Examining road safety impacts of Green Light Optimal Speed Advisory (GLOSA) system.

Mobility and environmental benefits of Green Light Optimal Speed Advisory (GLOSA) systems have been reported by many previous research studies, however, there is insufficient knowledge on the safety implications of such an application. For safe deployment of GLOSA system, it is most critical to identify and address potential safety issues in the design process. It can be argued that implementation of GLOSA system can improve safety by reducing traffic conflicts associated with the interrupted traffic flow at signalised intersections. However, more research findings are needed from field and simulation based studies to evaluate the impacts on safety under a variety of real-world scenarios. As part of the LEVITATE (Societal Level Impacts of Connected and Automated Vehicles) project under European Union's Horizon 2020 Programme, the main objective of this study is to examine the safety impacts of GLOSA under mixed traffic compositions with varying market penetration rates (MPR) of connected and automated vehicles (CAVs). A calibrated and validated microsimulation model (developed in Aimsun) of the greater Manchester area was used for this study where three signalised intersections in a corridor were identified for implementing GLOSA system. An improved algorithm was developed by identifying the potential issues/limitations in some of the GLOSA algorithms found in literature. Behaviours of CAVs were modelled based on the findings of a comprehensive literature review. Safety analysis was performed through processing the simulated vehicular trajectories in the surrogate safety assessment model (SSAM) by the Federal Highway Administration (FHWA). The surrogate safety assessment results showed small improvement in safety with the GLOSA implementation at multiple intersections in the test network only at low MPR (20%) scenarios of CAVs, as compared to the respective without GLOSA scenarios. No or rather slightly lower improvement in safety was observed with GLOSA implementation under mixed fleet scenarios with 40 % or higher 1st Generation or 2nd Generation CAVs, as compared to the respective scenarios without GLOSA. The implementation of GLOSA system was also found to have some impact on the traffic conflict types (although not consistent across all MPR scenarios), where rear-end conflicts were found to decrease while a slight increase was observed in lane-change conflicts.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles.

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven - 40% 1st generation AVs- 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.

Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System.

The performance of five forecasting models was investigated for predicting nursery mortality using the master table built for 3242 groups of pigs (~13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model's performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE = 0.406), Mean Absolute Error (MAE = 0.284), and Coefficient of Determination (R2 = 0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2 = 0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (>5%) or low (<5%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking condition variables collected post-placement in nursery sites.

From conflicts to crashes: Simulating macroscopic connected and automated driving vehicle safety.

Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.

Risk factors associated with sow mortality in breeding herds under one production system in the Midwestern United States.

Sow mortality has significantly increased throughout the world over the past several years, and it is a growing concern to the global swine industry. Sow mortality increases economic losses, including higher replacement rates, affects employees' morale, and raises concerns about animal well-being and sustainability. This study aimed to assess herd-level risk factors associated with sow mortality in a large swine production system in the Midwestern United States. This retrospective observational study used available production, health, nutritional, and management information between July 2019 and December 2021. A Poisson mixed regression model was used to identify the risk factors and to build a multivariate model using the weekly mortality rate per 1000 sows as the outcome. Different models were used to identify the risk factors according to this study's main reasons for sow mortality (total death, sudden death, lameness, and prolapse). The main reported causes of sow mortality were sudden death (31.22 %), lameness (28.78 %), prolapse (28.02 %), and other causes (11.99 %). The median (25th-75th percentile) distribution of the crude sow mortality rate/1000 sows was 3.37 (2.19 - 4.16). Breeding herds classified as epidemic for porcine reproductive and respiratory syndrome virus (PRRSV) were associated with higher total death, sudden death, and lameness death. Open pen gestation was associated with a higher total death and lameness compared with stalls. Pulses of feed medication was associated with lower sow mortality rate for all outcomes. Farms not performing bump feeding were associated with higher sow mortality due to lameness and prolapses, while Senecavirus A (SVA)-positive herds were associated with a higher mortality rate for total deaths and deaths due to lameness. Disease interactions (herds Mycoplasma hyopneumoniae positive and epidemic for PRRSV; SVA positive herds and epidemic for PRRSV) were associated with higher mortality rates compared to farms with single disease status. This study identified and measured the major risk factors associated with total sow mortality rate, sudden deaths, lameness deaths, and prolapse deaths in breeding herds under field conditions.

User preferences, driving context or manoeuvre characteristics? Exploring parameters affecting the acceptability of automated overtaking.

Future user acceptance will be a requirement for the AVs to accomplish their estimated safety benefits, highlighting the importance of acceptable driving behaviour. This study aims to investigate the parameters that affect the acceptability of highly automated overtaking. 237 respondents participated in a video based online survey, rating different motorway flying overtaking scenarios based on their preferences. The scores were analysed using a variety of methods (statistical tests, Principal Component Analysis, Linear Mixed Models). Long pull-out distances and manoeuvre duration values, as well as lower speeds were preferred by the participants, with some limited impact of the driving situation. Overall, behaviour simulating an average, cautious human driver is likely to positively influence acceptability and suggests the value of further research on context-adaptive automated driving to account for subjective risk perception. These findings can contribute towards user-centred systems that assist or autonomously perform overtaking manoeuvres, supporting their uptake and thus the realisation of their safety benefits.

Utilizing productivity and health breeding-to-market information along with disease diagnostic data to identify pig mortality risk factors in a U.S. swine production system.

Aggregated diagnostic data collected over time from swine production systems is an important data source to investigate swine productivity and health, especially when combined with records concerning the pre-weaning and post-weaning phases of production. The combination of multiple data streams collected over the lifetime of the pigs is the essence of the whole-herd epidemiological investigation. This approach is particularly valuable for investigating the multifaceted and ever-changing factors contributing to wean-to-finish (W2F) swine mortality. The objective of this study was to use a retrospective dataset ("master table") containing information on 1,742 groups of pigs marketed over time to identify the major risk factors associated with W2F mortality. The master table was built by combining historical breed-to-market performance and health data with disease diagnostic records (Dx Codes) from marketed groups of growing pigs. After building the master table, univariate analyses were conducted to screen for risk factors to be included in the initial multivariable model. After a stepwise backward model selection approach, 5 variables and 2 interactions remained in the final model. Notably, the diagnosis variable significantly associated with W2F mortality was porcine reproductive and respiratory syndrome virus (PRRSV). Closeouts with clinical signs suggestive of Salmonella spp. or Escherichia coli infection were also associated with higher W2F mortality. Source sow farm factors that remained significantly associated with W2F mortality were the sow farm PRRS status, average weaning age, and the average pre-weaning mortality. After testing for the possible interactions in the final model, two interactions were significantly associated with wean-to-finish pig mortality: (1) sow farm PRRS status and a laboratory diagnosis of PRRSV and (2) average weaning age and a laboratory diagnosis of PRRS. Closeouts originating from PRRS epidemic or PRRS negative sow farms, when diagnosed with PRRS in the growing phase, had the highest W2F mortality rates. Likewise, PRRS diagnosis in the growing phase was an important factor in mortality, regardless of the average weaning age of the closeouts. Overall, this study demonstrated the utility of a whole-herd approach when analyzing diagnostic information along with breeding-to-market productivity and health information, to measure the major risk factors associated with W2F mortality in specified time frames and pig populations.

Whole-herd risk factors associated with wean-to-finish mortality under the conditions of a Midwestern USA swine production system.

Swine wean-to-finish (W2F) mortality is a multifactorial, dynamic process and a key performance indicator of commercial swine production. Although swine producers typically capture the relevant data, analysis of W2F mortality risk factors is often hindered by the fact that, even if data is available, they are typically in different formats, non-uniform, and dispersed among multiple unconnected databases. In this study, an automated framework was created to link multiple data streams to specific cohorts of market animals, including sow farm productivity parameters, sow farm and growing pig health factors, facilities, management factors, and closeout data from a Midwestern USA production system. The final dataset (master-table) contained breeding-to-market data for 1,316 cohorts of pigs marketed between July 2018 and June 2019. Following integration into a master-table, continuous explanatory variables were categorized into quartiles averages, and the W2F mortality was log-transformed, reporting geometric mean mortality of 8.69 % for the study population. Further, univariate analyses were performed to identify individual variables associated with W2F mortality (p < 0.10) for further inclusion in a multivariable model, where model selection was applied. The final multivariable model consisted of 13 risk factors and accounted for 68.2 % (R2) of the variability of the W2F mortality, demonstrating that sow farm health and performance are closely linked to downstream W2F mortality. Higher sow farm productivity was associated with lower subsequent W2F mortality and, conversely, lower sow farm productivity with higher W2F mortality e.g., groups weaned in the highest quartiles for pre-weaning mortality and abortion rate had 13.5 %, and 12.5 %, respectively, which was statistically lower than the lowest quartiles for the same variables (10.5 %, and 10.6 %). Moreover, better sow farm health status was also associated with lower subsequent W2F mortality. A significant difference was detected in W2F mortality between epidemic versus negative groups for porcine reproductive and respiratory syndrome virus (15.4 % vs 8.7 %), and Mycoplasma hyopneumoniae epidemic versus negative groups (13.7 % vs 9.9 %). Overall, this study demonstrated the application of a whole-herd analysis by aggregating information of the pre-weaning phase with the post-weaning phase (breeding-to-market) to identify and measure the major risk factors of W2F mortality.

Emergence of a New Lineage 1C Variant of Porcine Reproductive and Respiratory Syndrome Virus 2 in the United States.

We report an ongoing regional outbreak of an emerging porcine reproductive and respiratory syndrome virus (PRRSV2) variant within Lineage 1C affecting 154 breeding and grow-finishing sites in the Midwestern U.S. Transmission seemed to have occurred in two waves, with the first peak of weekly cases occurring between October and December 2020 and the second starting in April 2021. Most of cases occurred within a 120 km radius. Both orf5 and whole genome sequencing results suggest that this represents the emergence of a new variant within Lineage 1C distinct from what has been previously circulating. A case-control study was conducted with 50 cases (sites affected with the newly emerged variant) and 58 controls (sites affected with other PRRSV variants) between October and December 2020. Sites that had a market vehicle that was not exclusive to the production system had 0.04 times the odds of being a case than a control. A spatial cluster (81.42 km radius) with 1.68 times higher the number of cases than controls was found. The average finishing mortality within the first 4 weeks after detection was higher amongst cases (4.50%) than controls (0.01%). The transmission of a highly similar virus between different farms carrying on trough spring rises concerns for the next high transmission season of PRRS.

Developing personalised braking and steering thresholds for driver support systems from SHRP2 NDS data.

Examining the relationships between the factors associated with the crash development enabled the realisation of driver support systems aiming to proactively avert and control crash causation at various points within the crash sequence. Developing such systems requires new insights in personalised pre-crash driver behaviour with respect to braking and steering to develop crash prevention strategies. Therefore, the current study utilises Strategic Highway Research Program 2 Naturalistic Driving Studies (SHRP2 NDS) data to investigate personalised steering and braking thresholds by examining the last stage of a crash sequence. More specifically, this paper carried out an in-depth examination of braking and steering manoeuvres observed in the final 30 s prior to safety critical events. Two algorithms were developed to extract braking and steering events by examining deceleration and yaw rate and another developed and applied to determine the sequence of the manoeuvres. Based on the analysis, thresholds for detecting emerging situations were recommended. The investigation of driver behaviour before the safety critical events, provides valuable insights into the transition from normal driving to safety critical scenarios. The results indicate that 20% of the drivers did not react to the impending event suggesting that they were not aware of the imminent safety critical situation. Future development of Advanced Driver Assistance Systems (ADAS) can focus on individual drivers' needs with tailored activation thresholds. The developed algorithms can facilitate driver behaviour and safety analysis for NDS while the thresholds recommended could be exploited for the design of new driver support systems.

Complete Coding Genome Sequence of a Novel Porcine Reproductive and Respiratory Syndrome Virus 2 Restriction Fragment Length Polymorphism 1-4-4 Lineage 1C Variant Identified in Iowa, USA.

A porcine reproductive and respiratory syndrome virus 2 strain was identified in lung samples from nursery piglets associated with a 17.15% mortality rate on a swine farm in Iowa. Open reading frame 5 (ORF5) sequencing indicated that this strain is a restriction fragment length polymorphism (RFLP) 1-4-4 lineage 1C variant strain, and its complete coding genome sequence was determined.

Investigation of accidents involving powered two wheelers and bicycles - A European in-depth study.

INTRODUCTION: The number of road fatalities have been falling throughout the European Union (EU) over the past 20 years and most Member States have achieved an overall reduction. Research has mainly focused on protecting car occupants, with car occupant fatalities reducing significantly. However, recently there has been a plateauing in fatalities amongst 'Vulnerable Road Users' (VRUs), and in 2016 accidents involving VRUs accounted for nearly half of all EU road deaths. METHOD: The SaferWheels study collected in-depth data on 500 accidents involving Powered Two-Wheelers (PTWs) and bicycles across six European countries. A standard in-depth accident investigation methodology was used by each team. The Driver Reliability and Error Analysis Method (DREAM) was used to systematically classify accident causation factors. RESULTS: The most common causal factors related to errors in observation by the PTW/bicycle rider or the driver of the other vehicle, typically called 'looked but failed to see' accidents. Common scenarios involved the other vehicle turning or crossing in front of the PTW/bicycle. A quarter of serious or fatal injuries to PTW riders occurred in accidents where the rider lost control with no other vehicle involvement. CONCLUSIONS: Highly detailed data have been collected for 500 accidents involving PTWs or bicycles in the EU. These data can be further analyzed by researchers on a case-study basis to gain detailed insights on such accidents. Preliminary analysis suggests that 'looked but failed to see' remains a common cause, and in many cases the actions of the other vehicle were the critical factor, though PTW rider speed or inexperience played a role in some cases. Practical Applications: The collected data can be analyzed to better understand the characteristics and causes of accidents involving PTWs and bicycles in the EU. The results can be used to develop policies aimed at reducing road deaths and injuries to VRUs.

Powered two-wheeler crash scenario development.

Powered two wheeler (PTW) riders are a group of vulnerable road users that are overrepresented compared to other road user groups with regards to crash injury outcomes. The understanding of the dynamics that occur before a crash benefits in providing suitable countermeasures for said crashes. A clearer interpretation of which factors interact to cause collisions allows an understanding of the mechanisms that produce higher risk in specific situations in the roadway. Real world in-depth crash data provides detailed data which includes human, vehicular and environmental factors collected on site for crash analysis purposes. This study used macroscopic on-scene crash data collected in the UK between the years 2000-2010 as part of the "Road Accident In-depth Study" to analyse the factors that were prevalent in 428 powered two-wheeler crashes. A descriptive analysis and latent class cluster analysis was performed to identify the interaction between different crash factors and develop PTW scenarios based on this analysis. The PTW rider was identified as the prime contributor in 36% of the multiple vehicle crashes. Results identified seven specific scenarios, the main types of which identified two particular 'looked but failed to see' crashes and two types of single vehicle PTW crashes. In cases where the PTW lost control diagnosis failures were more common, for road users other than the PTW rider detection issues were of particular relevance.

The European road safety decision support system on risks and measures.

The European Road Safety Decision Support System (roadsafety-dss.eu) is an innovative system providing the available evidence on a broad range of road risks and possible countermeasures. This paper describes the scientific basis of the DSS. The structure underlying the DSS consists of (1) a taxonomy identifying risk factors and measures and linking them to each other, (2) a repository of studies, and (3) synopses summarizing the effects estimated in the literature for each risk factor and measure, and (4) an economic efficiency evaluation instrument (E3-calculator). The DSS is implemented in a modern web-based tool with a highly ergonomic interface, allowing users to get a quick overview or go deeper into the results of single studies according to their own needs.

Burden of injury of serious road injuries in six EU countries.

BACKGROUND: Information about the burden of (non-fatal) road traffic injury is very useful to further improve road safety policy. Previous studies calculated the burden of injury in individual countries. This paper estimates and compares the burden of non-fatal serious road traffic injuries in six EU countries/regions: Austria, Belgium, England, The Netherlands, the Rhône region in France and Spain. METHODS: It is a cross-sectional study based on hospital discharge databases. POPULATION: of study are patients hospitalized with MAIS3+ due to road traffic injuries. The burden of injury (expressed in years lived with disability (YLD)) is calculated applying a method that is developed within the INTEGRIS study. The method assigns estimated disability information to the casualties using the EUROCOST injury classification. RESULTS: The average burden per MAIS3+ casualty varies between 2.4 YLD and 3.2 YLD per casualty. About 90% of the total burden of injury of MAIS3+ casualties is due to lifelong consequences that are experienced by 19% to 33% of the MAIS3+ casualties. Head injuries, spinal cord injuries and injuries to the lower extremities are responsible for more than 90% of the total burden of MAIS3+ road traffic injuries. Results per transport mode differ between the countries. Differences between countries are mainly due to differences in age distribution and in the distribution over EUROCOST injury groups of the casualties. CONCLUSION: The analyses presented in this paper can support further improvement of road safety policy. Countermeasures could for example be focused at reducing skull and brain injuries, spinal cord injuries and injuries to the lower extremities, as these injuries are responsible for more than 90% of the total burden of injury of MAIS3+ casualties.

Pre-crash scenarios at road junctions: A clustering method for car crash data.

Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth "On-the-Spot" database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations.

Fatal and serious collisions involving pedal cyclists and trucks in London between 2007 and 2011.

OBJECTIVE: Increased numbers of people riding pedal cycles have led to a greater focus on pedal cycle safety. The aim of this article is to explore factors that are associated with fatal and a small number of serious-injury pedal cyclist crashes involving trucks that occurred in London between 2007 and 2011. METHODS: Data were collected from police collision files for 53 crashes, 27 of which involved a truck (≥3.5 tonnes) and a pedal cycle. A systematic case review approach was used to identify the infrastructure, vehicle road user, and management factors that contributed to these crashes and injuries and how these factors interacted. RESULTS: Trucks turning left conflicting with pedal cyclists traveling straight ahead was a common crash scenario. Key contributory factors identified included the pedal cyclists not being visible to the truck drivers, road narrowing, and inappropriate positioning of pedal cyclists. CONCLUSIONS: Crashes involving trucks and pedal cyclists are complex events that are caused by multiple interacting factors; therefore, multiple measures are required to prevent them from occurring.

Evaluation of the benefits of vehicle safety technology: the MUNDS study.

Real-world retrospective evaluation of the safety benefits of new integrated safety technologies is hampered by the lack of sufficient data to assess early reliable benefits. This MUNDS study set out to examine if a "prospective" case-control meta-analysis had the potential to provide more rapid and rigorous analyses of vehicle and infrastructure safety improvements. To examine the validity of the approach, an analysis of the effectiveness of ESC using a consistent analytic strategy across 6 European and Australasian databases was undertaken. It was hypothesised that the approach would be valid if the results of the MUNDS analysis were consistent with those published earlier (this would confirm the suitability of the MUNDS approach). The findings confirm the hypothesis and also found stronger and more robust findings across the range of crash-types, road conditions, vehicle sizes and speed zones than previous. The study recommends that while a number of limitations were identified with the findings that need be addressed in future research, the MUNDS approach nevertheless should be adopted widely for the benefit of all vehicle occupants.

Identifying the causes of road crashes in Europe.

This research applies a recently developed model of accident causation, developed to investigate industrial accidents, to a specially gathered sample of 997 crashes investigated in-depth in 6 countries. Based on the work of Hollnagel the model considers a collision to be a consequence of a breakdown in the interaction between road users, vehicles and the organisation of the traffic environment. 54% of road users experienced interpretation errors while 44% made observation errors and 37% planning errors. In contrast to other studies only 11% of drivers were identified as distracted and 8% inattentive. There was remarkably little variation in these errors between the main road user types. The application of the model to future in-depth crash studies offers the opportunity to identify new measures to improve safety and to mitigate the social impact of collisions. Examples given include the potential value of co-driver advisory technologies to reduce observation errors and predictive technologies to avoid conflicting interactions between road users.

Factors related to fatal injury in frontal crashes involving European cars.

Despite considerable improvements in frontal impact crashworthiness, frontal crashes still account for a major number of front seat occupant fatalities in Great Britain. This study attempted to determine the remaining potential for further fatality reduction with passive safety improvements in frontal crashes. No evidence was found to support an increase in crash test speeds. Instead, assessment of scope for survival showed that at least 27% of all fatal drivers and 39% of all fatal front seat passengers have survival potential given attention to older occupant's chest injury tolerance and passenger compartment intrusion under 60 km/h. Considering only fatal frontal crashes that might be assessed with a barrier test, showed an estimated survival potential of at least 49% of belted drivers and 60% of belted front seat passengers. The high proportion of unbelted fatalities suggested that targeting unbelted occupant protection could have additional benefit.