RESUMEN
INTRODUCTION: Vehicles play an important role in pedestrian injury risk in crashes. This study examined the association between vehicle front-end geometry and the risk of fatal pedestrian injuries in motor vehicle crashes. METHOD: A total of 17,897 police-reported crashes involving a single passenger vehicle and a single pedestrian in seven states were used in the analysis. Front-end profile parameters of vehicles (2,958 vehicle makes, series, and model years) involved in these crashes were measured from vehicle profile photos, including hood leading edge height, bumper lead angle, hood length, hood angle, and windshield angle. We defined a front-end-shape indicator based on the hood leading edge height and bumper lead angle. Logistic regression analysis evaluated the effects of these parameters on the risk that a pedestrian was fatally injured in a single-vehicle crash. RESULTS: Vehicles with tall and blunt, tall and sloped, and medium-height and blunt front ends were associated with significant increases of 43.6%, 45.4%, and 25.6% in pedestrian fatality risk, respectively, when compared with low and sloped front ends. There was a significant 25.1% increase in the risk if a hood was relatively flat as defined in this study. A relatively long hood and a relatively large windshield angle were associated with 5.9% and 10.7% increases in the risk, respectively, but the increases were not significant. CONCLUSIONS: Vehicle front-end profiles that were significantly associated with increased pedestrian fatal injury risk were identified. PRACTICAL APPLICATIONS: Automakers can make vehicles more pedestrian friendly by designing vehicle front ends that are lower and more sloped. The National Highway Traffic Safety Administration (NHTSA) can consider evaluations that account for the growing hood heights and blunt front ends of the vehicle fleet in the New Car Assessment Program or regulation.
Asunto(s)
Accidentes de Tránsito , Peatones , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/mortalidad , Humanos , Peatones/estadística & datos numéricos , Heridas y Lesiones/epidemiología , Automóviles/estadística & datos numéricos , Estados Unidos/epidemiología , Vehículos a Motor/estadística & datos numéricos , Modelos Logísticos , Adulto , MasculinoRESUMEN
INTRODUCTION: Pedestrians are a particularly vulnerable group of road users. Mobile phone usage while walking (MPUWW) is a significant contributor to pedestrians' involvement in road crashes and associated injuries. The current study aims to explore the effect of state mindfulness on daily MPUWW via phone dependence (at the within-person level), and the moderating role of risk perception (at the between-person level) in the phone dependence-MPUWW relationship. METHOD: We utilized a fine-grained method, the daily diary methodology (DDM) to explore the aforementioned model. A total of 88 Chinese college students participated in a consecutive 12-day study, yielding 632 daily data. Unconflated multilevel modeling was used to analyze the data. RESULTS: After trait mindfulness being controlled, state mindfulness has a negative impact on MPUWW via phone dependence at the daily level. Furthermore, risk perception as an individual difference variable moderates the relationship between phone dependence and MPUWW, in which a weaker effect observed in individuals with higher levels of risk perception. CONCLUSIONS: State mindfulness can decrease the frequency of daily MPUWW by reducing phone dependence, and risk perception is a crucial factor in mitigating the negative effects of phone dependence on MPUWW. PRACTICAL APPLICATIONS: To lower MPUWW and thereby minimize the risk of road crashes and associated injuries, it is beneficial to foster present-moment awareness of individuals, encourage individuals to use mobile phones in a balanced and sensible manner, and integrate the enhancement of risk perception into road safety education.
Asunto(s)
Accidentes de Tránsito , Uso del Teléfono Celular , Atención Plena , Caminata , Humanos , Masculino , Femenino , China , Adulto Joven , Uso del Teléfono Celular/estadística & datos numéricos , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/psicología , Adulto , Teléfono Celular/estadística & datos numéricos , Peatones/psicología , Peatones/estadística & datos numéricos , Adolescente , Estudiantes/psicología , Estudiantes/estadística & datos numéricosRESUMEN
Crashes are frequently disproportionally observed in disadvantaged areas. Despite the evident disparities in transportation safety, there has been limited exploration of quantitative approaches to incorporating equity considerations into road safety management. This study proposes a novel concept of equity-aware safety performance functions (SPFs), enabling a distinct treatment of equity-related variables such as race and income. Equity-aware SPFs introduce a fairness distance and integrate it into the log-likelihood function of the negative binomial regression as a form of partial lasso regularization. A parameter λ is used to control the importance of the regularization term. Equity-aware SPFs are developed for pedestrian-involved crashes at the census tract level in Virginia, USA, and then employed to compute the potential for safety improvement (PSI), a prevalent metric used in hotspot identification. Results show that equity-aware SPFs can diminish the effects of equity-related variables, including poverty ratio, black ratio, Asian ratio, and the ratio of households without vehicles, on the expected crash frequencies, generating higher PSIs for disadvantaged areas. Based on the results of Wilcoxon signed-rank tests, it is evident that there are significant differences in the rankings of PSIs when equity awareness is considered, especially for disadvantaged areas. This study adds to the literature a new quantitative approach to harmonize equity and effectiveness considerations, empowering more equitable decision-making in safety management, such as allocating resources for safety enhancement.
Asunto(s)
Accidentes de Tránsito , Peatones , Seguridad , Humanos , Accidentes de Tránsito/prevención & control , Accidentes de Tránsito/estadística & datos numéricos , Peatones/estadística & datos numéricos , Virginia , Funciones de Verosimilitud , Poblaciones Vulnerables , Administración de la Seguridad , RentaRESUMEN
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.
Asunto(s)
Accidentes de Tránsito , Planificación Ambiental , Humanos , Accidentes de Tránsito/estadística & datos numéricos , Masculino , China/epidemiología , Adulto , Factores de Riesgo , Femenino , Heridas y Lesiones/epidemiología , Heridas y Lesiones/etiología , Persona de Mediana Edad , Modelos Logísticos , Peatones/estadística & datos numéricos , Adulto Joven , Vehículos a Motor/estadística & datos numéricos , Puntaje de Gravedad del Traumatismo , Índices de Gravedad del TraumaRESUMEN
Pedestrian fatalities comprise a quarter of all traffic deaths in Low-and-Middle-Income Countries (LMICs). The use of safer modes of transport such as buses can reduce road trauma as well as air pollution and traffic congestion. Although travelling by bus is safer than most other modes, accessing bus stops can be risky for pedestrians. This paper systematically reviews factors contributing to the safety of pedestrians near bus stops in countries of differing income levels. The review included forty-one studies from high (20), upper-middle (13) and lower-middle income countries (8) during the last two decades. The earliest research was conducted in high-income countries (HICs), but research has spread in the last decade. The factors influencing pedestrian safety fell into three groups: (a) characteristics of road users, (b) characteristics of bus stops and (c) characteristics of the road traffic environment. Pedestrians near bus stops are frequently exposed to a high risk of collisions and fatalities due to factors such as unsafe pedestrian behaviours (e.g., hurrying to cross the road), lack of bus stop amenities such as safe footpaths, high traffic speeds and traffic volumes, multiple lanes, and roadside hazards (e.g., parked cars obscuring pedestrians). Road crash statistics are commonly used to identify unsafe bus stops in HICs but the unavailability and unreliability of data have prevented more widespread use in LMICs. Future research is recommended to focus on surrogate safety measures to identify hazardous bus stops for pedestrians.
Asunto(s)
Accidentes de Tránsito , Países en Desarrollo , Renta , Vehículos a Motor , Peatones , Seguridad , Humanos , Accidentes de Tránsito/mortalidad , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/prevención & control , Peatones/estadística & datos numéricos , Seguridad/estadística & datos numéricos , Vehículos a Motor/estadística & datos numéricos , Planificación Ambiental , Factores de Riesgo , Caminata/lesiones , Caminata/estadística & datos numéricos , Países DesarrolladosRESUMEN
As vulnerable road users, pedestrians and cyclists are facing a growing number of injuries and fatalities, which has raised increasing safety concerns globally. Based on the crash records collected in the Australian Capital Territory (ACT) in Australia from 2012 to 2021, this research firstly establishes an extended crash dataset by integrating road network features, land use features, and other features. With the extended dataset, we further explore pedestrian and cyclist crashes at macro- and micro-levels. At the macro-level, random parameters negative binomial (RPNB) model is applied to evaluate the effects of Suburbs and Localities Zones (SLZs) based variables on the frequency of pedestrian and cyclist crashes. At the micro-level, binary logit model is adopted to evaluate the effects of event-based variables on the severity of pedestrian and cyclist crashes. The research findings show that multiple factors are associated with high frequency of pedestrian total crashes and fatal/injury crashes, including high population density, high percentage of urban arterial road, low on-road cycleway density, high number of traffic signals and high number of schools. Meanwhile, many factors have positive relations with high frequency of cyclist total crashes and fatal/injury crashes, including high population density, high percentage of residents cycling to work, high median household income, high percentage of households with no motor vehicle, high percentage of urban arterial road and rural road, high number of bus stops and high number of schools. Additionally, it is found that more severe pedestrian crashes occur: (i) at non-signal intersections, (ii) in suburb areas, (iii) in early morning, and (iv) on weekdays. More severe cyclist crashes are observed when the crash type is overturned or struck object/pedestrian/animal; when more than one cyclist is involved; and when crash occurs at park/green space/nature reserve areas.
Asunto(s)
Accidentes de Tránsito , Ciclismo , Peatones , Accidentes de Tránsito/estadística & datos numéricos , Humanos , Ciclismo/lesiones , Ciclismo/estadística & datos numéricos , Peatones/estadística & datos numéricos , Territorio de la Capital Australiana/epidemiología , Factores de Riesgo , Densidad de Población , Planificación Ambiental , Conjuntos de Datos como Asunto , Caminata/lesiones , Caminata/estadística & datos numéricosRESUMEN
Street intersection crashes often involve two parties: either two vehicles hitting each other (i.e., a vehicle-vehicle crash) or a vehicle colliding with a pedestrian (i.e., a vehicle-pedestrian crash). In such crashes, the severity of injuries can vary considerably between the parties involved. It is necessary to understand the injuries of both parties simultaneously to identify the causality of a vehicle-pedestrian or two-vehicle crash. While the latent class ordinal model has been used in crash severity studies to capture heterogeneity in crash propensity, most of these studies are univariate, which is inappropriate for crashes involving two parties. This study proposes a latent class parameterized correlation bivariate generalized ordered probit (LCp-BGOP) model to examine 32,308 vehicle-vehicle and vehicle-pedestrian crashes at intersections in Taipei City, Taiwan. The model parameterizes thresholds and within-crash correlations of crash severity involving two parties and classifies these crashes into two distinct risk groups: the "Ordinary Crash Severity" (OCS) group and the "High Crash Severity" (HCS) group. The OCS group is mainly two-vehicle crashes involving motorcycles. The HCS group comprises vulnerable road users such as pedestrians and cyclists, mainly in mixed traffic with heavy volumes. The results also show that the effects of party-specific factors contributing to injury severity are greater than those of generic factors. Our study provides invaluable insight into intersection crashes, helping to reduce the severity of injuries in vehicle-vehicle and vehicle-pedestrian crashes.
Asunto(s)
Accidentes de Tránsito , Peatones , Accidentes de Tránsito/estadística & datos numéricos , Humanos , Peatones/estadística & datos numéricos , Taiwán , Masculino , Adulto , Femenino , Persona de Mediana Edad , Adulto Joven , Adolescente , Anciano , Modelos Estadísticos , Motocicletas , Análisis de Clases Latentes , Índices de Gravedad del Trauma , Niño , Heridas y Lesiones/epidemiología , Factores de Riesgo , Planificación AmbientalRESUMEN
The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne's zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.
Asunto(s)
Accidentes de Tránsito , Ciclismo , Caminata , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/prevención & control , Humanos , Ciclismo/estadística & datos numéricos , Ciclismo/lesiones , Caminata/lesiones , Caminata/estadística & datos numéricos , Victoria/epidemiología , Máquina de Vectores de Soporte , Análisis de Sistemas , Conducción de Automóvil/estadística & datos numéricos , Transportes/estadística & datos numéricos , Análisis Espacial , Peatones/estadística & datos numéricos , SeguridadRESUMEN
OBJECTIVES: Road traffic injuries (RTIs) pose a significant public health burden, and more than half of these fatalities are attributed to vulnerable road users (VRUs). This study aimed to evaluate the epidemiology and outcomes of severe RTIs in Korea by focusing on different types of road users. METHODS: This is nationwide retrospective observational study. Using data from the Korean Nationwide Severe Trauma Registry, this study analyzed severe RTI cases from 2016 to 2020. The study included EMS-treated severe trauma patients, defining severe RTI as cases with an injury severity score (ISS) ≥16 or out-of-hospital cardiac arrest (OHCA). The main variable of interest was the road user type, classified as motor vehicle occupants (MVOs), pedestrians, motorcyclists, and bicyclists. Trends and injury characteristics by road user type were analyzed, and multivariate logistic regression was conducted to calculate the adjusted odds ratios (AORs) and 95 % confidence intervals (CIs) of road user type for in-hospital mortality. RESULTS: Of the 143,021 EMS-treated severe trauma cases, 24,464 were included in this study. Pedestrians represented the largest group (n = 8,782; 35.9 %). More than half of the patients died (n = 12,620, 51.6 %), and a high proportion of patients had OHCA (n = 10,048, 41.1 %). There was no significant change in the overall severe RTI numbers from 2016 to 2020, but a decrease in pedestrian cases and an increase in motorcyclist cases were noted (both p for trend<0.05). Low usage of safety devices was observed (28.2 % of motor vehicle occupants used seat belts, 35.9 % of motorcyclists used helmets, and 9.6 % of bicyclists used helmets). Head injuries were most common, particularly among bicyclists (77.0 %) and motorcyclists (69.8 %). Compared to motor vehicle occupants, pedestrians (AOR [95 % CI] 1.12 [1.04-1.20]) and others (AOR [95 % CI] 1.30 [1.02-1.65]) had higher odds of mortality, while motorcyclists (AOR [95 % CI] 0.64 [0.59-0.69]) and bicyclists (AOR [95 % CI] 0.68 [0.60-0.76]) had lower odds of mortality. CONCLUSION: We found varying trends and injury characteristics in severe RTIs according to road user type. Adapting prevention strategies for evolving road user patterns, with particular attention to increasing safety device usage and addressing the high mortality associated with severe RTIs are warranted.
Asunto(s)
Accidentes de Tránsito , Puntaje de Gravedad del Traumatismo , Motocicletas , Sistema de Registros , Heridas y Lesiones , Humanos , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/mortalidad , Masculino , Estudios Retrospectivos , Femenino , República de Corea/epidemiología , Adulto , Persona de Mediana Edad , Heridas y Lesiones/epidemiología , Heridas y Lesiones/mortalidad , Mortalidad Hospitalaria/tendencias , Anciano , Peatones/estadística & datos numéricos , Ciclismo/lesiones , Ciclismo/estadística & datos numéricos , Adulto Joven , Adolescente , Vehículos a Motor/estadística & datos numéricos , Paro Cardíaco Extrahospitalario/epidemiología , Paro Cardíaco Extrahospitalario/mortalidad , Servicios Médicos de Urgencia/estadística & datos numéricosRESUMEN
Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian-vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features' local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle-pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.
Asunto(s)
Accidentes de Tránsito , Entorno Construido , Planificación Ambiental , Aprendizaje Automático , Peatones , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/prevención & control , Humanos , Peatones/estadística & datos numéricos , Algoritmos , Aprendizaje Profundo , SeguridadRESUMEN
Various safety enhancements and policies have been proposed to enhance pedestrian safety and minimize vehicle-pedestrian accidents. A relatively recent approach involves marked sidewalks delineated by painted pathways, particularly in Asia's crowded urban centers, offering a cost-effective and space-efficient alternative to traditional paved sidewalks. While this measure has garnered interest, few studies have rigorously evaluated its effectiveness. Current before-after studies often use correlation-based approaches like regression, lacking effective consideration of causal relationships and confounding variables. Moreover, spatial heterogeneity in crash data is frequently overlooked during causal inference analyses, potentially leading to inaccurate estimations. This study introduces a geographically weighted difference-in-difference (GWDID) method to address these gaps and estimate the safety impact of marked sidewalks. This approach considers spatial heterogeneity within the dataset in the spatial causal inference framework, providing a more nuanced understanding of the intervention's effects. The simplicity of the modeling process makes it applicable to various study designs relying solely on pre- and post-exposure outcome measurements. Conventional DIDs and Spatial Lag-DID models were used for comparison. The dataset we utilized included a total of 13,641 pedestrian crashes across Taipei City, Taiwan. Then the crash point data was transformed into continuous probability values to determine the crash risk on each road segment using network kernel density estimation (NKDE). The treatment group comprised 1,407 road segments with marked sidewalks, while the control group comprised 3,097 segments with similar road widths. The pre-development program period was in 2017, and the post-development period was in 2020. Results showed that the GWDID model outperformed the spatial lag DID and traditional DID models. As a local causality model, it illustrated spatial heterogeneity in installing marked sidewalks. The program significantly reduced pedestrian crash risk in 43% of the total road segments in the treatment group. The coefficient distribution map revealed a range from -22.327 to 2.600, with over 95% of the area yielding negative values, indicating reduced crash risk after installing marked sidewalks. Notably, the impact of crash risk reduction increased from rural to urban areas, emphasizing the importance of considering spatial heterogeneity in transportation safety policy assessments.
Asunto(s)
Accidentes de Tránsito , Causalidad , Planificación Ambiental , Peatones , Seguridad , Análisis Espacial , Humanos , Accidentes de Tránsito/prevención & control , Accidentes de Tránsito/estadística & datos numéricos , Peatones/estadística & datos numéricos , Taiwán , Ciudades , Caminata/lesiones , Caminata/estadística & datos numéricosRESUMEN
BACKGROUND: The COVID-19 pandemic altered traffic patterns worldwide, potentially impacting pedestrian and bicyclists safety in urban areas. In Toronto, Canada, work from home policies, bicycle network expansion, and quiet streets were implemented to support walking and cycling. We examined pedestrian and bicyclist injury trends from 2012 to 2022, utilizing police-reported killed or severely injured (KSI), emergency department (ED) visits and hospitalization data. METHODS: We used an interrupted time series design, with injury counts aggregated quarterly. We fit a negative binomial regression using a Bayesian modeling approach to data prior to the pandemic that included a secular time trend, quarterly seasonal indicator variables, and autoregressive terms. The differences between observed and expected injury counts based on pre-pandemic trends with 95% credible intervals (CIs) were computed. RESULTS: There were 38% fewer pedestrian KSI (95%CI: 19%, 52%), 35% fewer ED visits (95%CI: 28%, 42%), and 19% fewer hospitalizations (95%CI: 2%, 32%) since the beginning of the COVID-19 pandemic. A reduction of 35% (95%CI: 7%, 54%) in KSI bicyclist injuries was observed, but However, ED visits and hospitalizations from bicycle-motor vehicle collisions were compatible with pre-pandemic trends. In contrast, for bicycle injuries not involving motor vehicles, large increases were observed for both ED visits, 73% (95% CI: 49%, 103%) and for hospitalization 108% (95% CI: 38%, 208%). CONCLUSION: New road safety interventions during the pandemic may have improved road safety for vulnerable road users with respect to collisions with motor vehicles; however, further investigation into the risk factors for bicycle injuries not involving motor vehicles is required.
Asunto(s)
Accidentes de Tránsito , Ciclismo , COVID-19 , Servicio de Urgencia en Hospital , Análisis de Series de Tiempo Interrumpido , Heridas y Lesiones , Humanos , COVID-19/epidemiología , Accidentes de Tránsito/estadística & datos numéricos , Ciclismo/lesiones , Ciclismo/estadística & datos numéricos , Heridas y Lesiones/epidemiología , Adulto , Masculino , Femenino , Ontario/epidemiología , Persona de Mediana Edad , Servicio de Urgencia en Hospital/estadística & datos numéricos , SARS-CoV-2 , Peatones/estadística & datos numéricos , Adolescente , Anciano , Pandemias , Adulto Joven , Niño , Caminata/lesiones , Caminata/estadística & datos numéricos , Hospitalización/estadística & datos numéricos , Preescolar , Teorema de Bayes , LactanteRESUMEN
INTRODUCTION: Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved. METHOD: In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country. RESULTS: The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.
Asunto(s)
Accidentes de Tránsito , Ciudades , Humanos , Accidentes de Tránsito/estadística & datos numéricos , Italia/epidemiología , Masculino , Adulto , Ciudades/epidemiología , Femenino , Persona de Mediana Edad , Conducción de Automóvil/estadística & datos numéricos , Modelos Logísticos , Heridas y Lesiones/epidemiología , Anciano , Adulto Joven , Adolescente , Peatones/estadística & datos numéricosRESUMEN
OBJECTIVE: The objective of this study was to describe fatal pedestrian injury patterns in youth aged 15 to 24 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using data from medicolegal death investigations of MVCs occurring in the current Canadian motor vehicle (MV) fleet. METHODS: Based on a systematic literature review, MVC-pedestrian injuries were collated in an injury data collection form (IDCF). The IDCF was coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3-6) that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario (OCCO) database of postmortem examinations done at the Provincial Forensic Pathology Unit (PFPU) in Toronto, Canada, and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs and MV dynamics and pedestrian kinematics.The study was approved by the Western University Health Science Research Ethics Board (Project ID: 113440; Lawson Health Research Institute Approval No. R-19-066). RESULTS: There were 88 youth, including 54 (61.4%) males and 34 (38.6%) females. Youth pedestrians comprised 13.1% (88/670) of all autopsied pedestrians. Cars (n = 25/88, 28.4%) were the most frequent type of vehicle in single-vehicle impacts, but collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) were in the majority. Forward projection (n = 34/88, 38.6%) was the most frequent type of pedestrian kinematics. Regardless of the type of vehicle, there was a tendency in most cases for the median MAISBR ≥ 3 to involve the head and thorax. A similar trend was seen in most of the pedestrian kinematics involving the various frontal impacts. Of the 88 cases, at least 63 (71.6%) were known to be engaged in risk-taking behaviors (e.g., activity on roadway). At least 12 deaths were nonaccidental (8 suicides and 4 homicides). Some activities may have been impairment related, because 26/63 (41.3%) pedestrians undertaking risk-taking behavior on the roadway were impaired. Toxicological analyses revealed that over half of the cases (47/88, 53.4%) tested positive for a drug that could have affected behavior. Ethanol was the most common. Thirty-one had positive blood results. CONCLUSION: A fatal dyad of head and thorax trauma was observed for pedestrians struck by cars. For those pedestrians hit by vehicles with high hood edges, which were involved in the majority of cases, a fatal triad of injuries to the head, thorax, and abdomen/retroperitoneum was observed. Most deaths occurred from frontal collisions and at speeds more than 35 km/h.
Asunto(s)
Accidentes de Tránsito , Peatones , Heridas y Lesiones , Humanos , Accidentes de Tránsito/mortalidad , Accidentes de Tránsito/estadística & datos numéricos , Peatones/estadística & datos numéricos , Adolescente , Adulto Joven , Masculino , Femenino , Heridas y Lesiones/mortalidad , Escala Resumida de Traumatismos , Fenómenos Biomecánicos , Canadá/epidemiología , Ontario/epidemiología , Vehículos a MotorRESUMEN
This study examines the variability in the impacts of factors influencing injury severity outcomes of elderly pedestrians (age >64) involved in vehicular crashes at intersections and non-intersections before, during, and after the COVID-19 pandemic. To account for unobserved heterogeneity in the crash data, a random parameters logit model with heterogeneity in the means approach is utilized to analyze vehicle-elderly pedestrian crash data from Seoul, South Korea, occurring between 2018 and 2022. Preliminary transferability tests revealed instability in factor impacts on injury severity outcomes, highlighting the need to estimate individual models across various road segments and time periods. Thus, the dataset was segregated by crash location (intersection/non-intersection) and period (before, during, and after COVID-19), with individual models estimated for each group. Results obtained from the analyses revealed that back injuries positively influenced fatalities at non-intersections after the pandemic and was negatively associated with fatalities at intersections before the pandemic. Additionally, several indicators demonstrated significant instability in their impact magnitudes across different road segments and crash years. During the pandemic, head injuries increased the probability of fatalities higher at non-intersections. After the pandemic, crosswalk locations decreased the possibility of fatalities more at intersections. Compared to intersection segments, the female indicator reduced the likelihood of fatal injuries at non-intersections more before, during, and after the pandemic. Before the pandemic, much older pedestrians experienced a greater decline in fatalities at intersections than non-intersections. This instability could be attributed to altered mobility patterns stemming from the COVID-19 pandemic. Overall, the study findings highlight the variability of determinants of fatal/severe injury outcomes among elderly pedestrians across various road segments and years, with the underlying cause of this fluctuation remaining unclear. Furthermore, the findings revealed that accounting for heterogeneity in the means of random parameters enhances model fit and provides valuable insights for safety professionals. The factor impact variability in the estimated models carries significant implications for elderly pedestrian safety, especially in scenarios where precise projections of the effects of alternative safety measures are essential. Road safety experts can leverage these findings to refine or update current policies to enhance elderly pedestrian safety at intersections and non-intersections.
Asunto(s)
Accidentes de Tránsito , COVID-19 , Peatones , Humanos , COVID-19/mortalidad , COVID-19/epidemiología , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/mortalidad , Anciano , Peatones/estadística & datos numéricos , República de Corea/epidemiología , Heridas y Lesiones/epidemiología , Heridas y Lesiones/mortalidad , Masculino , Femenino , Anciano de 80 o más AñosRESUMEN
Street space plays a critical role in pedestrian safety, but the influence of fine-scale street environment features has not been sufficiently understood. To analyze the effect of the street environment at the link level, it is essential to account for the spatial variation of pedestrian exposure across street links, which is challenging due to the lack of detailed pedestrian flow data. To address these issues, this study proposes to extract link-level pedestrian exposure from spatially ubiquitous street view images (SVIs) and investigate the impact of fine-scale street environment on pedestrian crash risks, with a particular focus on pedestrian facilities (e.g., crossing and sidewalk design). Both crash frequency and severity are analyzed at the link level, with the latter incorporating two distinct aggregation metrics: maximum severity and medium severity. Using Hong Kong as a case study, the results show that the link-level pedestrian exposure extracted from SVIs can lead to better model fit than alternative zone-level measurements. Specifically, higher pedestrian exposure is found to increase the total pedestrian crash frequency, while reducing the risk of serious injuries or fatalities, confirming the "safety in numbers" effect for pedestrians. Pedestrian facilities are also shown to influence pedestrian crash frequency and severity in different ways. The presence of crosswalks can increase crash frequency, but denser crosswalk design mitigates this effect. In addition, two-side sidewalks can increase crash frequency, while the absence of sidewalks leads to higher risks of crash severity. These findings highlight the importance of fine-scale street environment and pedestrian facility design for pedestrian safety.
Asunto(s)
Accidentes de Tránsito , Planificación Ambiental , Peatones , Humanos , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/prevención & control , Peatones/estadística & datos numéricos , Hong Kong , Seguridad , Caminata/lesiones , Entorno ConstruidoRESUMEN
Facilitating proactive pedestrian safety management, the application of extreme value theory (EVT) models has gained popularity due to its extrapolation capabilities of estimating crashes from their precursors (i.e., conflicts). However, past studies either applied EVT models for crash risk analysis of autonomous vehicle-pedestrian interactions or human-driven vehicle-pedestrian interactions at signalised intersections. However, our understanding of human-driven vehicle-pedestrian interactions remains elusive because of scant evidence of (i) EVT models' application for heterogeneous traffic conditions, (ii) appropriate set of determinants, (iii) which EVT approach to be used, and (iv) which conflict measure is appropriate. Addressing these issues, the objective of this study is to investigate pedestrian crash risk analysis in heterogeneous and disordered traffic conditions, where drivers do not follow lane disciplines. Eleven-hour video recording was collected from a busy pedestrian crossing at a midblock location in India and processed using artificial intelligence techniques. Vehicle-pedestrian interactions are characterised by two conflict measures (i.e., post encroachment time and gap time) and modelled using block maxima and peak over threshold approaches. To handle the non-stationarity of pedestrian conflict extremes, several explanatory variables are included in the models, which are estimated using the maximum likelihood estimation procedure. Modelling results indicate that the EVT models provide reasonable estimates of historical crash records at the study location. From the EVT models, a few key insights related to vehicle-pedestrian interactions are as follows. Firstly, a comparison of EVT models shows that the peak over threshold model outperforms the block maxima model. Secondly, post encroachment time conflict measure is found to be appropriate for modelling vehicle-pedestrian interactions compared to gap time. Thirdly, pedestrian crash risk significantly increases when they interact with two-wheelers in contrast with interactions involving buses where the crash risk decreases. Fourthly, pedestrian crash risk decreases when they cross in groups compared to crossing individually. Finally, pedestrian crash risk is positively related to average vehicle speed, pedestrian speed, and five-minute post encroachment time counts less than 1.5 s. Further, different block sizes are tested for the block maxima model, and the five-minute block size yields the most accurate and precise pedestrian crash estimates. These findings demonstrate the applicability of extreme value analysis for heterogeneous and disordered traffic conditions, thereby facilitating proactive safety management in disordered and undisciplined lane conditions.
Asunto(s)
Accidentes de Tránsito , Peatones , Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/prevención & control , Humanos , Peatones/estadística & datos numéricos , Medición de Riesgo/métodos , India , Grabación en Video , Modelos Teóricos , Inteligencia Artificial , Funciones de Verosimilitud , Planificación AmbientalRESUMEN
Traffic-related pedestrian deaths in the United States reached a 40-year high in 2021. Each year, pedestrians also suffer nonfatal traffic-related injuries requiring medical treatment. Near real-time emergency department visit data from CDC's National Syndromic Surveillance Program during January 2021-December 2023 indicated that among approximately 301 million visits identified, 137,325 involved a pedestrian injury (overall visit proportion = 45.62 per 100,000 visits). The proportions of visits for pedestrian injury were 1.53-2.47 times as high among six racial and ethnic minority groups as that among non-Hispanic White persons. Compared with persons aged ≥65 years, proportions among those aged 15-24 and 25-34 years were 2.83 and 2.61 times as high, respectively. The visit proportion was 1.93 times as high among males as among females, and 1.21 times as high during September-November as during June-August. Timely pedestrian injury data can help collaborating federal, state, and local partners rapidly monitor trends, identify disparities, and implement strategies supporting the Safe System approach, a framework for preventing traffic injuries among all road users.
Asunto(s)
Accidentes de Tránsito , Servicio de Urgencia en Hospital , Peatones , Heridas y Lesiones , Adolescente , Adulto , Anciano , Niño , Preescolar , Femenino , Humanos , Lactante , Masculino , Persona de Mediana Edad , Adulto Joven , Accidentes de Tránsito/estadística & datos numéricos , Distribución por Edad , Visitas a la Sala de Emergencias , Servicio de Urgencia en Hospital/estadística & datos numéricos , Peatones/estadística & datos numéricos , Estados Unidos/epidemiología , Heridas y Lesiones/epidemiología , Etnicidad/estadística & datos numéricos , Grupos Raciales/estadística & datos numéricosRESUMEN
BACKGROUND: Plans to phase out fossil fuel-powered internal combustion engine (ICE) vehicles and to replace these with electric and hybrid-electric (E-HE) vehicles represent a historic step to reduce air pollution and address the climate emergency. However, there are concerns that E-HE cars are more hazardous to pedestrians, due to being quieter. We investigated and compared injury risks to pedestrians from E-HE and ICE cars in urban and rural environments. METHODS: We conducted a cross-sectional study of pedestrians injured by cars or taxis in Great Britain. We estimated casualty rates per 100 million miles of travel by E-HE and ICE vehicles. Numerators (pedestrians) were extracted from STATS19 datasets. Denominators (car travel) were estimated by multiplying average annual mileage (using National Travel Survey datasets) by numbers of vehicles. We used Poisson regression to investigate modifying effects of environments where collisions occurred. RESULTS: During 2013-2017, casualty rates per 100 million miles were 5.16 (95% CI 4.92 to 5.42) for E-HE vehicles and 2.40 (95%CI 2.38 to 2.41) for ICE vehicles, indicating that collisions were twice as likely (RR 2.15; 95% CI 2.05 to 2.26) with E-HE vehicles. Poisson regression found no evidence that E-HE vehicles were more dangerous in rural environments (RR 0.91; 95% CI 0.74 to 1.11); but strong evidence that E-HE vehicles were three times more dangerous than ICE vehicles in urban environments (RR 2.97; 95% CI 2.41 to 3.7). Sensitivity analyses of missing data support main findings. CONCLUSION: E-HE cars pose greater risk to pedestrians than ICE cars in urban environments. This risk must be mitigated as governments phase out petrol and diesel cars.
Asunto(s)
Accidentes de Tránsito , Automóviles , Peatones , Humanos , Estudios Transversales , Peatones/estadística & datos numéricos , Reino Unido , Accidentes de Tránsito/prevención & control , Accidentes de Tránsito/estadística & datos numéricos , Seguridad , Masculino , Femenino , Adulto , Población Rural , Heridas y Lesiones/prevención & control , Heridas y Lesiones/epidemiologíaRESUMEN
INTRODUCTION: Pedestrians are considered the most vulnerable and complex road users as human behavior constitutes one of the fundamental reasons for traffic-related incidents involving pedestrians. However, the role of health literacy as a predictor of Pedestrian safety behavior remains underexplored. Therefore, the current study was designed to examine the level of health literacy and its association with the safety behavior of adult pedestrians in the city of Tabriz. METHODS: This cross-sectional analytical study was conducted among individuals aged 18 to 65 years in the metropolitan area of Tabriz from January to April 2023. Data were collected using the HELIA standard questionnaire (Health Literacy Instrument for adults), comprising 33 items across 5 domains (access, reading, understanding, appraisal, decision-making and behavior), as well as the Pedestrian Behavior Questionnaire (PBQ) consisting of 29 items. Data were analyzed using descriptive and analytical statistics (independent t-tests, ANOVA, and Pearson correlation coefficient) via SPSS-22 software. RESULTS: Based on the results, 94% (376 individuals) had excellent health literacy levels, and their safety behavior scores were at a good level. Health literacy and safety behavior were higher among the age group of 31 to 45 years, women, married individuals, those who read books, and individuals with higher education. However, safety behavior showed no significant association with education level (P > 0.05). There was a significant and positive relationship between health literacy and all its domains and pedestrian safety behavior (r = 0.369, P < 0.001). CONCLUSION: This study underscores the significant impact of health literacy on pedestrians' safety behavior. The findings reveal that higher levels of health literacy are associated with better safety behavior among individuals aged 18 to 63. Demographic factors such as age, gender, marital status, and education level also play a role in shaping both health literacy and safety behavior. By recognizing these relationships, interventions can be tailored to improve health literacy levels and promote safer pedestrian practices, ultimately contributing to a healthier and safer community in Tabriz city.