OBJECTIVE:Head-on crashes on undivided roads are particularly dangerous, often occurring at high impact speeds that may lead to fatal injury. This study examines the relationship between changes in vehicle velocity (delta-V) and fatality risk for head-on passenger vehicle crashes, which may assist regulators in setting safer speed limits for undivided roads. METHODS:A systematic review and meta-analysis was conducted to estimate the fatality risk by delta-V for head-on passenger vehicle crashes. Studies were included if they reported delta-V and occupant survival, and excluded unbelted occupants, crashes with multiple impacts or vehicle rollover, or simulated data. Random effects logistic regression with study-level random effects was used to estimate the fatality risk by delta-V, the change in fatality odds, and the delta-V corresponding to 1%, 5%, and 10% fatality risk. RESULTS:Five data sources met inclusion criteria including two published studies, two publicly available datasets, and a government dataset. The data included 6,247 casualties and 185 fatalities. The final random effects logistic regression model estimated a 6.0% increase in fatality odds for a 1 km/h increase in delta-V (OR: 1.06, 95% CI: 1.05, 1.07) with a 1% fatality risk at 23.3 km/h (95% CI: 14.2, 32.3), 5% fatality risk at 51.3 km/h (95% CI: 42.8, 59.8), and a 10% fatality risk at 64.0 km/h (95% CI: 55.4, 72.7). The fatality risk varied by region of data collection and more recent data estimates a similar increase in the odds of fatality for a 1 km/h increase in delta-V but an increase in delta-V for a 5% fatality risk (59.7 km/h, 95% CI: 51.0, 68.4). Similarly, there was an increase in delta-V for a 1% fatality risk (29.5 km/h, 95% CI: 18.5, 40.5) and for a 10% fatality risk (73.4 km/h, 95% CI: 63.5, 83.2). CONCLUSIONS:The survivability in a head-on crash has improved over time. The most recent data supports setting speed limits no higher than 60-70 km/h for a 5% fatality risk or 30-40 km/h for a 1% fatality risk on undivided roads which do not have a median road safety barrier installed to minimize the risk in a head-on crash fatality. For jurisdictions willing to accept a 10% fatality risk, the results suggest setting speed limits no higher than 70-80 km/h. The observed improvements in fatality risk may be due to recent advances in vehicle technologies, and some consideration should be given to low-to-middle income countries (LMIC) where such technologies may not be prevalent in their vehicle fleets.

Highlights Decrease fatalities and injuries in agricultural ATV incidents. Protect youth ATV riders in agricultural incidents. Use engineering controls to reduce agricultural ATV crashes. Comprehensive perspectives on ATV incidents (Australia, Canada, Israel, Sweden, and the USA). Abstract. All-terrain vehicles (ATVs) or quad bikes have raised serious concerns, especially in rural areas where they are used for occupation (i.e., agriculture and forestry) and recreation (i.e., hunting and recreational riding). ATVs are unstable vehicles, and their incidents have been linked to factors such as the rider’s physical capabilities (such as strength, anthropometry, and visual acuity) and behavior, safety awareness (training), application of personal protective equipment, lack of protective structure, and regulations. This manuscript presents perspectives of ATV safety experts from several countries, including Australia, Canada, Israel, Sweden, and the USA. The topics include the state of the art in youth riders, engineering control methods, stability, protective structures, safety rating systems, training and education, personal protection equipment, and new regulations. Keywords: Agriculture, ATV, Comprehensive perspective, Quad bikes, Safety, Youth.

Potential competing interests: No potential competing interests to declare.This paper certainly has merits, but it is not significant enough for publication as a peer-reviewed journal paper.It is more of a discussion paper about data sources.It might be appropriate for conference proceedings but contains little scientific novelty warranting publication.It mostly contains information that is already known to researchers and practitioners.There is some repetition in the paper, making it annoying to read in parts.For example, the first paragraph below the section 2 Literature Review header and the second paragraph on page 5.Another concern is that surveys and interviews were carried out without any ethics approval, or at least a statement concerning privacy issues, was not mentioned.To enhance the paper, some example analyses of data from one or two select regions in Zambia could have been looked at in detail, presenting actual values and comparisons between officially reported mortality data and actual fatalities.

Abstract Motorcyclists are the largest contributor to road fatalities in Indonesia, with the main causal factors found to be speeding and not wearing a helmet. One of the most efficient ways to reduce road deaths in Indonesia would be to reduce speeding behaviour and increase helmet wearing by strengthening speed and helmet wearing enforcement combined with education. Stronger enforcement combined with education have been shown to be highly effective in reducing fatalities in other countries. This study assesses the benefits of stronger enforcement on speed limit and helmet wearing. It also explores what are the most effective strategies to strengthen enforcement using fixed and mobile speed cameras, point to point speed cameras and AI technology that can be used to enforce helmet wearing rates. The paper concludes with an estimation of the potential benefits if such stronger enforcement were implemented in Indonesia.

Naturalistic driving data (NDS), collected from fitted vehicles with multiple sensors, including cameras, is a rich source to understand how drivers become distracted. However, detecting the distraction events traditionally required watching NDS videos which is expensive and time-consuming. Adopting state-of-the-art machine learning techniques to automate distraction detection using videos will save effort and money. Reducing/annotating a random sample of the videos and using it to train a machine learning model to carry out the data reduction for the rest of the dataset is possible. This paper investigates the feasibility of using pre-trained deep neural networks and random forest to detect driver distraction in the Australian Naturalistic Driving Study (ANDS) video data.The pre-trained model was fine-tuned using transfer learning; therefore, it can be used to classify (i.e. distracted/non-distracted) successive frames independently. To capture the time dependency between the consecutive images/frame during the training phase, we converted the 1-D output probability of the fine-tuned model into a higher dimension space. The distraction probability of each frame at a time is transformed into a vector by concatenating the distraction output probabilities of all frames within a time window centred at time. Moreover, the vector centred at the time was assigned the actual label of the frame at this time. Then we used Random Forest (RF) classifier to model the non-linear relationship between the probabilities in this new space and the distracted/non-distracted labels. This proposed framework was trained using some ANDS trips and tested using another set of trips. When applied to the dashboard camera, the proposed framework achieved 0.609, 0.218 and 0.325 for the true positive rate (TPR), the false positive rate (FPR), and the precision, respectively. Moreover, the face camera achieved 0.748, 0.344 and 0.651 for TPR, the FPR and the precision, respectively. These promising results suggest the proposed framework can be used routinely in many other data reduction tasks.

Perceived safety is recognized throughout the mode choice literature as a key barrier to cycling, yet its constructs are poorly understood. Although commonly understood to relate to crash and injury risk and sometimes vulnerability to crime, health impact assessments identify numerous other pathways through which cycling can negatively impact health. This study leverages a nationally representative survey of U.S. adults in 2022 to assess a set of eleven factors as potential components of perceived cycling safety. We use principal component analysis to identify components of perceived cycling safety and then employ principal component regression to assess these components in relation to predicting unsafe cycling perception. We identify five key dimensions of perceived safety. Specifically, we found that perceived bicycling safety can be encompassed in the following components: (1) contaminant exposure, (2) injurious collision risk, (3) street conditions, (4) weather conditions, and (5) crime risk. In evaluating each identified component, we found that injurious collision risk and street conditions were the most predictive of considering cycling as unsafe. We further develop an understanding of how differences in cycling behavior, such as using cycling for commuting purposes, may contribute to differences in how cycling safety components coalesce into perceived safety.

Naturalistic driving studies (NDS) are a method in transportation research that is increasingly used to bridge the gap between epidemiological research (e.g., using population crash databases) and individual level or experimental research (e.g., self-reported surveys or driving simulators). This article begins with defining NDS and providing a brief overview of NDS methods, including the strengths, limits and the unique ethical issues involved in conducting NDS. Following this, five case studies from Australia, Canada, China, the European Union, and the United States are presented, along with a synthesis of the lessons they have learned. The article concludes with a discussion of what the future of NDS may look like.

The Australian Census of Population and Housing includes a responder’s Method of Travel to Work for Persons (MTWP) on Census Day. With some exceptions, responders can select multiple modes of transport. In Australia and overseas, this data has been used to estimate mode share and the proportion of Australians who utilize various active transport modes. This is especially true for cycling as there are scant data sources for Australian cycling exposure. The aims of this paper are to discuss weaknesses of MTWP data and the appropriateness of MTWP data to estimate cycling in Australia, and to assess changes in MTWP data relative to the introduction of bicycle helmet legislation. The use of MTWP data to estimate Australian cycling is limited due to: (1) data collection occurring on single days in winter once every five years, (2) it is not possible to identify a primary mode of transport, and (3) the 1976 data was not a full enumeration. MTWP data estimates about 1.5% of Australians cycle while other data sources are much higher ranging from 10% to 36%. With regard to bicycle helmet legislation, comparisons were made for each state/territory for the census immediately preceding helmet legislation and the following census. Overall, the proportion of cyclists among active transport users is similar from pre- to post-legislation (relative change=+1%, 95% CI: -13%, +18%), although all but two states/territories estimate an increase in cycling. In conclusion, the Australian government should invest in routinely collecting high-quality mobility data for all modes of travel to assist in the decision-making and assessment of road safety policies.

Naturalistic driving research shows that drivers spend vast amounts of time engaging in secondary, non-driving tasks. Laboratory and simulation studies have demonstrated that, when engaging in a secondary task, drivers adopt strategies to interrupt, delay and resume the secondary task in order to manage their workload and risk. However, there is very little knowledge of the time-sharing strategies that drivers adopt for interweaving their attention across multiple tasks in real-world driving. This study examined the nature of observable visual and/or manual secondary task interruptions in real-word driving using naturalistic driving data. Video of 186 randomly selected trips from the Australian Naturalistic Driving Study were viewed to identify a range of secondary tasks and whether, when and why drivers interrupted engagement in these secondary tasks. It was found that under everyday naturalistic driving conditions, drivers interrupt (or temporarily disengage from) only a small percentage (13.5%) of the secondary tasks engaged in, with 87 percent of these tasks interrupted to re-engage in the driving task. The number of interruptions made to secondary tasks was found to differ according to a number of task characteristics, including task duration and visual load, with tasks of longer duration and higher visual load more likely to be interrupted. The results have a range of practical implications, particularly for the design of invehicle devices that better support drivers to break down long tasks into a series of sub-tasks, so that they can more easily disengage when driving demands necessitate.

There has historically been very little data on cycling in Australia. This lack of data has made it difficult to track whether cycling has changed over a long period of time. The number of cycling trips per day per person increased by 25.1% from the Day-to-Day Travel in Australia 1985/86 Survey to the 2011 National Cycling Participation Survey, while the Australian population 9 years of age and older has increased by 58.5%. The crude rate estimates a 20% reduction in cycling relative to population; however, this analysis does not account for changing Australian demographics during that time. When the rates of cycling are age-sex standardised, cycling trips in Australia increased by an estimated 11.0% (95% CI: 10.8%, 11.1%). The estimated increases in cycling trips, both in raw numbers and age-sex adjusted rates, support increased investments in cycling in Australia.

This article outlines a capacity review of Romania’s national road infrastructure and road safety in general. Romania’s road fatality rate per 100,000 population has improved overall from a 2008 high of around 15 to the current 2019 value of 9.6. However, the rate has flat-lined with no real improvement for the last decade, stalling at around 9.7 over the period 2011- 2019 and around double the EU rate. Moreover, Romania’s total annual number of road deaths has remained at an average of around 1900 fatalities per annum over this period. Romania has been the worst performing country in the European Union (EU) in recent years, and one of the worst performing countries compared to Organisation for Economic Co-operation and Development (OECD) nations in terms of road safety. The review performed in 2016 found inadequate political leadership and commitment to effective actions to reduce road fatalities, fragmented government road safety activities across a number of regulatory entities, speed limits set at levels that exceed internationally accepted survivable limits, weak traffic law enforcement including a lack of speed enforcement cameras resulting in a failure of drivers to comply with speed limits, and a lack of structured programs to implement human error tolerant road infrastructure constructed according to Safe System principles. A series of recommendations from the capacity review were adopted (as described here) since 2016, although much remains to improve road safety in Romania.

In this Special Issue, we encouraged authors to submit papers on road safety in LMICs for peer review. The four peer-reviewed papers cover: • Lack of pedestrian safety in Chennai, India; • Impact of density and urban design features on road safety outcomes in Bogota, Colombia • Availability and usage rates of seat belts in Malawi; and • Analyses of the context of speed management in Cambodia to improve implementation. In addition, we have three contributed articles: • Good practice road safety examples in LMICs; • Features of LMICs making road safety more challenging; and • Review of road safety management and infrastructure in Romania and recommended actions. We hope you find this Special Issue interesting and helpful in your work.

Within this exploratory study, data is presented regarding occupational and lifestyle factors that contribute to cardiovascular disease and depression within the truck driving industry, and subsequently contribute to reduced road safety in Australia. The study assessed associations between mood parameters, heart rate variability (HRV) and blood pressure in the unstudied population of Australian truck drivers. A total of 35 heavy vehicle truck drivers were recruited from the local community. Electrocardiogram recordings were obtained during an active and baseline driving simulator task. HRV low and high frequency parameters were obtained from the ECG. Subjects completed the Profile of Mood States questionnaire and the Lifestyle Appraisal Questionnaire. Blood pressure was recorded before and after the study. Numerous mood states (anger-aggression, total mood disturbance score) were correlated to an increase in sympathetic activity (p<0.05). Diastolic blood pressure was positively correlated to a number of mood states, the most significant correlation being depression-dejection (p<0.001).

A long-standing argument against bicycle helmet use is the risk compensation hypothesis, i.e., increased feelings of safety caused by wearing a helmet results in cyclists exhibiting more risky behaviour. However, past studies have found helmet wearing is not associated with risky behaviour, e.g., committing a traffic violation was positively associated with a lower frequency of helmet use. There is a lack of consensus in the research literature regarding bicycle helmet use and the risk compensation hypothesis, although this gap in knowledge was identified in the early 2000s. This is the first study to carry out a systematic review of the literature to assess whether helmet wearing is associated with risky behaviour. Two study authors systematically searched the peer-reviewed literature using five research databases (EMBASE, MEDLINE, COMPENDEX, SCOPUS, and WEB OF SCIENCE) and identified 141 unique articles and four articles from other sources. Twenty-three articles met inclusion criteria and their findings were summarised. Eighteen studies found no supportive evidence helmet use was positively associated with risky behaviour, while three studies provided mixed findings, i.e., results for and against the hypothesis. For many of these studies, bicycle helmet wearing was associated with safer cycling behaviour. Only two studies conducted from the same research lab provided evidence to support the risk compensation hypothesis. In sum, this systematic review found little to no support for the hypothesis bicycle helmet use is associated with engaging in risky behaviour. (C) 2018 The Authors. Published by Elsevier Ltd.

Using data from the Australian Naturalistic Driving Study (ANDS), this study examined patterns of secondary task engagement (e.g., mobile phone use, manipulating centre stack controls) during everyday driving trips to determine the type and duration of secondary task engaged in. Safety-related incidents associated with secondary task engagement were also examined. Results revealed that driver engagement in secondary tasks was frequent, with drivers engaging in one or more secondary tasks every 96 seconds, on average. However, drivers were more likely to initiate engagement in secondary tasks when the vehicle was stationary, suggesting that drivers do self-regulate the timing of task engagement to a certain degree. There was also evidence that drivers modified their engagement in a way suggestive of limiting their exposure to risk by engaging in some secondary tasks for shorter periods when the vehicle was moving compared to when it was stationary. Despite this, almost six percent of secondary tasks events were associated with a safety-related incident. The findings will be useful in targeting distraction countermeasures and policies and determining the effectiveness of these in managing driver distraction.

This paper examines the self-reported data from 1404 adult transport and recreational cyclists from New South Wales (Australia) on their experiences of behaviour they perceived to be intentionally aggressive in the previous week, from motor vehicle drivers (MVDs), pedestrians and other cyclists. The perception of aggression appears to be a common experience for cyclists, with about one in two cyclists reporting an aggressive encounter in the previous week. Most encounters (85.7%) were from MVDs, and most occurred on the road. After adjustment for exposure (time travelled) and environmental factors (proportion of cycling time on the road, and region in which most cycling was undertaken), younger cyclists (18-44 yrs), female cyclists and transport cyclists were more likely to report aggressive encounters from MVDs than older (60+ years), male, and recreational cyclists, respectively. The majority of cyclists who perceived aggression from a MVD attributed the behaviour to internal and stable factors in the MVD (such as an anti-cyclist, selfish or arrogant disposition or deliberately careless behaviour), followed by internal and unstable factors (such as ignorance of cyclists' rights or the road rules). These findings may have implications for initiatives by governments wishing to promote cycling. (C) 2019 Elsevier Ltd. All rights reserved.

BACKGROUND:Australian bicycle helmet laws were first introduced in Victoria in July 1990 and the remaining Australian states, Australian Capital Territory and Northern Territory by July 1992. Previous research on helmet legislation has focused on changes in helmet wearing and bicycle-related head injury. Although it is generally accepted that bicycle helmets can reduce the risk of fatality due to head injury, there has been little research assessing the impact of helmet legislation on cycling fatalities.METHODS:An interrupted time series approach was used to assess the impact of bicycle helmet legislation on yearly-aggregated rates of bicycle-related fatalities per population from 1971 to 2016.RESULTS:Immediately following bicycle helmet legislation, the rate of bicycle fatalities per 1 000 000 population reduced by 46% relative to the pre-legislation trend [95% confidence interval (CI): 31, 58]. For the period 1990-2016, we estimate 1332 fewer cycling fatalities (95% CI: 1201, 1463) or an average of 49.4 per year (95% CI: 44.5, 54.2). Reductions were also observed for pedestrian fatalities; however, bicycle fatalities declined by 36% relative to pedestrian fatalities (95% CI: 12, 54).CONCLUSIONS:In the absence of robust evidence showing a decline in cycling exposure following helmet legislation or other confounding factors, the reduction in Australian bicycle-related fatality appears to be primarily due to increased helmet use and not other factors.

Background: Pedestrians struck in motorised vehicle crashes constitute the largest group of traffic fatalities worldwide. Excessive speed is the primary contributory factor in such crashes. The relationship between estimated impact speed and the risk of a pedestrian fatality has generated much debate concerning what should be a safe maximum speed limit for vehicles in high pedestrian active areas. Methods: Four electronic databases (MEDLINE, EMBASE, COMPENDEX, and SCOPUS) were searched to identify relevant studies. Records were assessed, and data retrieved independently by two authors in adherence with the PRISMA statement. The included studies reported data on pedestrian fatalities from motorised vehicle crashes with known estimated impact speed. Summary odds ratios (OR) were obtained using meta-regression models. Time trends and publication bias were assessed. Results: Fifty-five studies were identified for a full-text assessment, 27 met inclusion criteria, and 20 were included in a meta-analysis. The analyses found that when the estimated impact speed increases by 1 km/h, the odds of a pedestrian fatality increases on average by 11% (OR = 1.11, 95% CI: 1.10-1.12). The risk of a fatality reaches 5% at an estimated impact speed of 30 km/h, 10% at 37 km/h, 50% at 59 km/h, 75% at 69 km/h and 90% at 80 km/h. Evidence of publication bias and time trend bias among included studies were found. Conclusions: The results of the meta-analysis support setting speed limits of 30-40 km/h for high pedestrian active areas. These speed limits are commonly used by best practice countries that have the lowest road fatality rates and that practice a Safe System Approach to road safety.