Bicycle Crashes Research Articles

Examining the crash risk factors associated with cycling by considering spatial and temporal disaggregation of exposure: Findings from four Dutch cities

Cycling levels in cities keep increasing, which is accompanied with more cyclists being involved in serious road crashes. This paper aims to contribute to safer urban cycling by examining risk factors associated with cycling in the four largest Dutch cities, incorporating spatial and temporal variations in bicycle crash risk. For this purpose, the crashes and exposure metrics are analysed on an hourly temporal resolution. The results reveal that utilising an hourly temporal resolution in the exposure metrics and bicycle crash risk gives more detailed results compared to daily averages of these metrics. Moreover, the exposure to cyclists and motorised vehicles both have a significant impact on bicycle crash risk. The results also imply that separating cyclists from high-speed motorised vehicles might be more important than implementing a lower speed limit to curb the increasing severity of crashes. Despite some local differences, the overall results of the risk factors are remarkably similar across the cities, providing increased generalisability and transferability of the study. The findings indicate that concerns about the effects of increasing bicycle use and large flows of motorised vehicles on bicycle crash risk are valid, showing the importance of efforts towards improving bicycle safety in cities.

Trends and characteristics of severe road traffic injuries in children: a nationwide cohort study in Japan.

The purpose of this study was to evaluate temporal trends of characteristics of severe road traffic injuries in children and identify factors associated with mortality using a nationwide database in Japan. We performed a retrospective analysis of Japan Trauma Data Bank (JTDB) from 2004 to 2018. We included patients with traffic injuries under the age of 18 who were hospitalized. The primary outcome was in-hospital mortality. We evaluated trends in characteristics and assessed factors associated with in-hospital mortality using a logistic regression analysis. A total of 4706 patients were analyzed. The most common mechanism of injury was bicycle crash (34.4%), followed by pedestrian (28.3%), and motorcycle crash (21.3%). The overall in-hospital mortality was 11.2%. We found decreasing trends in motorcycle crash and in-hospital mortality and increasing trends in rear passenger seats in cars over the 15-year period. The following factors were associated with in-hospital mortality: car crash (aOR 1.69, 95%CI 1.18-2.40), pedestrian (aOR 1.50, 95%CI 1.13-1.99), motorcycle crash (aOR 1.42, 95%CI 1.03-1.95) [bicycle crash as a reference]; concomitant injuries to head/neck (aOR 5.06, 95%CI 3.81-6.79), thorax (aOR 2.34, 95%CI 1.92-2.87), abdomen (aOR 1.74, 95%CI 1.29-2.33), pelvis/lower-extremity (aOR 1.57, 95%CI 1.23-2.00), spine (aOR 3.01, 95%CI 2.02-4.43); and 5-year increase in time period (aOR 0.80, 95%CI 0.70-0.91). We found decreasing trends in motorcycle crash and in-hospital mortality, increasing trends in rear passenger seats in cars over the 15-year period, and factors associated with in-hospital mortality such as type of mechanisms and concomitant injuries. Strengthening child road safety measures, particularly for rear passenger seats in vehicles, is imperative to enhance our dedication to injury prevention.

Wider view over bicycle crashes: Complementing and extending bicycle crash statistics in urban areas using surveys

Introduction: In pursuit of sustainability goals, many cities are introducing measures to increase the usage of bicycles as a means of transportation. City planners aim to ensure that this increase does not lead to an increase in crashes, but must make corresponding infrastructure decisions with limited information. Sufficient data to perform a statistical analysis of location-specific crash frequencies is rarely available. For example, only approximately 10% of all bicycle crashes are reported to the police (Shinar et al., 2018). Therefore, urban planners often rely on expert opinion, which may lead to suboptimal prioritization and realization of infrastructure improvements. Method: This paper demonstrates how surveys on bicycle crashes can be used to aid urban planners in making infrastructure decisions. In addition to confirming the location and characteristics of reported crashes, surveys can uncover characteristics of crashes that are not reported to the police, situations in which a crash almost occurred, and locations perceived by cyclists to be dangerous. Surveys also allow urban planners to investigate non-infrastructure related causes of crashes, such as the frequency with which individual cyclists use other modes of transportation. Practical Applications: The usefulness of surveys in the determination of urban cycling safety is demonstrated in this paper through analysis of survey results from the city of Zurich in 2018.

Development of German pedelec (and bicycle) crashes between 2013 and 2021

Introduction: The continuous growth in the use of e-bikes (in Germany mostly pedelecs that support pedaling up to 25 km/h) raises questions about the use of historic crash data for the development of road safety measures. The aim of this study was to address this issue, by conducting a longitudinal analysis of pedelec and bicycle crash data over a period of nine years to identify trends and to clarify whether such trends are specific to pedelecs. Method: We analyzed 95,338 police reported pedelec and bicycle injury crashes from 2013 through 2021. The dataset consisted of crashes from three federal states of Germany: Brandenburg, Hesse and Saxony. Data were analyzed with respect to sex and age distribution, time, location and type of accident, conflict partner, cause of crash and injury severity. Results: Many of the analyzed variables showed a considerable degree of temporal stability, with differences as well as similarities between the two bicycle types staying quite consistent over the years. One notable difference was the mean age of the involved riders, with crashed pedelec riders being significantly older than conventional cyclists. At the same time, however, the mean age of these pedelec riders has decreased by eight years over time. Single vehicle crashes were consistently more common for pedelec riders than for cyclists. Similarly, pedelec rider crashes went with a higher injury severity over all the years. Conclusions and practical applications: While, on a more detailed level, we found differences between the two bicycle types, overall crash characteristics were remarkably similar and consistent over time. Our findings provide no clear argument for road safety measures that are specifically designed to target pedelecs. Instead, the stable crash total, and the increases in ridership of both bicycles and pedelecs, highlight the demand for new, innovative solutions to improve cycling safety in general.

Deep transfer learning-based anomaly detection for cycling safety

Introduction: Despite the general improvements in road safety, with the growing number of bicycle users, cycling safety is still a challenge as demonstrated by the fact that it is the only road transport mode with an increase in the number of fatalities in EU cities. Problem: Moreover, to analyze the problem to improve the road transport system, the traditional network screening based on crash statistics is a reactive approach and less effective due to the lack of suitable bicycle data availability, as well. In such a framework, new opportunities for data collection in smart cities and communities are emerging as proactive approaches to identify critical locations where safety treatments can be effectively applied to prevent bicycle crashes. Method: This research applied a deep transfer learning model to detect anomalies in cycling behavior that can be associated with traffic conflicts or near-miss crashes. Results: The paper presents how to build a users’ tailored riding model named DTL AD to detect and localize riding anomalies by using a set of data in the National Marine Electronics Association (NMEA) string of Global Navigation Satellite System (GNSS) recorded with instrumented bicycles by different cyclists. Conclusion: More specifically, DTL AD exploits a convolutional autoencoder (CAE) with transfer learning to reduce data labelling and training effort. Practical Application: A case study demonstrates the identification of anomalies in cycling behavior visually represented on Geographic Information Systems (GIS) maps, showing how data clustering is well located in high-risk areas.

Determinants of bicycle crashes at urban signalized intersections

Problem: Bicycle volumes are increasing in many regions worldwide leading to higher relevance of an in-depth understanding of bicyclist safety mechanisms. Detailed studies on bicyclist safety that consider exposure and distinguish by intersection category and crash types are missing for urban signalized intersections, which are of particular relevance for bicyclist safety. Method: Based on a comprehensive dataset of motorist and bicyclist volumes and infrastructure characteristics for a sample of 269 signalized intersections in two German cities, we utilize a top-down approach to analyze firstly, bicycle crashes of all types and secondly, bicycle crashes by type including turning, right-of-way and loss-of-control. A combination of descriptive statistics and Accident Prediction Models (APM) are applied as analysis methods. Results: Bicycle volumes are relevant for all types of intersections and crashes, whereas the effect of motor vehicle volumes differ between these different applications. The separation of bicyclists from motor vehicles in time and space increases their safety but also leads to behavioral adaption and risk compensation. The likelihood of right-of-way crashes even increases with more separation in the signaling scheme. The main predictor for loss-of-control crashes in terms of infrastructure are tram tracks. Summary: This study provides insights on relevant determinants of bicycle crashes at urban signalized intersections at several levels of detail. Exposure variables as well as the physical separation of bicyclists from motor vehicles show consistent effects on bicycle crash numbers whereas the effects of signaling differ between crash types. Practical Applications: The different types of intersections and crashes follow each specific mechanism of bicyclist safety. The separation of bicyclists and motorists in time and space are paramount at intersections with high bicycle volumes. Risk compensation such as red light running becomes more important as intersections get smaller and motor vehicle volumes decrease.

Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context

This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using geographic weighted regression (GWR). The sample period includes bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crash index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bike trips’ effects on the severity index.

Estimated prevalence of helmet use while bicycling, rollerblading, and skateboarding among middle school students in selected U.S. States— Youth Behavior Risk Survey, 2013–2019

Background: Helmet use helps prevent severe and fatal head and brain injuries from bicycle, rollerblade, and skateboard crashes. This study explores the prevalence of self-reported helmet use among middle school students while bicycling, skateboarding, and rollerblading. Methods: Data from the Middle School Youth Risk Behavior Survey (YRBS) for selected states were analyzed. Self-reported prevalence (frequency) of helmet use while bicycling, rollerblading, or skateboarding and other variables (sex, grade level, and race/ethnicity) are reported. Results: The overall prevalence of rarely or never wearing a helmet while bicycling among middle school students in selected states was 68.6%; decreasing from 71.7% in 2013 to 67.1% in 2019. The overall prevalence of rarely or never wearing a helmet while rollerblading or skateboarding in middle school students in selected states was 74.6%; decreasing from 76.4% in 2013 to 73.5% in 2019. Students in 7th and 8th grade and students of non-Hispanic race/ethnicity had significantly higher odds of rarely or never wearing a helmet while bicycling or while rollerblading and skateboarding than students in 6th grade and non-Hispanic White students. Conclusions: While helmet use among middle school students improved over time, overall helmet use during bicycling, rollerblading, and skateboarding remained low. These estimates illustrate the continued call for universal implementation of helmet use efforts among kids using established strategies. Practical Applications: Future research on helmet use among youth who rollerblade and skateboard, as well as multi-pronged efforts to promote helmet use among middle schoolers who bicycle, skateboard, and rollerblade (inclusive of education, helmet distribution, and social marketing techniques, as well as the provision of helmets at no-cost) may be beneficial for addressing perceived risks for injury and other barriers.

Assessing bicycle crash risks controlling for detailed exposure: A Copenhagen case study

A better understanding of factors associated with bicycle crashes can inform future efforts to limit crash risks. Many previous studies have analysed crash risk based on crash databases. However, these can only provide conditional information on crash risks. A few recent studies have included aggregate flow measures in their crash risk analyses. This study incorporates detailed bicycle flow to investigate factors related to bicycle crashes. Specifically, the study assesses the relative crash risk given various conditions by applying Palm distributions to control for exposure.The study specifically investigates the relationship between weather and time conditions and the relative risk of bicycle crashes at a disaggregate level. The study uses bicycle crash data from police reports of bicycle crashes from 2017–2020 in the greater Copenhagen area (N = 4877).The relations between the bicycle crash risk and the air temperature and wind speeds are found to be highly non-linear. The relative risk of bicycle crashes is elevated at low and high temperatures (0 °C ¿ x, x ¿ 21 °C). The results also show how decreasing visibility relates to increasing bicycle crash risk. Meanwhile, cycling during the early morning peak (7–8) and afternoon peak hours (15–18) is related to an increased risk of bicycle crashes. While some of the effects are likely spurious, they highlight specific conditions associated with higher relative risk. Finally, the results illustrate the increased risk at weekend night times when cyclists are likely to bike under the influence of alcohol.In conclusion, the analysis confirms that visibility, slippery surfaces, and intoxication are all factors associated with a higher risk of bicycle crashes. Hence, it is relevant to consider how infrastructure planning and preventive measures can modify the bicycle environment to minimise these risks.

Cyclists perception and self-reported behaviour towards interacting with fully automated vehicles

Fully automated vehicles (FAVs) have the potential to improve road safety and reduce traffic congestion and emissions. Most studies of acceptance of FAVs have focused on motor vehicle users, largely ignoring other road users, such as cyclists. This study investigates the factors that influence cyclists’ receptivity towards sharing roads with FAVs and their behavioural intentions in interactions with FAVs. The online survey collected information on participant demographics (e.g. age, gender, crash experience), self-reported on-road cycling behaviours (e.g. violations, errors, positive behaviours) and their receptivity towards sharing roads with FAVs (e.g. attitude, social norms, trust). Three typical cyclist-vehicle interaction scenarios were presented to test the cyclists’ intention to engage in self-protective behaviours (e.g. giving a hand signal, giving way or moving over) during the interaction with a FAV. Three hundred and fourteen Australian adults (106 females vs 208 males) who had ridden a bicycle at least once in the past year completed the survey. The results show that older cyclists and male cyclists had a lower receptivity towards sharing roads with FAVs than younger cyclists and female cyclists, respectively. Cyclists who reported being involved in a bicycle crash in the last two years and those who reported committing more errors on roads were more willing to share roads with FAVs. Cyclists who had a higher propensity to risky behaviours and positive behaviours were less likely to take intended self-protective behaviours during interaction with FAVs. Findings of the study provide some insights from the cyclist’s perspective to facilitate the development and implementation of automated vehicles.

Bicyclist crashes with cars and SUVs: Injury severity and risk factors

Objective The popularity of bicycle travel has increased in recent years alongside a comparable increase in the risk of injury or death for those cyclists. The current study was conducted to investigate the differences in injury outcomes between bicyclists struck by SUVs and those struck by cars and to uncover the mechanisms behind injury patterns that have been observed in past research. Methods We analyzed 71 single-vehicle crashes from the Vulnerable Road User Injury Prevention Alliance pedestrian crash database, focusing on crashes involving an SUV or car. Each crash from this database included an in-depth analysis of police reports, bicyclist medical records, crash reconstructions, and injury attribution by a panel of experts. Results Bicyclist injuries from crashes with SUVs were more severe than those from crashes with cars, particularly with respect to head injuries. The greater injury severity associated with SUVs was related to these vehicles’ tendency to produce injuries from ground contact or from vehicle components near the ground. In contrast, cars were much less likely to produce ground injuries and instead tended to distribute less severe injuries across multiple vehicle components. Conclusions The pattern of results suggest that the size and shape of SUV front ends are responsible for the differences in bicyclist injury outcomes. In particular, we found that SUV crashes inflicted more severe head injuries compared with car crashes and that SUVs were disproportionately likely to throw bicyclists to the ground and run them over.

Effectiveness of bicycle helmets and injury prevention: a systematic review of meta-analyses

To mitigate the risk of injuries, many countries recommend bicycle helmets. The current paper seeks to examine the effectiveness of bicycle helmets by performing a systematic review focusing on meta-analyses. First, the current paper explores the findings of studies that employ meta-analyses using bicycle crash data. Second, the results are discussed considering the findings from research analyzing bicycle helmet effectiveness in a laboratory using simulation, and then are complemented with key methodological papers that address cycling and the overall factors contributing to the injury severity. The examined literature confirms that wearing a helmet while cycling is beneficial, regardless of age, crash severity, or crash type. The relative benefit is found to be higher in high-risk situations and when cycling on shared roads and particularly preventing severe head injuries. The results from the studies performed in laboratories also suggest that the shape and size of the head itself play a role in the protective effects of helmets. However, concerns regarding the equitability of the test conditions were found as all reviewed studies used a fifty-percentile male head and body forms. Lastly, the paper discusses the literature findings in a broader societal context.

Increase in trauma volume as compared to emergency department volume during the COVID-19 pandemic

BackgroundDuring the months between April through June 2020 when we experienced the largest number of COVID-19 patients in our hospital, the volume of patients in the Emergency Department (ED) was decreased by more than 30%. In contrast to most early reports we did not notice a decrease in trauma volume during this time period. Materials and MethodsWe compared trauma patients presenting to our Level III Trauma Center, between April 2019 through June 2019 to those presenting from April 2020 through June 2020, the initial surge in COVID-19 patients. We compared ground level falls (GLF), motor vehicle crashes (MVC), bicycle and skateboard crashes, assault, and other. ResultsThere was a 13% increase in trauma patients presenting during the study period in 2020 as compared to 2019, and the total number of trauma patients as a percentage of total ED patients also increased 269/9235 (2.9%) to 308/6216 (5.0%), P < 0.0001. There was no significant difference in demographics or outcome data between the trauma patients presenting during the two time periods. Although traffic decreased by more than 40%, the number of MVC's was similar. ConclusionThe volume of patients presenting to our Trauma Center as compared to the total ED volume increased during the time period from April through June 2020 versus the year just prior to the COVID-19 pandemic. Despite the fact that the total traffic volume decreased more than 40 percent between these two time periods, the actual number of motor vehicle crashes remained similar.

Multiscale spatial analysis of macro-level determinants of bicycle crash frequencies in the Phoenix metro region

The realization of the many benefits of bicycling will not be achieved in American regions until safer bike infrastructure and bicycling conditions are presented to a more general population. The Phoenix region—one of the nation’s most populous—has sought policies and programs to increase bicycling rates. Yet, the region continues to have a small mode share, underscoring a need to motivate population-level bicycling adoption. This study examines 2015–2019 bicycle-vehicle crash data to identify those macro-level factors associated with bicycle-vehicle crashes and a subset of crashes where a serious injury or fatality occurred. Specifically, the effects of a robust set of socioeconomic and built environment factors, measured at three hexagon spatial extents, in negative binomial and spatial Durbin models were estimated for the two crash outcomes. Results show denser zones with a traditional network design experienced more bicyclist-involved crashes, as did zones with a higher percentage of low-income households and working-age adults. Findings, which also found spatial clustering of total and severe bicyclist-involved crashes, suggest that the targeted provision of safer bike infrastructure and a more complete network in zones exhibiting certain macro-level attributes holds promise in creating bike-friendly conditions that generate more utilitarian and recreational bicycling throughout the region.

Applying Few-Shot Learning in Classifying Pedestrian Crash Typing

Pedestrian deaths account for 23% of all road traffic fatalities worldwide. After declining for three decades, pedestrian fatalities in the United States have been increasing with 6,941 fatalities in 2020, the highest number for more than two decades, impeding progress toward a zero-deaths transportation system. The Pedestrian and Bicycle Crash Analysis Tool (PBCAT) was developed to describe the pre-crash actions of the parties involved to better define the sequence of events and precipitating actions that lead to crashes involving motor vehicles and pedestrians or cyclists. Undoubtedly, police crash data influence decision-making processes in the transportation agencies. Using crash data from three major cities in Texas (during the period from 2018 to 2020), this study assessed the data quality of police-reported crash narratives on pedestrian-involved traffic crashes. As the pedestrian crash typing involves many categories, conventional machine-learning algorithms will not be sufficient in solving the classification problem from narrative texts. This study used few-shot learning (FSL), an advanced machine learning, to solve this issue. Using the pre-knowledge obtained from five different crash types and a few labeled data points of three unseen new crash types, the proposed model achieved roughly 40% overall accuracy. Also, four different configurations of crash types were formed and tested which indicates that the model is robust.

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Exploring the heterogeneity of factors influencing the severity of electric bicycle crashes and electric motorcycle crashes can help target accident prevention policies to improve traffic safety. Therefore, this paper establishes a mean heterogeneity random parameter logit model using crash data from 2016 to 2020 in Guangxi to explore the different factors influencing the severity of crashes involving electric motorcycles and electric bicycles. The results show that the key influences on crash severity differ in electric motorcycle crashes and electric bicycle crashes. At the same time, some common factors affect the two types of crashes to different degrees. In addition, the complex interaction effects of unobserved heterogeneity were considered to explore the random parameters of the two types of crashes. The effect of unobserved heterogeneity on the distribution characteristics of the random parameters was then determined. For example, in electric motorcycle crashes, street lighting at night has a random parameter characteristic. The likelihood of serious crashes decreased when both street lighting at night and vehicle left turn were involved, and decreased when both street lighting at night and no signal control were involved. In electric bicycle crashes, large trucks have a random parameter characteristic. The likelihood of serious crashes increased when both large trucks and motor vehicle lights not turned on were involved, and increased when both large trucks and visibility less than or equal to 200 meters were involved. The results provide a basis for improving the road safety of electric motorcycles and electric bicycles.

Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Pedestrians are the most vulnerable roadway user. While there is much emphasis on “green transportation,” a troubling fact emerges in the U.S.A.: pedestrian deaths are increasing significantly in comparison to motorist deaths, reaching nearly 6941 in 2020—the highest in over two decades. The Pedestrian and Bicycle Crash Analysis Tool was developed to determine motorists and non-motorists’ actions before a crash to accurately define the sequence of events and precipitating actions leading to traffic crashes between motor vehicles and pedestrians or bicyclists. Police report traffic crash data and crash narrative reports undoubtedly play a major role in decision-making for the safety engineers. Using crash data from three major cities in Texas (2018–2020), this study assessed the data quality of text narratives in police reports of pedestrian crashes. The objective of this study was to develop a framework for applying advanced language models to classify pedestrian maneuver types from unstructured textual content. The results show that although natural language processing models are promising as crash typing tools, narration inconsistency, data imbalance, and small sample sizes are holding back progress in this area. The framework demonstrated high accuracy for the binary classification task, but it was inconsistent for the more complex multiclass task. This framework provides the basis for applying advanced language models such as the bidirectional encoder representations from transformers model in identifying pedestrian maneuver types associated with pedestrian crashes.

Assessing the effectiveness of built environment-based safety measures in urban and rural areas for reducing the non-motorist crashes

IntroductionBuilt environment (BE) has a well-documented impact on non-motorist crashes. Interestingly, the urban-rural distinction of the impacts received scant attention in the literature. Moreover, the combined effect of these elements are less studied than their standalone effects. ObjectiveThis study explores the combined effectiveness of built environment-based safety measures in urban and rural settings. Data and methodThe study uses nine years (2011–2019) of non-motorist (pedestrian and bicyclist) crash data in Florida. It classifies urban and rural areas with the multivariate clustering method and models the crash count with Log-transformed Spatial Error Models. ResultsFindings suggest that urban areas, tracts with low median income, a lower percentage of senior citizens, and a higher percentage of black, white, and Hispanic people are significantly associated with a high number of nonmotorist crashes. The percentage of pedestrian and bicyclist commuters is positively associated with pedestrian and bicycle crash count, respectively. Among BE variables, more crashes are observed in tracts with more commercial land use (LU), less recreational LU, higher LU mix, more traffic, signalized intersection, transit stops, and sidewalks. Having more traffic and fewer transit stops pose lesser risk in urban areas than rural areas. The combined effects suggest that increasing commercial LU where LU entropy is high (or vice-versa) will help to reduce nonmotorist crashes. Also, in high entropy areas, increasing rural traffic is riskier whereas increasing urban traffic is safer. ConclusionThis paper documents the need of considering urban-rural differences and interaction effects among BE elements for nonmotorist safety.

Traumatic blunt urethral injuries in females: A retrospective study of the National Trauma Data Bank.

Female blunt urethral injury (FBUI) is much less common than in males. Due to this rarity, studies of FBUI are largely confined to smaller case series. This study analyzes circumstances associated with FBUI and its contribution to mortality in greater detail. Using the National Trauma Data Bank, we analyzed predictors of FBUI, and tested FBUI as a predictor of mortality. Univariate analysis used Chi-squared for categorical data and T-test for continuous data. Multivariate analysis used multiple logistic regression. A total of 245 (0.021%) of 1 185 904 female blunt trauma patients sustained FBUI, vs. 2242 (0.145%) for males (p<0.001). Eighty-seven FBUIs (0.097%) occurred under age 16 vs. 153 (0.016%) in older patients (p<0.001). FBUI was more common with motorcycle (n=14, 0.203%), bicycle (n=11, 0.110%), and automobile vs. pedestrian accidents (n=47, 0.146%) than falls (n=72, 0.011%) or automobile accidents (n=61, 0.029%) (p<0.001). FBUI occurred in 114 (0.011%) patients with Injury Severity Score (ISS) <15 vs. 131 (0.091%) with ISS >15 (p<0.001). Slightly more than half (56.7%) of FBUI occurred with pelvic fractures. Age (odds ratio [OR] 0.95, p<0.001), injury severity (OR 1.05, p<0.001), auto vs. pedestrian (OR 4.1, p<0.001), motorcycle crashes (OR 6.9, p<0.001), and bicycle crashes (OR 3.9, p<0.001) independently predicted FBUI. A total of 9.4% of FBUI patients died vs. 2.5% without FBUI (p<0.001). The association of FBUI with death was not significant on multivariate analysis. FBUI is more prevalent in young patients with high-force direct trauma. FBUI is not an independent predictor of mortality, suggesting that it is a marker of severe injury rather than a direct cause of death.

Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm

Pedestrians and bicyclists from marginalized and underserved populations experienced disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity among road users of different races and the increasing trend of traffic risk for underserved racial groups called for an urgent agenda for transportation policy making and research to ensure equity in roadway safety. Pedestrian and bicyclist crashes involved drivers and pedestrians/bicyclists; the latter were usually victims. Traditional safety studies did not account for the interaction between the two parties and assumed that they were independent from each other. In this study we paired the driver and pedestrian/bicyclist involved in the same crash to understand the socioeconomic and demographic make-up of the two parties involved in crashes and assessed the geographic distribution of these crashes and crash-contributing factors. For this purpose, we applied thelatent class clustering analysis (LCA) to classify different crash types and analyze the patterns of the crashes based on the income and ethnicity of both drivers and victims involved in pedestrian and bicyclist crashes. We then used random forest algorithms and partial dependence plots (PDPs) to model and interpreted the contributing factors of the clusters in both pedestrian and bicyclist models. The clustering results showed a pattern of social segregation in pedestrian and bicyclist crashes that drivers and victims with similar socioeconomic characteristics tend to be involved in one crash. Pedestrian/bicyclist exposure, driver’s age, victim’s age, year of the car in use, annual average daily traffic (AADT), speed limit, roadbed width, and lane width were the most influential factors contributing to this pattern. Crashes that involved drivers and victims with lower income and non-white ethnicity tended to happen in the location with higher pedestrian/bicyclist exposure, higher speed limit, and wider road. The findings of this research can help to inform the decision-making process for improving safety to ensure equitable and sustainable safety for all road users and communities.