Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones: The Empirical Analysis of Beijing

Characteristics of media-reported road traffic crashes related to new energy vehicles in China

INTRODUCTION

New energy vehicles (NEVs) refer to vehicles entirely or primarily powered by energy sources outside of conventional fuels. As the number of NEVs increases, road traffic crashes related to NEVs have emerged as a new challenge for road traffic injury prevention. However, basic epidemiological data are scarce concerning NEV-related crashes.

METHODS

Using media-reported crash data from the Automated Road Traffic Crash Data Platform (ARTCDP), a data platform developed and validated by our research group to gather eligible reports automatically and systematically from online Chinese media concerning road traffic crashes, we examined the characteristics of new energy vehicles between 2015 and 2022 in China.

RESULTS

The ARTCDP captured 2,927 crashes related to NEVs from 2015 to 2022, accounting for 1.1% of total number of motor vehicle-related crashes indexed by the ARTCDP during the same time period. Of them, 2,262 (77.3%) crashes occurred in east and central China. NEV-related traffic crashes occurred most often on urban roads (68.8%), well-lit roads (72.2%), roads without adequate safety infrastructure facilities (63.2%), and at intersections (78.7%). 1,864 media reports described the reason for the crash, with 44.1% listing two or more factors to explain the NEV-related crashes. Brake system failure and dangerous or improper driving operations were more frequently reported in NEV-related crashes than in other motorvehicle crashes (55.6% vs. 18.3% and 37.5% vs. 20.8%, P < 0.01). NEV-related crashes occurred more often on rainy days and on foggy or smoggy days than other motor-vehicle crashes (83.6% vs. 72.2% and 4.1% vs. 0.7%, P < 0.01).

CONCLUSIONS

Media-reported news elucidate distinct characteristics of road traffic crashes involving NEVs versus other motor vehicles in China.

PRACTICAL APPLICATIONS

NEV-related crashes represent an emerging road traffic safety challenge in China and worldwide. Characteristics revealed by media-reported NEV-related crashes merit the attention of policymakers, automobile industry, researchers, and law enforcement.

Factors associated with the severity of road traffic injuries from emergency department based surveillance system in two Mexican cities

Background

Limited data from low- and middle-income countries (LMICs) on the severity of road traffic injuries (RTIs) and their relation to different variables of interest are routinely obtained. Knowledge on this subject relies on evidence from high-income countries, which might not be the same as in LMICs. This information is greatly needed to advance and inform local and regional efforts towards the United Nations’ Decade of Action and the Sustainable Development Goals.

Methods

From May 2012 to November 2014, a RTI surveillance system was implemented in two referral hospitals in two Mexican cities, León and Guadalajara, with the objective of exploring the relationship between Injury Severity Score (ISS) and different sociodemographic characteristics of the injured as well as different variables related to the event and the environment. All individuals suffering RTIs who visited the Emergency Rooms (ER) were included after granting informed consent. A Zero-Truncated Negative Binomial Model was employed to explore the statistical association between ISS and variables of interest.

Results

3024 individuals participated in the study: 2185 (72.3%) patients from León and 839 patients (27.7%) from Guadalajara. Being male, in the 20–59 age-group, having less schooling, events occurring in Guadalajara, on Sundays, at night, and arriving at ER via public/private ambulance were all associated with an increased log count of ISS. Found a significant interaction effect (p-value< 0.05) between type of road user and alcohol intake six hours before the accident on severity of the injury (ISS). The use of illicit drugs, cellphones and safety devices during the event showed no association to ISS.

Conclusions

Our study contributes to the statistical analysis of ISS obtained through RTI hospital surveillance systems. Findings might facilitate the development and evaluation of focused interventions to reduce RTIs in vulnerable users, to enhance ER services and prehospital care, and to reduce drink driving.

Applying count time series to assess 13-year pedestrian mortality trend caused by traffic accidents in East-Azerbaijan province, Iran

In populated cities, pedestrian mortality is higher compared to other traffic mortalities. The current study aimed to describe the trend of pedestrian mortality in the East-Azerbaijan (Northwest of Iran) province from 2006 to 2019 and find the factors that affect the mortality number. Pedestrian mortality data from March 2006 to March 2019 was obtained from the Legal Medicine Organization database of Iran. Generalized Linear Auto Regressive Moving Average (GLARMA) models were used to assess the trend, and affecting factors of pedestrian mortality. According to the traffic accident data from 21 March 2006 until 20 March 2019 in East-Azerbaijan 24.11% of mortalities are related to pedestrians. Pedestrian mortality had a decreasing seasonal trend during 2006-2019. The result of the GLARMA model showed that age >65, being non-educated, cases with head trauma death cause, pre-hospital death, accident inside the city, vehicle type and self-employed jobs had a direct relation to pedestrian's mortality.

Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-Based Cancer Registry Study

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, previous studies providing adequate evidence to support screening for SPCs in endometrial cancer are lacking. This study aimed to develop effective risk prediction models of second primary endometrial cancer (SPEC) in women with obesity (body mass index (BMI) > 25) and included datasets on the incidence of SPEC and the other risks of SPEC in 4480 primary cancer survivors from a hospital-based cancer registry database. We found that obesity plays a key role in SPEC. We used 10 independent variables as predicting variables, which correlated to obesity, and so should be monitored for the early detection of SPEC in endometrial cancer. Our proposed scheme is promising for SPEC prediction and demonstrates the important influence of obesity and clinical data representation in all cases following primary treatments. Our results suggest that obesity is still a crucial risk factor for SPEC in endometrial cancer.

Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA)

Background

Identifying hidden patterns and relationships among the features of the Fatal Pedestrian Road Traffic Injuries (FPRTI) can be effective in reducing pedestrian fatalities. This study is thus aimed to detect the patterns among the fatally injured pedestrians due to FPRTI in East Azerbaijan province, Iran.

Methods

This descriptive-analytic research was carried out based on the data of all 1782 FPRTI that occurred in East Azerbaijan, Iran from 2010 to 2019 collected by the forensic organization. Categorical Principal Component Analysis (CATPCA) was performed to recognize hidden patterns in the data by extracting principal components from the set of 13 features of FPRTI. The importance of each component was assessed by using the variance accounted for (VAF) index.

Results

The optimum number of components to fit the CATPCA model was six which explained 71.09% of the total variation. The first and most important component with VAF = 22.04% contained the demographic and socioeconomic characteristics of the killed pedestrians. The second-ranked component with VAF = 12.96% was related to the injury type. The third component with VAF = 10.56% was the severity of the injury. The fourth component with VAF = 9.07% was somehow related to the knowledge and observance of the traffic rules. The fifth component with VAF = 8.63% was about the quality of medical relief and finally, the sixth component with VAF = 7.82% dealt with environmental conditions.

Conclusion

CATPCA revealed hidden patterns among the fatally injured pedestrians in the form of six components. The revealed patterns showed that some interactions between correlated features led to a higher mortality rate.

Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou, China

INTRODUCTION

With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors.

METHOD

The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark.

RESULTS

The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels.

Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals

Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes datasets from 19,270 patients, provided by an adult health examination program from 32 chain clinics and three special physical examination centers, between 2015 and 2019. There were 11 independent variables, and the glomerular filtration rate (GFR) was used as the predictive variable. The C4.5 decision tree algorithm outperformed the three comparison models for predicting early CKD based on accuracy, sensitivity, specificity, and area under the curve metrics. It is, therefore, a promising method for early CKD prediction. The experimental results showed that Urine protein and creatinine ratio (UPCR), Proteinuria (PRO), Red blood cells (RBC), Glucose Fasting (GLU), Triglycerides (TG), Total Cholesterol (T-CHO), age, and gender are important risk factors. CKD care is closely related to primary care level and is recognized as a healthcare priority in national strategy. The proposed risk prediction models can support the important influence of personality and health examination representations in predicting early CKD.

Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea

Pedestrian-vehicle crashes can result in serious injury to pedestrians, who are exposed to danger when in close proximity to moving vehicles. Furthermore, these injuries can be considerably serious and even lead to death in a manner that varies depending on the pedestrian's age. This is because the pedestrian's physical characteristics and behaviors, particularly in relation to roads with moving vehicles, differ depending on the pedestrian's age. This study examines the determinants of pedestrian injury severity by pedestrian age using binary logistic regression. Factors in the built environment, such as road characteristics and land use of the places where pedestrian crashes occurred, were considered, as were the accident characteristics of the pedestrians and drivers. The analysis determined that the accident characteristics of drivers and pedestrians are more influential in pedestrian-vehicle crashes than the factors of the built environmental characteristics. However, there are substantial differences in injury severity relative to the pedestrian's age. Young pedestrians (aged under 20 years old) are more likely to suffer serious injury in school zones; however, no association between silver zones and injury severity is found for elderly pedestrians. For people in the age range of 20-39 years old, the severity of pedestrian injuries is lower in areas with more crosswalks and speed cameras. People in the age range of 40-64 years old are more likely to be injured in areas with more neighborhood streets and industrial land use. Elderly pedestrians are likely to suffer fatal injuries in areas with more traffic signals. This study finds that there are differences in the factors of pedestrian injury severity according to the age of pedestrians. Therefore, it is suggested that concrete and efficient policies related to pedestrian age are required to improve pedestrian safety and reduce pedestrian-vehicle crashes.

Exploring Factors Influencing Injury Severity of Vehicle At-fault Accidents: A Comparative Analysis of Passenger and Freight Vehicles

The objective of this study is to find factors influencing the injury severity of vehicle at-fault accidents in Shenyang (China), and discuss the commonalities and differences between passenger and freight vehicle accidents. We analyzed 1647 traffic accidents from 2015 to 2017, in which motor vehicles were fully or mainly responsible, including 1164 traffic accidents caused by passenger vehicles and 483 traffic accidents caused by freight vehicles. Twenty influencing factors from the aspects of accident, driver, time, space and environmental attributes are analyzed to find their statistical connection with injury severity using the binary logistic regression model. For passenger vehicles, five influencing factors (side collision; illegal act while driving; hit-and-run; season and administrative division), showed statistically significant correlations with the injury severity. For freight vehicles, three influencing factors (illegal act while driving; season and administrative division), showed statistically significant correlations with the injury severity. Illegal act while driving is the only common influencing factor for the injury severity of both passenger and freight vehicle accidents. Side collision and hit-and-run are significant influencing factors for the injury severity of passenger vehicle accidents, but not for freight vehicle accidents. Season and administrative division present different results on influencing passenger and freight vehicle accidents. Based on these results, measures including driver education and road infrastructure improvement could be implemented to reduce the injury severity of accidents in passenger and freight vehicles.

Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors

The purpose of this paper is to investigate the existence of stratification heterogeneity in traffic accidents in Shenzhen, what factors influence the casualties, and the interaction of those factors. Geographical detection methods are used for the analysis of traffic accidents in Shenzhen. Results show that spatial stratification heterogeneity does exist, and the influencing factors of fatalities and injuries are different. The traffic accident causes and types of primary responsible party have a strong impact on fatalities and injuries, followed by zones and time interval. However, road factors, lighting, topography, etc., only have a certain impact on fatalities. Drunk driving, speeding over 50%, and overloading are more likely to cause more casualties than other illegal behaviors. Speeding over 50% and speeding below 50% have significant different influences on fatalities, while the influences on injuries are not obvious, and so do drunk driving (Blood Alcohol Concentration ≥ 0.08) and driving under the influence of alcohol (0.08 > Blood Alcohol Concentration ≥ 0.02). Both pedestrians and cyclists violating the traffic law are vulnerable to fatality. Heavy truck overloading is more likely to cause major traffic accidents than minibuses. More importantly, there are nonlinear enhanced interactions between the influencing factors, the combination of previous non-significant factors and other factors can have a significant impact on the traffic accident casualties. The findings could be helpful for making differentiated prevention and control measures for traffic accidents in Shenzhen and the method selection of subsequent research.

Analysis of Driving Behavior Based on Dynamic Changes of Personality States

This study investigated the relationship between personality states and driving behavior from a dynamic perspective. A personality baseline was introduced to reflect the driver's trait level and can be used as a basic reference for the dynamic change of personality states. Three kinds of simulated scenarios triggered by pedestrian crossing the street were established using a virtual reality driving simulator. Fifty licensed drivers completed the driving experiments and filled in the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) questionnaire to measure the drivers' personality baselines. Key indicators were quantified to characterize the five types of personality states by K-means clustering algorithm. The results indicated that the high-risk situation had a greater impact on the drivers, especially for drivers with openness and extroversion. Furthermore, for the drivers of extroverted personality, the fluctuation of personality states in the high-risk scenario was more pronounced. This paper put forward a novel idea for the analysis of driving behavior, and the research results provide a personalized personality database for the selection of different driving modes.

Built Environment Features and Pedestrian Accidents: An Italian Retrospective Study

Daily walking is a recommended physical activity. It can be an all-age suitable, environment-friendly transport option. However, traffic crashes are a widely recognized risk factor, associated with drivers’ errors or a combination of several environmental factors, including physical characteristics of the road space. The aim of this study was to assess the characteristics of built environments on pedestrian safety. Data on road accidents that had occurred between 2005 and 2015, in Alghero, Italy, were retrieved and matched with spatial and functional street qualities. On-street parking was found to increase the risk of pedestrian accidents by about two times, whereas, narrow travel lanes and intersections reduced the incidence of crashes and their public relevance. These field results could inform urban health and spatial planning policies with the final goal of improving health and providing more sustainable models of urban organization.