Crash Injury Research Articles

In-depth analysis of scenarios and injuries in crashes between cyclists and commercial vehicles in Germany

Large vehicles such as buses and heavy goods vehicles (HGVs) pose a serious threat to cyclists and can cause serious injuries. Therefore, it is important to understand current safety issues related to these vehicles, to identify and to develop safety interventions that could address these issues. The aim of this study is to describe the characteristics of and injuries resulting from crashes between cyclists and buses and HGVs. In this study, the German In-Depth Accident Study (GIDAS) was queried for all cases that involved either a bus or a HGV and where the opponent was a cyclist. In total, 98 crashes with the involvement of both a bus and a cyclist, and 295 cases where both a HGV and a cyclist were involved, were identified for our analysis. The crashes with cyclists typically occur within city limits, during daylight conditions, on dry surfaces and with clear weather. The cyclists involved in these crashes are mainly male and cyclists under 18-years old account for 28% of crashes with buses, and 16% for crashes with HGVs. The most common crash scenarios are crossing scenarios and turning-off-a-road crashes. In bus crashes, the collision speeds are mainly below 35 km/h and injuries to lower extremities and head are dominating, caused by the road surface and the front of the bus. In HGV crashes, collision speeds are most often below 20 km/h and injuries to the head and thorax are most common, mainly caused by being run over.

Traffic Congestion and Safety: Mixed Effects on Total and Fatal Crashes

This paper examines the effects of traffic congestion on total crashes, fatal or serious injury (FSI) crashes, and fatal-only crashes in peak periods using a zone-level safety analysis in Greater Melbourne. Bayesian mixed-effect negative binomial models are employed to investigate the relationship between a congestion index and the frequency of total and FSI crashes. In addition, Bayesian mixed-effect binary logistic models are adopted to explore the association between the congestion index and the likelihood of having fatal crashes in Statistical Area Level 2 (SA2) zones. Modelling results indicate that traffic congestion tends to increase total crashes in both the AM and PM peak periods and FSI crashes in the AM peak period. In contrast, traffic congestion tends to decrease the likelihood of having fatal crashes at both the AM and PM peaks. These findings suggest that many policies to reduce traffic congestion may also enhance road safety by lowering the overall number of crashes. However, it is crucial to incorporate careful speed management within these policies to reduce the risk of fatal crashes effectively.

Enhancing work zone crash severity analysis: The role of synthetic minority oversampling technique in balancing minority categories

Road work zones are becoming increasingly common due to the aging infrastructure and the need for capacity enhancement. They present significant safety risks due to narrow lanes, uneven traffic flow, lower speed, and reduced visibility. It is particularly important to understand the role of human behavioral factors in WZ crash injury severity due to difficulty navigating such areas. Furthermore, the crash injury data available is mostly imbalanced, primarily due to the lower incidence of high-cost fatal and severe injuries, and can benefit from the use of emerging analysis techniques. This research study examines a unique dataset comprising 7,855 WZ crashes in Tennessee from 2018 to 2022 as a case study to provide useful insight into the behavioral factors associated with injury severity and how they change after adjusting for the underrepresented fatal and serious injuries within the dataset. The study applies frequentist methods and a machine learning technique enhanced with the Synthetic Minority Oversampling Technique (SMOTE), addressing the data imbalance (relatively fewer fatal and serious injuries) for useful inferences and predictions. The study results indicate that aggressive driving, overspeeding, and drunk driving significantly elevate injury severity. Additionally, after balancing the minority categories of crash injury severity levels, the importance of contributing factors changes. The study offers engineers and data analysts a framework for analyzing imbalanced data, a prevalent issue in crash injury severity analysis. By exploring key behavioral factors responsible for injury severity in WZ crashes, the study provides useful insight and valuable information to traffic safety engineers, transportation agencies, and policymakers to implement enhanced safety measures in WZ design and management, ultimately aiming to mitigate injury severity and to improve overall safety for road users.

Determinants influencing alcohol-related two-vehicle crash severity: A multivariate Bayesian hierarchical random parameters correlated outcomes logit model

Alcohol-related driving remains a significant concern due to its profound association with the likelihood of traffic crashes and the severity of resulting injuries, especially between two vehicles. To investigate the determinants influencing the alcohol-related two-vehicle crash severity, a foundational framework employed was a multinomial logit model. Meanwhile, by incorporating random intercept from individual case and vehicle levels to accommodate unobserved heterogeneity, and covariance matrices to underscore correlated outcomes, a multivariate hierarchical random parameters correlated outcomes logit model was proposed. Additionally, to further explore the potential temporal instability of explanatory variables, a random slope from a per-year indicator was introduced into the model. Crash data from the US Statewide Integrated Traffic Records System (SWITRS) database spanning from January 1, 2016, to December 31, 2021, was used. Three crash injury severity categories were examined, encompassing severe injury, minor injury, and no injury, with characteristics related to the driver, vehicle, road, environment, crash, and time serving as explanatory variables. The model results highlighted significant heterogeneity, with each case and vehicle accounting for 56.9% of the total variance for minor injuries and 50.8% for severe injuries. Furthermore, a significant negative correlation was explicitly exhibited between minor injury and severe injury outcomes at the case level. In terms of potential temporal instability, we provided per-year (2016–2019) parameter estimates and identified significant instability for indicators such as non-intersection, broadside and head-on collisions, cloudy weather conditions, and drivers who had been drinking but were not under the influence. Considering the impact of the COVID-19 pandemic, we divided the accident time into pre-COVID and during-COVID periods, modeling parameter estimates for both periods. This analysis revealed significant instability in several factors influenced by the pandemic. Additionally, noteworthy disparities in the estimated results of explanatory variables emerged in comparison to those general two-vehicle crashes or alcohol-related crashes, providing valuable insights. For instance, drivers who had been drinking but were not under the influence were less likely to sustain severe injuries, but the probability of minor injuries increased. These findings underscore the significance of thorough investigations into the determinants of injury severity in alcohol-impaired two-vehicle crash severity, along with the temporal instability of such factors. They hold important implications for effective traffic safety management and the formulation of prohibitive countermeasures.

Identifying the reciprocal causation between hit-and-run behavior and crash injury severity

Objective Hit-and-run behavior is believed to exacerbate the injury severity of traffic crashes due to the delayed emergency response for the victims. However, several previous studies indicated the opposite finding that hit-and-run crashes were associated with less severe injuries. The relevant studies mainly identified the statistical associations between hit-and-run behavior and injury severity without revealing causation between them. To this end, the study aims to explore the reciprocal causation between the two variables. Method The two-stage probit model with endogenous regressors is employed to identify the reciprocal causation between hit-and-run behavior and crash injury severity for single- and two-vehicle crashes, respectively, with the use of crash data extracted from the Crash Report Sampling System and Fatality Analysis Reporting System (2016-2019). Results The results indicate that 1) for both single- and two-vehicle crashes, the fleeing behavior can significantly increase the injury severity of the victims in the crashes while the severe injury of the victims has a negative impact on the propensity of such behavior, 2) the propensity of hit-and-run behavior is influenced by various instrumental variables such as driver age, gender, alcohol involvement, weekday, area type, and light condition, and 3) crash injury severity is significantly related to the victim age, gender, and vehicle damage. Conclusions There is a reciprocal causation between hit-and-run behavior and injury severity in traffic crashes. The analytical results can provide a reasonable explanation for the counterintuitive finding on hit-an-run crashes and help mitigate the injury severity.

Roundabout Safety Benefits, Design Considerations and Safety Evaluation Techniques

Roundabouts are increasingly replacing conventional signalized intersections due to their potential to enhance traffic safety and operational efficiency. This paper examines and reviews the safety benefits, design considerations, and evaluation methods for roundabouts. Studies consistently show significant reductions in crashes at roundabouts relative to other at-grade intersection control systems, particularly severe injury crashes, with reductions ranging from 37% to 88%. Roundabouts reduce conflict points, lower vehicle speeds, and improve safety for both motorized and vulnerable road users. The methodology for this paper involved a comprehensive review of literature on roundabout design and safety evaluation techniques. Data from global studies were analyzed to identify key design elements such as geometric layout, signage, and pedestrian provisions, which contribute to roundabout safety. Various safety evaluation methods, including crash data analysis, traffic conflict techniques, and surrogate safety measures, were also assessed for their effectiveness in evaluating roundabout performance. Findings indicate that proper geometric design, clear signage, and the use of innovative features like right-turn bypass lanes and roundabout metering systems can enhance safety and traffic flow. Based on these findings, the paper recommends integrating these design elements, prioritizing the safety of vulnerable users, and utilizing advanced safety evaluation techniques, including simulation modelling and intelligent transportation systems (ITS) to further optimize roundabout performance.

Injury Assessment of Pedestrians, Bicyclists, and Motorcyclists Utilizing Linkage of Police-Reported Crash Data to EMS Data in Michigan

In Michigan, police crash reports are the primary source of data on traffic crashes. Though many aspects are very reliable, some details are difficult to observe or require subjective judgement. Injury severity, which is central to the road safety issue, unfortunately falls into the second category. The KABCO injury scale used by law enforcement classifies injuries as “possible,” “suspected minor,” and “suspected serious” which may not correspond to determinations made by medical professionals. EMS records can provide a means to improve crash injury data using medical evaluations, but these records are not linked to the police reports. This project explores the linkage of police crash reports and EMS records using both Michigan data sources from 2018 to 2021. Given the association between severity of injuries and person type, three crash subsets with high injury risk were the focus: pedestrians, bicyclists, and motorcyclists. Collectively, these person types represent 25.1% of the police reported fatalities and suspected serious injuries in Michigan in 2022 despite being only 1.2% of the crashing population. Person-level records for pedestrians, bicyclists, and motorcyclists were identified and divided into three separate datasets for each data source. EMS data events were restricted cases believed to be associated with traffic crashes using ICD-10 injury codes. Personal identifiers were not provided, so probabilistic linkage was used to link person crash records to EMS records. Event date, location, ambulance ID, and person demographic variables were utilized. Linkage rates and injury severity agreement was computed to assess differences between the two datasets.

Effect of population density in aging societies and severity of motor vehicle crash injuries: the case of Spain

The concentration of population in cities and processes of rural depopulation coupled with the generational shift to older societies represent new challenges in road safety. Here, we examine the severity of injuries suffered by the occupants of motor vehicles involved in a crash based on the population density of the area in which the crash occurs, the driver’s age and the density of their place of residence. We conduct the study in Spain, a country with one of the highest levels of elderly population concentrated in rural areas in Europe. Relational methods are used to match Eurostat’s urbanization classifications with the accident database of Spain’s Directorate General of Traffic so as to correlate each crash with the population density of the place where it occurred. A set of generalized linear models with random effects is fitted to analyze the relationship between population density and the bodily injury severity of the occupants of the vehicle(s) involved in a crash, measuring the effect of drivers’ relocation and aging by geographical area. Independence of injury severity and the degree of urbanization was rejected at the 5% significance level. While 53.8% of the Spanish population is living in densely populated areas and only 13.5% in rural areas, the latter concentrates most crashes with fatalities: 2.3 times more than in urban areas (43.5 and 18.6%, respectively). Drivers living in rural areas are more likely to be associated with serious or fatal injuries when involved in a crash in urban and intermediate areas. Moreover, drivers aged over 75 are significantly more likely to be associated with serious and fatal injuries, especially when the crash occurred in urban areas. Recent research alerts on the implications for rural (often elderly) residents of concentrating public services, particularly healthcare, in densely populated areas. Our study shows that motor crashes in more densely populated areas are also a rural health concern. Policy decision-makers need to address this issue to reduce the number of victims and their bodily injury severity.

Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections

Objectives The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe injury. The number of conflicts in different severity levels categorized by the safety index is used as an explanatory variable for developing statistical models for pro-actively estimating crashes. Methods Video-derived conflicts in different severity levels between left-turning vehicles and opposing through vehicles, a well-recognized severe injury crash typology at signalized intersections, were identified by jointly integrating the indicators of frequency and severity, using an autoencoder neural network integration method to develop anomaly scores. Regression models were then developed to relate crashes at the same intersections to the classified conflicts based on the value of their safety indexes. Cumulative Residual plots were investigated. Finally, equations defining the boundary between consecutive anomaly score levels were developed to facilitate application in practice. Results Regression models for total and fatal plus severe (FSI) crashes utilizing classified extreme conflicts based on anomaly scores were found to outperform the models using total conflicts. The improvement is more pronounced for FSI crashes. The results also suggest that the machine learning integration method can efficiently classify conflicts accurately according to crash severity levels since the higher anomaly score is associated with a higher crash severity level (i.e., FSI). Conclusions The proposed framework represents a methodological advancement in traffic conflict-based estimation of crashes using a machine learning model to classify conflicts by their anomaly scores. For jurisdictions without the resources to develop such a model to classify conflicts for their own datasets, the simple equations defining the boundary between consecutive anomaly score levels could be used as an approximation.

Traffic collisions and micromobility: A comparison between personal mobility devices and bicycles based on police reports

Introduction: The recent increase in the use of bicycles and personal mobility devices (PMDs), including mostly E-scooters, is associated with a rapid rise in injuries. Understanding the main crash scenarios leading to these injuries is essential to evaluate and improve preventive and protective measures, especially for PMDs, which are often equated with bicycles. The objective of this study is to identify and compare the most common two-party collision scenarios for bicycles and PMDs, and to identify factors affecting injury severity. Method: Crashes involving at least one PMD or one bicycle and another road user were analyzed from the 2019–2022 French police-reported road crashes database. We investigated the rider, the other vehicle, the road, and the crash scenarios characteristics (pre-crash maneuvers, impact zone on vehicles) and their joint effect on injury severity (hospitalization or fatality: yes/no). Results: We included 16,302 bicycle crashes and 4,118 PMD crashes in the analysis. Most of these collisions (75%) were against a car. The most frequent and the most severe collision scenario was the side-on-head for both bicycles (51%) and PMDs (58%); 67% of both bicycles and PMDs were going straight before the collision. Main factors associated with increased injury severity included colliding with a greater size vehicle, age above 50, and riding on roads with a higher speed limit. Bicycles remained at a higher risk of severe injury than PMDs after accounting for adjustment factors. Conclusions: Although collision scenarios appear similar for bicycles and PMDs, differences in other crash characteristics and injury severity suggest that these two modes of transportation should not be equated in crash investigations. Practical implications: These findings emphasize the need to primarily investigate side-on-head collisions with a moving car for both PMDs and bicycles in order to develop, evaluate, and improve protective devices to reduce the risk of injuries.

Comprehensive analysis of traumatic rupture of the aorta in road traffic crashes: incorporating epidemiological insights and K-prototype clustering

Objectives The identification of crash characteristics associated with traumatic rupture of the aorta (TRA) can significantly enhance countermeasures against TRA. Conventional epidemiological approaches struggle to adequately handle the substantial variability of traffic crash data. Consequently, this study aims to integrate conventional epidemiological analysis with data-driven cluster analysis to more comprehensively analyze TRA-related crash characteristics. Methods A total of 350 unweighted TRA crashes were extracted from traffic crash databases including comprehensive crash details and injury descriptions. Initially, a selection was made of 11 continuous variables and 9 categorical variables, describing crash characteristics. After correlation analysis and principal component analysis were applied to the dataset, K-prototype clustering was finally conducted using 6retained categorical variables and 6 principal components derived from the continuous variables. Results This study found significant age and gender disparities among TRA victims, with 50% falling within the age range of 25–59 years and an overwhelming majority (62.2%) being males. Side impacts emerged as the primary cause of TRA-related crashes (37.2%), followed by collisions with off-road objects (28.6%) and head-on collisions (24.8%). Cluster analyses revealed 6 distinct clusters within the TRA-related crash dataset. These clusters were characterized by factors such as vehicle model year, curb weight, collision dynamics, and seatbelt usage, providing a deeper understanding of the heterogeneity in TRA incidents and their associated factors. Conclusions Although limitations related to available data sources and factors such as accompanying injuries and vehicle weight warrant further comprehensive investigations in the future, this study contributes valuable insights into TRA analysis to enhance understanding and prevention strategies.

Investigating the impact of temporal instability in smart roadway retrofitting on terrain-related crash injury severity

The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions.

Substance use and driver fatality in Norway: An expanded case-control study.

Using alcohol or psychoactive drugs before driving a motor vehicle may increase the risk of crash involvement, injury, and death. This is better documented for alcohol than for drugs. The aim of this study was to expand a previous case-control study on substance use and driver fatality by doubling the number of cases and controls, and hence improve the statistical power and enable the analysis of combined substance use. We collected data on alcohol and drug use from all 1197 drivers of cars and vans who were fatally injured in road traffic crashes in Norway between 2005 and 2020 ('cases') by analyzing blood samples or reviewing other information on substance use. We also collected data on alcohol and drug use among 17,219 drivers in random road traffic ('controls') by analyzing oral fluid samples. Substance use was converted to dichotomous variables (no use/use). We used unconditional logistic regression to estimate adjusted odds ratios (aORs) with 95% confidence intervals (CIs) of driver fatality for mutually exclusive substance groups, adjusted for sex, age, geographic region, urban centrality class, and time interval of the week. Compared to no substance use, the aOR (95% CI) for driver fatality was for alcohol 91 (61-137), stimulants (primarily amphetamines) aOR 22 (9-56), benzodiazepines and z-hypnotics (BZDs) aOR 4.0 (2.7-5.9), tetrahydrocannabinol (THC) aOR 3.4 (1.7-6.7), and opioids aOR 1.4 (0.4-4.9). The aOR for any polysubstance use was 168 (96-297). The combinations of BZDs with stimulants or THC were associated with markedly higher aORs for driver fatality than the use of single substance groups. Alcohol and polysubstance use are the most important predictors of fatal injury, followed by stimulants.

Age-related driver injury occurrence from crashes at curve–grade combined segments

Objectives Due to their relatively complex roadway characteristics, horizontal and vertical curve segments are associated with decreased visibility and a higher risk of rollovers. Multiple studies have identified the associated risk of young and older drivers separately in such complicated driving environments. This study investigated the relationship between driver age and injury occurrence from crashes occurring along curve–grade combined segments. Methods Crash data recorded in Ohio State between 2012 and 2017 were used in this study. Driver age was categorized into 3 groups: teen (age <20 years), adult (age 20–64), and older adult (age >64). Descriptive statistics were summarized using random forest, gradient boosting, and extreme gradient boosting (XGBoost) to estimate the probability of a driver incurring an injury in case of a crash at curve–grade combined segments. The area under the receiver operating characteristics curve (AUROC) was used to select the best performing model. Partial dependence plots (PDPs) were used to interpret the model results. Results The probability of injury occurrence is different for older drivers compared to teen and adult drivers. Although teen and adult drivers showed a higher probability of sustaining injuries in crashes with an increase in the degree of curvature, older drivers were more likely to sustain injuries in roadways with higher annual average daily traffic (AADT), steeper grades, and more occupants in the vehicle. Older drivers were observed to have a higher probability of sustaining injuries during peak hours and when unrestrained compared to teen and adult drivers. Conclusions The results emphasize the significance of tailored education and outreach countermeasures, particularly for teen and older drivers, aimed at decreasing the likelihood of injuries in such driving environments. This research adds to the expanding body of knowledge concerning the age-related occurrence of driver injuries resulting from crashes at curve–grade combined segments. The study findings provide insights into the potential over- or underrepresentation of certain age groups in analyzing crash injury occurrence. The insights gained from the machine learning analysis could also assist policymakers, transportation agencies, and traffic safety experts in developing targeted strategies to enhance road safety and protect vulnerable age groups.

Child vulnerable road user crash injury severity

Road traffic crashes (RTC) are the main cause of mortality, morbidity, and disability for children globally as well as in the United Arab Emirates (UAE). Although vehicle occupants usually account for the majority of children involved in RTC in developed countries and in the UAE, injuries sustained by child vulnerable road users (VRUs) are usually more severe due to their lack of protection compared to child occupants. Such injuries are known to result in long-term suffering for children including disabilities in some cases, thereby posing a severe public health burden and economic losses to the population. However, despite the severity of injuries to child VRUs involved in RTC, studies in the UAE have mostly focused on vehicle occupants for both children and adults. To the best of our knowledge, this is the first dedicated study on child VRU RTC injuries in the UAE. Additionally, the UAE government is promoting an active transportation policy among children in a bid to curb childhood obesity. Hence, this study examined the factors contributing to RTC injury severity for child VRUs in the UAE. The results of this study will help in enhancing the safety outcomes of child VRU RTC injuries as well as providing policy recommendations for safe active transport among children in the country.

Implications of using administrative healthcare data to identify risk of motor vehicle crash-related injury: the importance of distinguishing crash from crash-related injury

BackgroundAdministrative healthcare databases, such as Medicare, are increasingly used to identify groups at risk of a crash. However, they only contain information on crash-related injuries, not all crashes. If the driver characteristics associated with crash and crash-related injury differ, conflating the two may result in ineffective or imprecise policy interventions.MethodsWe linked 10 years (2008–2017) of Medicare claims to New Jersey police crash reports to compare the demographics, clinical diagnoses, and prescription drug dispensings for crash-involved drivers ≥ 68 years with a police-reported crash to those with a claim for a crash-related injury. We calculated standardized mean differences to compare characteristics between groups.ResultsCrash-involved drivers with a Medicare claim for an injury were more likely than those with a police-reported crash to be female (62.4% vs. 51.8%, standardized mean difference [SMD] = 0.30), had more clinical diagnoses including Alzheimer’s disease and related dementias (13.0% vs. 9.2%, SMD = 0.20) and rheumatoid arthritis/osteoarthritis (69.5% vs 61.4%, SMD = 0.20), and a higher rate of dispensing for opioids (33.8% vs 27.6%, SMD = 0.18) and antiepileptics (12.9% vs 9.6%, SMD = 0.14) prior to the crash. Despite documented inconsistencies in coding practices, findings were robust when restricted to claims indicating the injured party was the driver or was left unspecified.ConclusionsTo identify effective mechanisms for reducing morbidity and mortality from crashes, researchers should consider augmenting administrative datasets with information from police crash reports, and vice versa. When those data are not available, we caution researchers and policymakers against the tendency to conflate crash and crash-related injury when interpreting their findings.

Research on occupant injury severity prediction of autonomous vehicles based on transfer learning

AbstractThe focus of the future of autonomous vehicles has shifted from feasibility to safety and comfort. The seat of an autonomous vehicle may be equipped with a rotational function, and the occupant's sitting position would be diverse. This poses a higher challenge to occupant injury protection during vehicle collisions. The main objective of the current study is to develop occupant injury prediction models for autonomous vehicles that can be used to predict the injury severity of occupants in different seat orientations and sitting positions. The first step is to establish an occupant crash model database with different seat orientations. It is used to simulate the occupant crash injury database of an autonomous vehicle, considering seat rotation and the back inclination angle. The second step is to establish a pre‐training occupant injury prediction model based on the existing database and then train the autonomous vehicle occupant injury prediction model using an in‐house database based on the transfer learning method. Occupant injury prediction models achieve good accuracy (82.8% on the numerical database and 62.9% on the real verification database) and shorter computational time (4.86 ± 0.33 ms) on the prediction tasks. Finally, the influence of the model input variables is analyzed. This study demonstrates the feasibility of using a small‐sample database based on transfer learning for occupant injury prediction in autonomous vehicles.

Rethinking cycling safety: The role of gender in cyclist crash injury severity outcomes

Given the ongoing climate crisis and the need for environmentally friendly communities, there has been an increasing interest in sustainable mobility solutions such as cycling. This study seeks to incorporate an equitable component to studying cycling safety and uses one full year’s data of 4,457 single bicycle-single motor vehicle crashes that took place in 2022 in the state of Florida to estimate a series of random parameters multinomial logit models with heterogeneity in the means and variances to capture gender differences in outcome severities. A comparison of advanced statistical models such as unconstrained and partially constrained approaches, that were previously employed in the literature to test for temporal stability, is undertaken in a new application. A partially constrained model is estimated to best identify gender specific factors and argue the need to evaluate and promote safety of female and male cyclists separately. The study finds substantial differences between how the contributing factors and crash circumstances impact the crash injury severity of women and men cyclists. It evaluates factors such as age, location, cyclist behavior, weather, and road design as well as performs out-of-sample simulation to gain additional insights. The findings of this research emphasize the need for targeted approaches in designing our cities and policy making that account for the collective differences in behavior and experience of women and men cyclists.

Aggression of electric bike riders in China: comparisons of the Cycling Anger Expression Inventory toward drivers, pedestrians, and other riders

ABSTRACT Electric bikes (e-bikes) account for an unexpected proportion of traffic crash injuries and deaths in China. There is an urgent need to study the factors influencing the aggressive behaviours of e-bike riders and reduce their potential involvement in crashes. In addition, aggression varies when directed at different types of road users, particularly when expressed by vulnerable groups. This study aimed to validate the Cycling Anger Expression Inventory (CAX) for e-bikers and explore whether the expression of aggression in e-bikers differs depending on the recipients, such as drivers, pedestrians, or other riders. The analysis examined the aggression specificity of e-bikers by employing tests of measurement invariance (MI). These findings indicate that adaptive/constructive expression has emerged as a mainstream strategy for managing e-biker anger. E-bikers exhibited the highest levels of aggression toward drivers. These results provide a strong basis for the prevention and intervention of aggression and accidents among Chinese e-bikers.

Developing a crash severity model based on multi objective evolutionary feature selection approaches

The main purpose of this article is assessing the capability of multiobjective optimisation evolutionary algorithms (MOEAs) along with machine learning method on predicting the crash injury severity with the approach of reducing input variables. Dataset of crashes which were occurred in Iran was analysed over the period of 2016–2020. The data were classified into two classes in terms of injury severity: fatal and non-fatal datasets. At first, general prediction models were created by inserting 31 available input variables into support vector machine (SVM) method based on two imbalanced and obtained balanced datasets. A cluster based under sampling technique was used to obtained balanced data. Then evolutionary algorithms of two objectives optimisation algorithms namely non-dominated sorting genetic algorithm (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEA/D), and pareto envelope-based selection algorithm (PESA-II) along with SVM were used to select features that significantly affect the severity of crashes and developed the simplified models. Then the simplified developed model was compared to the model which was prepared with random forest (RF) method in feature selection. It is shown that the simplified model developed by combined MOEAs and SVM is more appropriate to accurately predict the crash severity, considering the number of input variables in comparison to RF.