of road traffic crashes and deaths on our roads. Majority of these deaths occur in low-and-middle income countries including Nigeria. This study analyzed road traffic crashes in Anambra State, Nigeria with the intention of developing accurate predictive models for forecasting crash frequency in the State using autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modelling techniques. The result showed that ARIMAX model outperformed the ARIMA (1,1,1) model generated when their performances were compared using the lower Bayesian information criterion, mean absolute percentage error, root mean square error; and higher coefficient of determination (R-Squared) as accuracy measures. The findings of this study reveal that incorporating human, vehicle and environmental related factors in time series analysis of crash dataset produces a more robust predictive model than solely using aggregated crash count. This study contributes to the body of knowledge on road traffic safety and provides an approach to forecasting using many human, vehicle and environmental factors. The recommendations made in this study if applied will help in reducing the number of road traffic crashes in Nigeria., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

This paper examines the determinants of road traffic crash fatalities in Queensland for the period 1958-2007 using a state-space time-series model. In particular, we investigate the effects of policies that aimed to reduce drink-driving on traffic fatalities, as well as indicators of the economic environment that may affect exposure to traffic, and hence affect the number of accidents and fatalities. The results show that the introduction of a random breath testing program in 1988 was associated with a 11.3% reduction in traffic fatalities; its expansion in 1998 was associated with a 26.2% reduction in traffic fatalities; and the effect of the "Safe4life" program, which was introduced in 2004, was a 14.3% reduction in traffic fatalities. Reductions in economic activity are also associated with reductions in road fatalities: we estimate that a one percent increase in the unemployment rate is associated with a 0.2% reduction in traffic fatalities., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: The severity of disability related to road traffic crashes has been little studied, despite the significant health and socio-economic impacts that determine victims' quality of life., Objective: To estimate the consequences of road traffic crashes on the severity of disability, in terms of individuals' capacity to execute activities and perform tasks in their current environment, using aids., Methods: Cross-sectional study conducted on community-dwelling participants in the "2008 National Survey of Disability", with data on 91,846 households having 20,425 disabled persons, 443 of whom had disability due to road traffic crashes. We measured severity using two indicators, i.e., the Capacity (CSI) and Performance (PSI) Severity Indices., Results: The highest proportion of disability was mild (CSI=70.5%; PSI=80.8%), while 7.6% (CSI) and 4.9% (PSI) was severe/complete respectively. The moderate/severe disability rate was 0.6 per thousand on the CSI, decreasing to 0.4 per thousand on the PSI. No differences were observed by age or sex. Moderate/severely disabled persons had a fourfold higher probability of being retired or unfit for work. Mental and nervous system impairments were more closely related to moderate/severe/complete problems of capacity and performance (p<0.001), disability for carrying out general tasks and demands, and interpersonal interactions and relationships (p<0.001). Being permanently bedridden (p<0.001), receiving aids (p<0.001), family support (p<0.05) and moving home (p<0.05) increased with an increase in the level of severity., Conclusion: Road traffic crashes mainly cause mild disability. Moderate/severe disability is associated with lower work capacity, greater functional dependence, and increased need of aids, moving home and family support., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Objective: To assess the population impact of chronic conditions on the risk of road traffic crashes., Methods: Data from three French national databases were extracted and matched: the national health care insurance database, police reports and the national police database of injurious crashes. Exposure to chronic conditions were compared between responsible and nonresponsible drivers. Analysis was performed using the Lasso (least absolute shrinkage and selection operator) method., Results: 69,630 drivers involved in an injurious crash in France between 2005 and 2008, were included. 6210 (8.9%) were suffering from at least one long-term disease. When adjusted for prescription of medicines, blood alcohol, demographic driver characteristics and crash characteristics, increased risk of being responsible for a crash was found in drivers registered in the French healthcare database with the following long-term diseases: epilepsy (odds ratio [OR]=2.53 [1.53-4.20]), type 1 diabetes (OR=1.47) [1.12-1.92], alcoholic liver disease (OR=3.37 [1.40-8.13]), asthma (OR=1.72 [1.13-2.60]) and specific personality disorders (OR=1.35 [1.05-1.74]). No association was found for cardiovascular diseases or Alzheimer's disease., Conclusion: The results update the list of medical conditions that may impair driving skills. However, results should be considered cautiously with regards to potential regulatory driving judgments that could have a negative impact on patients' social life., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Objective: Evaluating the relation between Iranian drivers' knowledge, attitude, and practice (KAP) regarding traffic regulations, and their deterministic effect on road traffic crashes (RTCs)., Setting: Two cities of Tehran and Zahedan, Iran., Methods: A cross-sectional study was designed. Using a simplified cluster sampling design, 2200 motor vehicle drivers including 1200 in Tehran and 1000 in Zahedan were selected. Sixty locations in Tehran and 50 in Zahedan were chosen. In each pre-identified location, 20 adult drivers were approached consecutively. A questionnaire developed by researchers was filled by each participant. The questionnaire had four sections including items assessing the demographics, knowledge, attitude and practice of drivers toward traffic regulations. Logistic regression analysis was used to evaluate the relationship between the RTCs and KAP variables., Results: The study sample consisted of 619 (28.1%) occupational and 1580 (71.8%) private drivers. Among them, 86.4% were male. The median age was 33.6 ± 10.83. Drivers in Tehran and Zahedan had no significant differences between their mean scores of KAP items of the questionnaire. Higher knowledge, safer attitude, and safer practice were associated with a decreased number of RTC. After adjusting for possible confounders, increase of one standard deviation in attitude and practice scores (but not knowledge) resulted in 26.4% and 18.5% decrease in RTC, respectively. Finally, considering knowledge, attitude and practice of drivers in one model to assess their mutual effect, it was shown that only attitude is significantly associated with a decrease of RTC (OR=0.76, P=0.007)., Conclusion: Increase in attitude and practice accompanied with decreased number of RTCs in Iranian drivers. Specifically, drivers' attitude had the crucial effect. It is not knowledge and standard traffic education; rather it is how such education is registered as an attitude that translates what is being learned into actions. Without safer attitude, even safer self-reported practice will not result in lower RTCs., (Copyright © 2014 Elsevier Ltd. All rights reserved.)