Fatal Pedestrian Crashes Research Articles

Generation of nighttime pedestrian fatal precrash scenarios at junctions in Tamil Nadu, India, using cluster correspondence analysis

Objective Modern transportation amenities and lifestyles have changed people’s behavioral patterns while using the road, specifically at nighttime. Pedestrian and driver maneuver behaviors change based on their exposure to the environment. Pedestrians are more vulnerable to fatal injuries at junctions due to increased conflict points with vehicles. Generation of precrash scenarios allows drivers and pedestrians to understand errors on the road during driver maneuvering and pedestrian walking/crossing. This study aims to generate precrash scenarios using comprehensive nighttime fatal pedestrian crashes at junctions in Tamil Nadu, India. Methods Though numerous studies were available on identifying pedestrian crash patterns, only some focused on identifying crash patterns at junctions at night. We used cluster correspondence analysis (CCA) to address this research gap to identify the patterns in nighttime pedestrian fatal crashes at junctions. Further, high-risk precrash scenarios were generated based on the positive residual means available in each cluster. This study used crash data from the Road Accident Database Management System of Tamil Nadu State in India from 2009 to 2018. Characteristics of pedestrians, drivers, vehicles, crashes, light, and roads were input to the CCA to find optimal clusters using the average silhouette width, Calinski-Harabasz measure, and objective values. Results CCA found 4 clusters with 2 dimensions as optimal clusters, with an objective value of 3.3618 and a valence criteria ratio of 80.03%. Results from the analysis distinctly clustered the pedestrian precrash behaviors: Clusters 1 and 2 on pedestrian walking behaviors and clusters 3 and 4 on crossing behaviors. Moreover, a hidden pattern was observed in cluster 4, such as transgender drivers involved in fatal pedestrian crashes at junctions at night. Conclusion The generated precrash scenarios may be used to train drivers (novice and inexperienced for nighttime driving), test scenario creation for developing advanced driver/rider assistance systems, hypothesis creation for researchers, and planning of effective strategic interventions for engineers and policymakers to change pedestrian and driver behaviors toward sustainable safety on Indian roads.

Investigating Racial and Poverty-Level Disparities Associated with Pedestrian Nighttime Crashes

Recently, there has been a large increase in fatal pedestrian crashes that occur at night in the United States of America. In the state of Michigan, for the past 11 years (2011–2021), 76% of all fatal pedestrian crashes occurred at night. With the Toward Zero Death national strategy, identifying the presence of disparities by population characteristics in pedestrian nighttime crashes will enable better allocation of resources to the overrepresented segments of the population and enable the implementation of focused countermeasures to improve the safety of everyone. This study, therefore, aims to investigate the racial and poverty status disparities associated with pedestrian nighttime crashes in Michigan. Pedestrian nighttime crashes from 2011 to 2021 were aggregated at a census tract level. Demographic, socioeconomic, and built environment data were included at a census tract level. The negative binomial model was used to identify whether the racial makeup and economic status of census tracts are significantly associated with the increase in pedestrian nighttime crashes. Results show that census tracts where the majority of people are Black are associated with a 20% increase in pedestrian nighttime crashes. Moreover, poor tracts are associated with a 40% increase in pedestrian nighttime crashes. It was revealed that crashes recorded by police officers as “dark unlighted” are concentrated more in poor census tracks. Also, dark unlighted crashes were more concentrated in a census tract where a majority of the residents identify as Black compared with the majority of residents who identify as White. The use of LED lighting is recommended to improve pedestrian visibility at night.

Identifying the latent relationships between factors associated with traffic crashes through graphical models

Traffic safety field has been oriented toward finding the relationships between crash outcomes and predictor variables to understand crash phenomena and/or predict future crashes. In the literature, the main framework established for this purpose is based on constructing a modelling equation in which crash outcome (e.g., frequencies) is examined in relation to explanatory variables chosen based on the problem at hand. Despite the importance and success of this approach, there are two issues that are generally not discussed: 1) the latent relationships between factors associated with crashes are oftentimes not the focus of analysis or not observed; and 2) there are not many tools to make informed decisions on which variables might have an impact on the crash outcome and should be included in a safety model, particularly when observations are limited. To address these issues, this paper proposes the use of graphical models, namely a Markov random field (MRF) modelling, Bayesian network modelling, and a graphical XGBoost approach, to disclose relationship topologies of explanatory variables leading to fatal and incapacitating injury pedestrian crashes. The application of graph learning models in traffic safety has a high potential because they are not only useful to understand the mechanism behind the crash occurrence but also can assist in devising accurate and reliable prevention measures by identifying the true variable structure and essential factors jointly acting towards crash occurrence, similar to a pathological examination.

A matched case–control approach to identify the risk factors of fatal pedestrian crashes on a six-lane rural highway in India

Globally, the increase in pedestrian fatalities due to road traffic crashes (RTCs) on transport networks has been a major concern. In low- and middle-income countries (LMICs), pedestrians face a high risk due to RTCs on the rural highway network. The safety evaluation methods, such as observational before-after, empirical Bayes, full Bayes, and cross-sectional methods have been used to identify risk factors of RTCs. However, these methods are data-intensive and have associated limitations. Thus, this study employed a matched case–control method to identify the risk factors of fatal pedestrian crashes. This study utilized crash, traffic volume, speed, geometric, and roadside environment data of a 175 km six-lane rural highway in India. The identified major risk factors, such as clear zone width, the presence of habitation, service roads, and horizontal curve sections, increase the likelihood of a fatal pedestrian crash. This study provides specific insights for modifying the speed limit of highway sections passing through habitation. On such highway sections, designers should shift focus to pedestrian safety. It also suggests that the service road design needs to be reconsidered from a pedestrian safety viewpoint. The proposed method can be used in any other setting having similar traffic and socio-economic conditions.

How can front crash prevention systems address more police-reported crashes in the United States?

Government and consumer-information organizations can motivate automakers to address additional crash types through front crash prevention (FCP) testing programs. This study examined the current state of crashes potentially relevant to current and future FCP systems to provide a roadmap for the next crash types that vehicle testing programs in the United States should evaluate.Crash records from 2016 to 2020 were extracted from the Crash Report Sampling System (CRSS) and the Fatality Analysis Reporting System (FARS). Crashes were restricted to ones involving no more than two vehicles where the striking or path-intruding vehicle was a passenger vehicle and a vehicle defect was not coded. Percentages of police-reported crashes, nonfatal-injury crashes, and fatal crashes were computed for different crash types and circumstances.Rear-end and pedestrian crashes evaluated in existing FCP testing programs accounted for 27% of all police-reported crashes, 19% of nonfatal-injury crashes, and 18% of fatal crashes. The remaining crash types relevant to FCP accounted for 25% of police-reported crashes, 31% of nonfatal-injury crashes, and 23% of fatal crashes. A turning passenger vehicle crossing the path of an oncoming vehicle accounted for the largest proportion of the remaining police-reported (8%) and nonfatal-injury crashes (13%). Head-on crashes accounted for the largest proportion of remaining fatal crashes (9%). Most FCP-relevant police-reported crashes occurred on roads with a posted speed limit between 30 and 50 mph. Medium/heavy trucks were the crash partner in a disproportionate number of fatal head-on and rear-end crashes and motorcycles in a disproportionate number of fatal rear-end and turning crossing-path crashes. Fatal bicyclist and pedestrian crashes were overrepresented at night.The findings from this study indicate that testing organizations should evaluate FCP performance at higher speeds; with non-passenger vehicles and vulnerable road users; during the night; and in more complex head-on and turning crash scenarios to reduce crashes of all severities. Some of these conditions are currently assessed by other testing organizations and can be readily adopted by U.S. programs or possibly addressed with new approaches like virtual testing.

The Most Vulnerable User: Examining the Role of Income, Race, and the Built Environment on Pedestrian Injuries and Deaths

This study examined the incidence of injurious and fatal pedestrian crashes for lower-income and affluent communities in Broward and Palm Beach counties, FL, finding notable differences in the environmental risk factors for these populations. In lower-income areas, pedestrian deaths and injuries increased with traffic volumes, multilane streets, and restaurants and shopping centers. They decreased with the presence of raised medians, which can serve as a refuge island for crossing pedestrians. These variables all suggest that, at least in lower-income areas, pedestrian death and injury was associated with difficulties safely accessing household-supporting destinations. For affluent areas, the factors associated with increased pedestrian death and injury were those relating to recreation and nightlife—specifically, bars and clubs, hotels, and restaurants. Neither traffic volumes nor multilane roads proved to be meaningfully related to increased pedestrian death or injury in affluent areas. Perhaps most notably, higher concentrations of Black populations were strongly related to increased pedestrian death and injury, even after accounting for differences in income. Considered as a whole, these results suggest that pedestrian crash risk, like much else in U.S. society, is strongly intertwined with broader issues of racial and income inequality. Attempts to address the safety of the transportation system’s most vulnerable users need to move beyond asserting that any pedestrian project constitutes a safety enhancement, and to begin to more meaningfully account for social vulnerabilities associated with race and income.

Pedestrian- and bicyclist-involved crashes: Associations with spatial factors, pedestrian infrastructure, and equity impacts

Introduction: We analyze and compare the factors that influence the fatality of pedestrian and bicyclist involved crashes in New Jersey using available police-reported crash data between 2016 and 2020. Under three percent of crashes involve non-motorists statewide, but these account for about one third of all traffic fatalities in the state. Methods: Our analysis is broken down into five parts: we (1) analyze the relationship between minority and low-income communities and non-motorist involved crashes; (2) identify spatial differences between non-motorist involved crashes and non-motorist involved fatal crashes; (3) compare the factors affecting fatal pedestrian crashes in New Jersey and in four counties in southern New Jersey for which we have data on pedestrian infrastructure; (4) compare the factors affecting fatal pedestrian crashes and fatal cyclist crashes in New Jersey; and, (5) discuss priority areas for improving safety. Results: Crashes occur disproportionately more often in low-income communities. Moreover, we find that crashes are less likely to be geocoded if they take place in low-income and minority areas, a concerning finding considering that geocoded crashes are of paramount importance in identifying specific corridors for improvement. Light conditions, non-motorist age, posted speed, and vehicle type are significant factors influencing the fatality of non-motorist involved crashes. The proximity to a crosswalk or sidewalk is associated with decreased risk of a fatal crash for pedestrians. Cyclist crashes in low-income neighborhoods were more likely to be fatal – a finding that we attribute to lower access to bicycle facilities in low-income areas. Conclusions: We conclude with countermeasures, including a call for better data collection.

The effect of age on injury patterns in pedestrian deaths.

Although it is known that elderly pedestrians are at increased risk of injury and death from vehicle crashes the specific pattern of lethal injuries related to age has not been extensively studied. Data on the numbers of pedestrian fatalities and ages were obtained from 1990 to 2020 from the Traffic Accident Reporting System, The University of Adelaide, Adelaide, South Australia and detailed autopsy data on fatal pedestrian crashes from the pathology database at Forensic Science SA, Adelaide, South Australia from 2000 to 2020. Fatal injuries were separated into the following regions: head/face, spine, chest, abdomen and limbs/skeleton. Analysis of 634 cases of pedestrian fatalities (1990-2020) showed a significant decline in numbers over the years (p < 0.001). Analysis of fatal injuries in 219 cases (2000-2020) showed a significant reduction in the proportion of fatal head injuries with increasing age (p < 0.05), a significant increase in the proportion of fatal chest injuries with increasing age (p < 0.01) and a significant increase in the proportion of fatal limb/skeletal injuries with increasing age (p < 0.05). Older pedestrians are, therefore, more likely to sustain lethal chest and limb/skeletal injuries than head injuries compared to those who are younger, presumably due to greater physical fragility that occurs with age, with loss of protective muscle bulk and bone density.

What affects pedestrian crossing difficulty at urban intersections in a developing country?

In developing countries, road traffic crashes involving pedestrians have become a foremost concern. At present, road safety assessment plans and selection of interventions are primarily restricted to traditional approaches that depend on the investigations of historical crash data. However, in developing countries such as India, the availability, consistency, and accuracy of crash data are major concerns. In contrast, proactive approaches such as studying road users' risk perception have emerged as a substitute method of examining potential risk factors. An individual's risk perception offers vital information on probable crash risk, which may be beneficial in detecting high-risk locations and major causes of crashes. Since the pedestrian fatality risk is not uniform across the urban road network level, it may be expected that pedestrians' perceived risk measured in terms of “crossing difficulty” would also vary across the sites. In this perspective, the present paper establishes a mathematical association between the pedestrians' perceived “crossing difficulty” and actual crashes. The model outcome confirms that pedestrians' perceived crossing difficulty is a good surrogate of fatal pedestrian crashes at the intersection level in Kolkata City, India. Subsequently, to examine the impact of traffic exposures, road infrastructure, land use, spatial factors, and pedestrian-level attributes on pedestrians' “crossing difficulty”; a set of Ordered Logit models are developed. The model outcomes show that high vehicle and pedestrian volume, vehicular speed, absence of designated bus stop, the presence of inaccessible pedestrian crosswalk, on-street parking, lack of signalized control (for both vehicle and pedestrian), inadequate sight distance, land use pattern, slum population, pedestrian-vehicular post encroachment time, waiting time before crossing, road width, and absence of police enforcement at an intersection significantly and positively increase pedestrian's crossing difficulty at urban intersections. To end, the model findings are advantageously utilized to develop a set of countermeasures across 3E's of road safety.

Understanding the role of faster emergency medical service response in the survival time of pedestrians

Fatalities and severe injuries among vulnerable road users, particularly pedestrians, are rising. In addition to the loss of life, about 6,000 annual pedestrian deaths in the U.S. cost society about $6 billion. Contrary to the assumption that all fatal pedestrian-involved crashes are similar, instantaneous death is substantially more severe than death that occurs several days after the crash. Instead of homogenizing all fatal pedestrian crashes, this study takes into account the severity of fatal injury crashes as a timeline based on the survival time of pedestrians. This study extracts valuable information from fatal crashes by examining pedestrians' survival time ranging from early death to death within 30 days of the crash. The Fatality Analysis Reporting System dataset is utilized from 2015 to 2018. The emergency medical service (EMS) response time is the key post-crash measure, while controlling for pedestrian, driver, roadway, and environmental characteristics. Notably, the response time and survival time can cause endogeneity, i.e., the response times may be shorter for more severe crashes. Due to the spatial and temporal nature of traffic crashes, to extract the association of different variables with pedestrians' survival time, a geographically and temporally weighted truncated regression with a two-stage residual inclusion treatment (local model) is estimated. The local model can overcome the endogeneity limitation (between EMS response time and survival time) and uncover the potentially spatially and temporally varying correlates of pedestrians’ survival time with associated factors to account for unobserved heterogeneity. Moreover, to verify the variations are noticeable, a truncated regression with the two-stage residual inclusion treatment is developed (global model). The modeling results indicate that while capturing the unobserved heterogeneity, the local model outperformed the global model. The empirical results show that EMS response time, speeding, and some pedestrian behaviors are the most important factors that affect pedestrians’ survival time in fatal injury crashes. However, the effect of factors on pedestrians’ survival time is noticeably varied spatially and temporally. The results and their implications are discussed in detail in the paper.

Analysis of contributory factors of fatal pedestrian crashes by mixed logit model and association rules

Pedestrians are the most vulnerable road users and pedestrian crashes are a major concern both for their number and their severity. In Italy, pedestrians account for 34% of the road fatalities in urban area. To improve pedestrian safety, this study is aimed at analysing the roadway, environmental, vehicle, driver and pedestrian-related factors that are associated with fatal pedestrian crashes in Italy and providing insights for the development of effective countermeasures. This study used an econometric model, the mixed logit model, and a machine learning algorithm, the association rules, to analyse 101,032 pedestrian crashes that occurred in Italy. Study results identified several factors associated with fatal pedestrian crashes. The mixed logit identified 46 significant indicator variables (1 with random parameter), and the association rules provided 119 valid rules. F-measure and G-mean showed higher prediction performance of the mixed logit over the association rules. Study results recommend using both models as complementary approaches since their combination is effective in providing meaningful insights about pedestrian crash contributory factors and their interdependencies. To address the contributory factors identified by the study, behavioural/engineering pedestrian safety countermeasures are recommended. The findings provided new insights for transportation agencies to develop effective countermeasures for pedestrian safety improvement.

Where are pedestrians injured and killed on Kampala city's road network?

Road traffic crashes are a threat to walkers in cities in the global south. It is important for us to know where these crashes frequently occur as well as gain an in-depth understanding of the traffic generators around these sites. Kampala city has experienced many changes over time, and as a result, the crash sites could have changed over time. Pedestrian crashes in cities cluster in specific places than others on a road network known as black spots. It is important to know and update the current black spots as well as reassess the circumstances surrounding these crash sites. This study utilized 5-year traffic crash data (2015–2019) to map and categorize pedestrian black spots in Kampala city, Uganda, by road location type, as well as the nature of activity happening around. A total of 1,095 unique crash locations emerged from the data. Based on the criteria of locations with at least 10 serious or fatal crashes in 5 years, 46 locations were identified as black spots. The bulk (48%) of serious and fatal pedestrian black spots were junctions, especially those with traffic lights, while 74% of these black spots were around places with commercial land use. Whereas implementation of appropriate interventions at black spots is a general well-proven method for reducing the number and severity of crashes at these sites on the road network, the measures taken to treat accident black spots vary from place to place dependent on the unique local circumstances and risk factors. The draft black spot list serves as a basis for further investigation and analysis of the local risk factors surrounding serious and fatal pedestrian crashes in low-income settings as a key step in generating appropriate interventions.

Effects of large vehicles on pedestrian and pedalcyclist injury severity

Introduction: Fatal pedestrian and pedalcyclist crashes have been on the rise in the United States since 2009. This rise in fatalities coincides with the rise of large vehicles on American roadways, continuing a trend that began years earlier. Method: Through rare access to both crash and hospital records, this report investigates the relationship between striking vehicle type and medical outcomes of pedestrian and pedalcyclist cases. Results: Results suggest that children are eight times more likely to die when struck by a SUV compared to those struck by a passenger car. Passenger cars were the striking vehicle in most fatal pedestrian and pedalcyclist crashes, though they were underrepresented relative to the proportion of all crashes in which they were involved. Though pickup trucks were the striking vehicle in just 5.6% of pedestrian and pedalcyclist crashes, they were involved in 12.6% of fatalities. SUVs were similarly overrepresented in fatalities relative to the proportion of their involvement in all crashes. SUVs struck 14.7% of the pedestrians and pedalcyclists investigated here, but were involved in 25.4% of the fatalities. Head and thorax injury severities are examined by vehicle type and age. Hospital charges of pedestrian and pedalcycle crash victims are also analyzed by striking vehicle type and victim age. Practical applications: Findings suggest larger vehicles are involved in pedestrian and pedalcyclist crashes with more severe injuries that result in higher hospital charges. By race, Blacks are also found to be overrepresented as pedestrian and pedalcyclist crash victims.

Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions

The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need to examine pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010–2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that daylight pedestrian crashes are associated with children (&lt;15 years), senior pedestrians (&gt;64 years), older drivers (&gt;64 years), and other driving behaviors such as “failure to yield,”“inattentive/distracted,”“illness/fatigue/asleep.” Additionally, young drivers (15–24 years) are involved in severe pedestrian crashes in daylight conditions. This study also found pedestrian alcohol/drug involvement as the most frequent item in the dark-with-streetlight condition. This crash type is particularly associated with pedestrian action (crossing intersection/midblock), driver age (55–64 years), speed limit (30–35 mph), and specific area type (business with mixed residential area). Fatal pedestrian crashes are found to be associated with roadways with high speed limits (&gt;50 mph) during the dark without streetlight condition. Some other risk factors linked with “high speed limit” related crashes are pedestrians walking with/against the traffic, presence of pedestrian dark clothing, and pedestrian alcohol/drug involvement. The research findings are expected to provide an improved understanding of the underlying relationships between pedestrian crash risk factors and specific lighting conditions. Highway safety experts can utilize these findings to conduct a decision-making process for selecting effective countermeasures to reduce pedestrian crashes strategically.

Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes

This study examined the impact of density, diversity, design, distance to transit, and destination accessibility, five measures, known as the 5Ds, that characterize the built environment, on pedestrian–vehicle crashes in Seoul, Korea. Using spatial analysis based on 500-m grid cells, this study employed negative binomial regression models on the frequencies of three specific types of pedestrian–vehicle crashes: crashes causing death, major injury, and minor injury to pedestrians. Analysis shows that compact and mixed-use urban environments represented by 5D measures have mixed effects on pedestrian safety. Trade-off effects are found between a higher risk for all types of pedestrian crashes, and a lower risk for fatal pedestrian crashes in 5D urban environments. As a design variable, a higher number of intersections is more likely to increase some types of pedestrian crashes, including fatal crashes, a finding which warrants policy attention to promote pedestrian safety near intersection areas. This study also confirms an urgent need to secure the travel safety of pedestrians near public transit stations due to the higher risk of pedestrian crashes near such facilities. Various destinations, such as retail stores, traditional markets, and hospitals, are associated with pedestrian crashes. Pedestrian safety measures should be implemented to reduce the likelihood of pedestrian crashes near major destination facilities.

Investigating the fatal pedestrian crash occurrence in urban setup in a developing country using multiple-risk source model

Pedestrian fatalities and injuries are a major public health burden in developing countries. In the safety literature, pedestrian crashes have been modelled predominately using single equation regression models, assuming a single underlying source of risk factors. In contrast, the fatal pedestrian crash counts at a site may be an outcome of multiple sources of risk factors, such as poor road infrastructure, land use type, traffic exposures, and operational parameters, site-specific socio-demographic characteristics, as well as pedestrians’ poor risk perception and dangerous crossing behavior, which may be influenced by poor road infrastructure and lack of information, etc. However, these multiple sources are generally overlooked in traditional single equation crash prediction models.In this background, this study postulates, and demonstrates empirically, that the total fatal pedestrian crash counts at the urban road network level may arise from multiple simultaneous and interdependent sources of risk factors, rather than one. Each of these sources may distinctively contribute to the total observed crash count.Intersection-level crash data obtained from the “Kolkata Police”, India, is utilized to demonstrate the present modelling methodology. The three-components mixture model and a joint econometric model are developed to predict fatal pedestrian crashes. The study outcomes indicate that the multiple-source risk models perform significantly better than the single equation regression model in terms of prediction ability and goodness-of-fit measures. Moreover, while the single equation model predicts total fatal crash counts for individual sites, the multiple risk source model predicts crash count proportions contributed by each source of risk factors and predicts crashes by a particular source.

Nighttime pedestrian fatalities: A comprehensive examination of infrastructure, user, vehicle, and situational factors

Introduction: Pedestrian fatalities in the United States increased 45.5% between 2009 and 2017. More than 85% of those additional pedestrian fatalities occurred at night. Method: We examine Fatality Analysis Reporting System (FARS) data for fatal pedestrian crashes that occurred in the dark between 2002 and 2017. Within-variable and before/after examinations of crashes in terms of infrastructure, user, vehicle, and situational characteristics are performed with one-way analysis of variance (ANOVA) and two-sample t-tests. We model changes in crash characteristic proportions between 2002–2009 and 2010–2017 using linear regressions and test for autocorrelation with Breusch-Godfrey tests. Results: The increase in fatal nighttime pedestrian crashes is most strongly correlated with infrastructure factors: non-intersection unmarked locations (saw 80.8% of additional fatalities); 40–45 mph roads (54.6%); five-lane roads (40.7%); urban (99.7%); and arterials (81.1%). In addition, SUVs were involved in 39.7% of additional fatalities, overrepresenting their share of the fleet. Increased pedestrian alcohol and drug involvement warrant further investigation. The age of pedestrians killed increased more (18.1%) than the national average (3.2%). Conclusions: By identifying factors related to the increase in nighttime pedestrian fatalities, this work constitutes a vital first step in making our streets safer for pedestrians. Practical Applications: More research is needed to understand the efficacy of different solutions, but this paper provides guidance for such future research. Engineering solutions such as road diets or traffic calming may be used to improve identified infrastructure issues by reducing vehicle speeds and road widths. Rethinking vehicle design, especially high front profiles, may improve vehicle issues. However, the problems giving rise to these pedestrian fatalities are likely a result of not only engineering issues but also interrelated social and political factors. Solutions may be correspondingly comprehensive, employing non-linear, systems-based approaches such as Safe Systems.

Identifying Future Vehicle Safety Priority Areas in Australia for the Light Vehicle Fleet

Formulating priorities for future road safety strategies requires supporting analysis to predict what the future crash population will look like and to assess how the countermeasures either already in place or planned will address the crash problems forecast. This analysis aimed to identify future priority action areas for light vehicle safety by identifying crash types that will not be fully addressed in the future by projected improvements in active and passive safety in the Australian light vehicle fleet. The future crash profile was modelled from 2017 to 2030 using crash data from 5 Australian jurisdictions overlayed with available evidence on vehicle safety feature fitment and effectiveness. The methodology can be applied to larger sets of safety technologies when sufficient evidence and supporting crash data become available. Three future vehicle safety priority areas were identified from the analysis: (i) fatal pedestrian crashes, (ii) single vehicle frontal crashes with objects, and (iii) front-to-front vehicle crashes both at intersections and midblocks, and front-to-side impacts at intersections including straight crossing path and right turn across path crash types. These crash types were projected to be the largest contributors to fatalities by 2030. Projections showed that remaining crash types in 2030 will be poorly addressed by current vehicle safety technologies such as autonomous emergency braking, lane departure warning and electronic stability control. Future vehicle safety policy priorities should address these crash types through the development of additional or enhanced vehicle safety technologies and where vehicle safety technology proves inadequate other countermeasures such as road infrastructure treatments and appropriate speed limit setting for high risk environments that address the key crash types remaining in the system.

Fatal pedestrian crashes at intersections: Trend mining using association rules

In 2018, about 6,677 pedestrians were killed on the US roadways. Around one-fourth of these crashes happened at intersections or near intersection locations. This high death toll requires careful investigation. The purpose of this study is to provide an overview of the characteristics and associated crash scenarios resulting in fatal pedestrian crashes in the US. The current study collected five years (2014–2018) of fatal crash data with additional details of pedestrian crash typing. This dataset provides specifics of scenarios associated with fatal pedestrian crashes. This study applied associated rules mining on four sub-groups, which were determined based on the highest frequencies of fatal crash scenarios. This study also developed the top 20 rules for all four sub-groups and used ‘a priori’ algorithm with ‘lift’ as a performance measure. Some of the key variable categories such as dark with lighting condition, vehicle going straight, vehicle turning, local municipality streets, pedestrian age range from 45 years and above are frequently presented in the developed rules. The patterns of the rules differ by the pedestrian’s position within and outside of crosswalk area. If the pedestrian is outside the crosswalk area, no lighting at dark is associated with high number of crashes. As lift provides quantitative measures in the form of the likelihood, the rules can be transferred into data-driven decision making. The findings of the current study can be used by safety engineers and planners to improve pedestrian safety at intersections.

Exploratory Analysis of Pedestrian Road Trauma in Finland

In Finland, all fatal on-road and off-road motor vehicle crashes are subject to an in-depth investigation coordinated by the Finnish Crash Data Institute (OTI). This study presents an exploratory and two-step cluster analysis of fatal pedestrian crashes between 2010 and 2019 that were subject to in-depth investigations. In total, 281 investigations occurred across Finland between 2010 and 2019. The highest number of cases were recorded in the Uusimaa region, including Helsinki, representing 26.4% of cases. Females (48.0%) were involved in fewer cases than males; however, older females represented the most commonly injured demographic. A unique element to the patterns of injury in this study is the seasonal effects, with the highest proportion of crashes investigated in winter and autumn. Cluster analysis identified four unique clusters. Clusters were characterised by crashes involving older pedestrians crossing in low-speed environments, crashes in higher speed environments away from pedestrian crossings, crashes on private roads or in parking facilities, and crashes involving intoxicated pedestrians. The most common recommendations from the investigation teams to improve safety were signalisation and infrastructure upgrades of pedestrian crossings, improvements to street lighting, advanced driver assistance (ADAS) technologies, and increased emphasis on driver behaviour and training. The findings highlight road safety issues that need to be addressed to reduce pedestrian trauma in Finland, including provision of safer crossing facilities for elderly pedestrians, improvements to parking and shared facilities, and addressing issues of intoxicated pedestrians. Efforts to remedy these key issues will further Finland’s progression towards meeting Vision Zero targets while creating a safer and sustainable urban environment in line with the United Nations sustainable development goals.