Risk factors associated with traffic violations and accident severity in China

Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework

The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into three levels (i.e., slight, ordinary, severe) based on point deduction, and explore the patterns of serious traffic violations (i.e., ordinary, severe) using multi-source data. This paper designed an interpretable machine learning framework, in which four popular machine learning models were enhanced and compared. Specifically, adaptive synthetic sampling method was applied to overcome the effects of imbalanced data and improve the prediction accuracy of minority classes (i.e., ordinary, severe); multi-objective feature selection based on NSGA-II was used to remove the redundant factors to increase the computational efficiency and make the patterns discovered by the explainer more effective; Bayesian hyperparameter optimization aimed to obtain more effective hyperparameters combination with fewer iterations and boost the model adaptability. Results show that the proposed interpretable machine learning framework can significantly improve and distinguish the performance of four popular machine learning models and two post-hoc interpretation methods. It is found that six of the top ten important factors belong to multi-scale built environment attributes. By comparing the results of feature contribution and interaction effects, some findings can be summarized: ordinary and severe traffic violations have some identical influencing factors and interactive effects; have the same influencing factors or the same combinations of influencing factors, but the values of the factors are different; have some unique influencing factors and unique combinations of influencing factors.

Injury severity analysis of e-bike riders in China based on the in-vehicle recording video crash data: a random parameter ordered logit model

This study investigates the impacts of various factors on e-bike riders' injury severity in crashes with motor vehicles, based on the in-vehicle recording video crash data in China. Variables from human factors, vehicle characteristics, road conditions, and environmental attributes are extracted from the video, especially for drivers and riders' illegal and avoidance behaviour before the crash, and sun shade canopy use. Results of mixed logit models reveal that drivers' speeding, running red lights, slow-down and swerve behaviour, light trucks, heavy trucks, and buses have significantly varied impacts on riders' injury. Moreover, both drivers and riders' illegal behaviour leads to an increased injury, while their avoidance behaviour before crashes can protect riders. In addition, types of visual obstacles, accidents occurring at night, large vehicles' involvement, and the application of sunshade canopies by riders increased the probability of severe injury, while helmet use can protect riders in accidents with motor vehicles.

Factors influencing the road accidents in low and middle-income countries: a systematic literature review

This paper studies the main factors affecting road traffic accidents (RTAs) using a systematic review. The primary focus is on factors related to road characteristics and driver behaviours. This review also addresses the socioeconomic and demographic factors to provide a clear overview of which groups suffer the most from RTAs. Several factors were found to affect RTAs, notably road characteristics: highways, high-speed roads, unplanned intersections and two-way roads without dividers; driver behaviours: reckless/aggressive driving and riding, excessive speeding, unawareness of traffic laws, and not using safety equipment; and vehicle types: four and two-wheeled. This review found that male and economically productive people with less education were mostly associated with RTAs. In addition, for most of the low and middle-income countries analyzed, there is a lack of quality data relating to RTAs. Nevertheless, this review provides researchers and policy makers with a better understanding of road accidents for improving road safety.

Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?

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.

Research on Active Safety Situation of Road Passenger Transportation Enterprises: Evaluation, Prediction, and Analysis

The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS.

Critical risk factors associated with fatal/severe crash outcomes in personal mobility device rider at-fault crashes: A two-step inter-cluster rule mining technique

Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key factors, particularly those causing fatal/severe outcomes. Remarkably, there exists a noticeable gap in the research concerning the analysis of determinants behind fatal/severe PMD crashes, specifically in PMD rider-at-fault collisions. This study addresses this gap by identifying uniform groups of PMD rider-at-fault crashes and investigating cluster-specific key factor associations and determinants of fatal/severe crash outcomes using Seoul's PMD rider-at-fault crash data from 2017 to 2021. A comprehensive two-step framework, integrating Cluster Correspondence Analysis (CCA) and Association Rules Mining (ARM) techniques is employed to segment PMD rider-at-fault crash data into homogeneous groups, revealing unique risk factor patterns within each cluster and further exploring the combination of factors associated with fatal/severe PMD rider-at-fault crash outcomes. CCA revealed three distinct groups: PMD-vehicle, PMD-pedestrian, and single-PMD crashes. From the ARM, it was found that fatal/severe crashes were linked to dry road conditions, male PMD users, and weekdays, irrespective of the cluster. Whereas speeding violations and side collisions were associated with fatal/severe PMD-vehicle rider-at-fault crashes, traffic control violations were related to fatal/severe PMD-pedestrian rider-at-fault crashes at pedestrian crossings. Unsafe riding practices predominantly caused single-PMD crashes during daytime hours. From the findings, engineering improvements, awareness campaigns, education, and law enforcement actions are recommended. The new insights gleaned from this research provide a foundation for informed decision-making and the implementation of policies designed to enhance PMD safety.

Identification and screening of key traffic violations: based on the perspective of expressing driver's accident risk

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.

Identification and analysis of accident black spots using Geographic Information System (GIS): A study on Kushtia-Jhenaidah national highway (N704), Bangladesh

Road accidents, mostly on national highways, pose a significant public health and economic burden in Bangladesh, requiring in-depth analysis for road safety measures. This study comprehensively analyzes accident trends, characteristics, causes, and consequences by identifying the accident black spots on the Kushtia-Jhenaidah National Highway (N704). Accident records from 2017 to 2021 were collected from nearby police stations. Additionally, using a cluster random sampling approach, a questionnaire survey with 100 respondents (50% drivers and 50% general road users) was also conducted to capture diverse perceptions and behaviors. The study utilizes descriptive methods, such as trends analysis and frequency distributions, alongside spatial analysis techniques, including severity index, Kernel Density Estimation, and hotspot analysis. Findings indicate a decrease in accidents from 2018 to 2021, yet a concerning rise in fatalities in 2021. Trucks (47.9%) emerge as the primary contributor among 169 vehicles involved in accidents. Head-on collisions (36%) are prevalent, attributed to both human and environmental factors, including driver inexperience (56%), mobile phone use while driving (78%), lack of proper training (12%), overspeeding (28.3%), and nighttime driving (54%) influenced by seasons and land use. Mostly, victims aged from 20 to 40, where men are more affected by fatalities (70.7%) and women by injuries (86.3%). Out of 35 identified accident spots, including Battail, Bittipara Bazar, Laxmipur Bazar, Modhupur Bazar, IU Main Gate, Sheikhpara Bazar, and DM College Front, are designated as blackspot zones based on the frequency of accidents, deaths, and injuries. The study concludes by recommending targeted interventions, driver training, infrastructure improvements, regulatory measures, and victim assistance in collaboration with local and national agencies to enhance road safety and mitigate accident risks.

Mining the accident causes of railway dangerous goods transportation: A Logistics-DT-TFP based approach

Accurately and quickly mining the hidden information in railway dangerous goods transportation (RDGT) accident reports has great significance for its safety management. In this paper, a data mining method Logistics-DT-TFP is proposed for analysing the causes of RDGT accidents. Firstly, analyse the transportation process, extract the cause of the accident, and classify the severity of the accident. Then, using ordered multi-classification Logistic regression for correlation calculation, qualitatively judge and quantitatively analyse the relationship between each cause and the severity of the accident. The feature tags of the Decision Tree (DT) are screened, the C5.0 algorithm is used to obtain the accident coupling rules. Next, the FP-Growth algorithm is used to mine frequent itemsets, and TOP-K is used to improve it and output effective association rules with the degree of lift as the indicator, which avoids repeated traversal of the database, shortens the time complexity, and reduces the impact of the minimum support setting on the calculation results. The degree of lift among the causes in the coupling chain is calculated as a complement to the extraction of coupling rules. Finally, based on the analysis and mining results of case study, the management strategies for railway dangerous goods are proposed.

A systematic approach to macro-level safety assessment and contributing factors analysis considering traffic crashes and violations

During rapid urbanization and increase in motorization, it becomes particularly important to understand the relationships between traffic safety and risk factors in order to provide targeted improvements and policy recommendations. Violations and police enforcement are key variables, but the endogenous relationship between crashes and violations has made these variables unreliable and has limited their use. To manage this problem, this study developed a systematic approach for the joint modeling of crashes and violations to identify crash and violation hotspots and examine the mechanisms underlying macro-level contributing factors. Socio-economic, road network, public facility, traffic enforcement, and land use intensity data from 115 towns in Suzhou, China, were collected as independent variables. A bivariate negative binomial spatial conditional autoregressive model (BNB-CAR) and the potential for safety improvement (PSI) method were adopted to identify crash-prone and violation-prone areas, and an interpretable machine learning framework was applied to explore the factors' effects by area. Results showed that the proposed framework was able to accurately identify problem areas and quantify the impact of key factors, which, in Suzhou, were the number of traffic police and their daily patrol time. Considering such enforcement-related information provided important insights into reducing crash and violation frequency; for example, keeping the number of traffic police and daily patrol time under certain thresholds (number of police lower than 11 and patrol time lower than 2.3 h in this sample) was as effective as increasing these numbers for reducing the probability of high-crash and high-violation areas. The proposed approach can help traffic administrators identify the key contributing factors, especially enforcement factors, in crash-prone and violation-prone areas and provide guidelines for improvement.

An integrated data- and theory-driven crash severity model

For crash severity modeling, researchers typically view theory-driven models and data-driven models as different or even conflicting approaches. The reason is that the machine-learning models offer good predictability but weak interpretability, while the latter has robust interpretability but moderate predictability. In order to alleviate the tension between them, this study proposes an integrated data- and theory-driven crash-severity model, known as Embedded Fusion model based on Text Vector Representations (TVR-EF), by leveraging the complementary strengths of both. The model specification consists of two parts. (i) the data-driven component not only mitigate the deficiencies of traditional econometric models, where one-hot encoding is frequently used and makes it impossible to observe semantic relatedness between variable categories, but also enhances the interpretability for the relationship between crash severity and potential influencing factors using the learned embedding weight matrix. (ii) In the theory-driven component, the multinomial logit model is implemented as a 2D-Convolutional Neural Network (2D-CNN) to increase flexibility and decrease dependency on prior knowledge for different crash-severity outcomes. A crash dataset from Guangdong Province, China, is utilized to estimate the TVR-EF model, which is then benchmarked against two traditional econometric models and three widely used machine-learning models. Results indicate that TVR-EF model does not only improve the predictive performance but also makes it easier to interpret.

Traffic violations analysis: Identifying risky areas and common violations

Road traffic accidents caused by traffic violations are a major public health issue that results in loss of lives and economic costs. Therefore, it is important to prioritize road safety measures that reduce the incidence and severity of accidents. In this study, we suggest an incremental road safety strategy that identifies high-risk areas and common traffic violations in order to prioritize further enforcement. In fact, by analyzing data on traffic violations in different districts and comparing them to the overall average using the Kolmogorov-Smirnov (KS) test, risky areas are identified and the most common violations are detected. We performed a comparison between several types of clustering optimizations to spot clusters to be enforced in order to reduce violations. Our results indicate that some Districts have a higher risk of traffic violations than others do, and some violations (Speeding, Registration, License, Belt, Influence, Phone, etc.) are more common than others are. We also find that k-means clustering provides the best results for identifying clusters of violations records and optimizing enforcement strategies. Our findings can be adopted by law enforcement agencies to focus on high-risk areas and target the most common violations in order to optimize their resources and improve road safety.

Prediction of drowsiness using EEG signals in young Indonesian drivers

Indonesia is among the countries with the highest accident rates in the world. Fatigue and drowsiness are among the main causes of the increased risks of accidents in the road transport sector. Sleep-related factors (quality and quantity, time of day) and work-related factors significantly affect the development of fatigue. The EEG signal indicator is often referred to as the gold standard for measuring fatigue and drowsiness. However, previous studies focused primarily on the trends of EEG signals under certain conditions but overlooking the development of drowsiness indicators based on EEG signals. Furthermore, existing studies still do not agree on what parameters in the EEG signal indicator are best at detecting drowsiness. Thus, this study aims to design an EEG signal-based drowsiness indicator under simulated driving conditions. Drowsy drivers were monitored through EEG signal indicators and subjective assessments. The methods used in this study include statistical significance tests, logistic regression, and support vector machine. The results showed that sleep deprivation had a significant effect on increasing alpha, beta, and theta waves. In addition, driving duration significantly increased the theta power and all EEG ratios and decreased the beta power in the alert group. The ratio of (θ + α)/β and θ/β in the SD group also showed a considerable increase in the end of driving. Furthermore, sleep status and driving duration both influenced subjective sleepiness. EEG signals combined with sleep status and driving duration factors generated acceptable model accuracies (77.1% and 90.2% in training and testing, respectively), with 90.5% sensitivity and 90% specificity in data test. Support vector machine showed better classification than that of logistics regression, with the linear kernel as the best classifier. Theta power had the highest effect in the model compared with other EEG signals.

The road safety and risky behavior analysis of delivery vehicle drivers in China

The delivery industry has seen dramatic growth in demand and scale in China. Due to the stock limitations and delivery time restrictions, the couriers may commit traffic violations while delivering, resulting in a pessimistic road safety situation. This study aims to reveal critical factors that influence delivery vehicle crash risks. A cross-sectional structured questionnaire survey is conducted to collect demographic attributes, workload, work emotions, risky driving behavior, and road crash involvement data among 824 couriers in three developed regions of China. The collected data is then analyzed through an established path model to identify the contributing factors of delivery road crash risks and risky behaviors. The road crash risk level (RCRL) indicator is defined by taking into consideration both frequency and severity. While the risky behaviors are defined by both their frequency and correlations to crash risks. The results indicate that 1) Beijing-Tianjin Urban Agglomeration has the highest road crash frequency and RCRL; 2) distracted driving and wrong-lane-use are among the top three risky behaviors for both Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration. For Beijing-Tianjin Urban Agglomeration, distracted driving, aggressive driving, and lack of protection are the top three risky behaviors; 3) time demand and personal efforts are important factors contributing to the cognitive workload of couriers; 4) objective workload can affect the cognitive workload and both workloads influence drivers' emotions (anxiety and anger); 5) the objective, cognitive workload, drivers' emotions influence the RCRL through their impacts on risky behavior but in different paths for three agglomerations. The findings highlight the importance of developing targeted countermeasures to reduce the delivery workers' workload, improve their performance on roads, and mitigate severe crash risks.

The relationship between aggressive driving and driver performance: A systematic review with meta-analysis

Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver's performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver's control performance among the general driver population in four-wheeled passenger vehicles and published with full text in English. These 34 selected studies involved 1731 participants in total. By examining the selected 34 studies, the measures relating to vehicle speed (e.g., mean speed, n = 22), lateral control (e.g., lane deviation, n = 17) and driving errors (e.g., violation of traffic rules, n = 12) were reported most frequently with a significant difference observed between aggressive driving and driving in the control group. The result of the meta-analysis indicates that the aggressive driving behaviour would have 1) a significantly faster speed than the behaviour in the control group with an increase of 5.32 km/h (95% confidence interval, [3.27, 7.37] km/h) based on 8 studies with 639 participants in total; 2) 2.51 times more driving errors (95% confidence interval, [1.32, 3.71] times) than the behaviour in the control group, based on 5 studies with 136 participants in total. This finding can be used to support the identification and quantification of aggressive driving behaviour, which could form the basis of an in-vehicle aggressive driving monitoring system.

Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels

Although there have been several studies conducted exploring the factors affecting injury severity in tunnel crashes, most studies have focused on identifying factors that directly influence injury severity. In particular, variables related to crash characteristics and tunnel characteristics affect the injury severity, but the inconvenient driving environment in a tunnel space, characterized by narrow space and dark lighting, can affect crash characteristics such as secondary collisions, which in turn can affect the injury severity. Moreover, studies on secondary collisions in freeway tunnels are very limited. The objective of this study was to explore factors affecting injury severity with the consideration of secondary collisions in freeway tunnel crashes. To account for complex relationships between multiple exogenous variables and endogenous variables by considering the direct and indirect relationships between them, this study used a structural equation modeling with tunnel crash data obtained from Korean freeway tunnels from 2013 to 2017. Moreover, based on high-definition closed-circuit televisions installed every 250 m to monitor incidents in Korean freeway tunnels, this study utilized unique crash characteristics such as secondary collisions. As a result, we found that tunnel characteristics indirectly affected injury severity through crash characteristics. In addition, one variable regarding crashes involving drivers younger than 40 years old was associated with decreased injury severity. By contrast, ten variables exhibited a higher likelihood of severe injuries: crashes by male drivers, crashes by trucks, crashes in March, crashes under sunny weather conditions, crashes on dry surface conditions, crashes in interior zones, crashes in wider tunnels, crashes in longer tunnels, rear-end collisions, and secondary collisions with other vehicles.

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then improve the accuracy of traffic violation prediction. Therefore, we propose a GATR (graph attention network based on road network) model to predict the spatiotemporal distribution of traffic violations, which adopts a graph attention network model combined with historical traffic violation features, external environmental features, and urban functional features. Experiments show that the GATR model can express the spatiotemporal distribution pattern of traffic violations more clearly and has higher prediction accuracy (RMSE = 1.7078) than Conv-LSTM (RMSE = 1.9180). The verification of the GATR model based on GNN Explainer shows the subgraph of the road network and the influence degree of features, which proves GATR is reasonable. GATR can provide an important reference for prevention and control of traffic violations and improve traffic safety.

Generalized trust and traffic violations: The moderating role of the individualism dimension

Traffic crashes take well over a million lives every year and are mainly caused by driver behavior and traffic violations. Drivers' attitudes and beliefs are at the root of whether traffic violations will be committed, making it important to explore what contributes to disobedience of traffic law. Generalized trust is one of the most influential factors in interpersonal behavior but has not yet been studied empirically in the context of driving behavior in general, and traffic violations in specific. Using data from about 30,000 participants from 20 European countries, this study examines the relationship between generalized trust and committing traffic violations while paying attention to differences between countries scoring high and low in individualism. A multilevel mixed-effects logistic regression analysis shows that in countries with high individualism scores, the probability to commit traffic violations increases significantly as generalized trust increases, while the association between generalized trust and traffic violations decreases as the country's individualism level decreases. The findings and their implications are discussed with suggestions for future research.

Multivariate analysis of roadway multi-fatality crashes using association rules mining and rules graph structures: A case study in China

Roadway multi-fatality crashes have always been a vital issue for traffic safety. This study aims to explore the contributory factors and interdependent characteristics of multi-fatality crashes using a novel framework combining association rules mining and rules graph structures. A case study is conducted using data from 1068 severe fatal crashes in China from 2015 to 2020, and 1452 interesting rules are generated using an association rule mining approach. Several modular rules graph structures are constructed based on graph theory to reflect the interactions and patterns between different variables. The results indicate that multi-fatality crashes are highly associated with improper operations, passenger overload, fewer lanes, mountainous terrain, and run-off-the-road crashes, representing the key variables of factors concerning driver, vehicle, road, environment, and accident, respectively. Furthermore, crashes involving different severity levels, road categories, and terrain are verified to possess unique association rules and independent crash patterns. Moreover, the proportion of severe crashes caused by a combination of human-vehicle-road-environment factors (43%) is much higher than that of normal crashes (3%). This study reveals that the hidden associations between various factors contribute to the overrepresentation and severity of multi-fatality crashes. It also demonstrates that the crash mechanisms involving multi-fatality crashes and their interactions are more complex at the system level than those for normal crashes. The proposed framework can effectively map the intrinsic link between multiple crash factors and potential risks, providing transportation agencies with helpful insights for targeted safety measures and preventive strategies.

Generation Paths of Major Road Accidents Based on Fuzzy-Set Qualitative Comparative Analysis

In the process of continuously promoting safety management, major road accidents have become a key obstacle to improving overall road safety. The analysis of the overall road accidents hides the characteristics and laws of major road accidents. To clarify the causes of major road accidents, an analysis framework of "individual-vehicle-environment-management" is presented based on a literature review. Considering the interaction of the above variables, the fuzz-set qualitative comparative analysis (fsQCA) was used to explore the generating paths of major road accidents based on 42 road accidents. The work shows that: (1) Major road accidents are caused due to the interactive coupling of "individual-vehicle-environment-management" elements. Major road accidents can occur with normal driving behaviors or sufficient response and rescue capabilities. (2) General road accidents and relatively major road accidents are more likely to occur in the presence of driving behavior errors, favorable road facilities, and sufficient response and rescue capabilities. Moreover, major road accidents are more likely to occur due to large vehicles with adverse vehicle performances. (3) There are three path modes and five condition configurations in major road accidents, namely individual-vehicle-management induced, individual-vehicle-environment induced, and vehicle induced mode. This work enriches the accident causation mode from a new configuration perspective and explains which variable combinations lead to the occurrence of major road accidents. Clarification of the differences between general accidents and major accidents will help to accurately predict and restrain the development of major road accidents.

Driving Fine and its Relationship with Dangerous Driving Behaviour Among Heavy Vehicle Drivers

Context

There is a significant difference between actual and existing statistics of traffic fines; since some invisible fines and most of the visible traffic violations cannot be recorded by traffic officers. Therefore, dealing with driving fines and road fatalities is considered an important issue in social and public management worldwide.

Aims

Explore the factors associated with unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers.

Settings and Design

The present cross-sectional study was conducted in Iran, from February 2019 to September 2020.

Methods and Material

This study used the driver behavior questionnaire (DBQ), demographic and driving characteristics, the number of fines, and structural equation modeling. Also, in this study 320 professional drivers participated.

Statistical Analysis Used

This article used structural equation modeling for Statistical analysis.

Results

The results of structural equation modeling analysis indicated that the data fit well with the theoretical model proposed in this study. The number of fines was directly predicted by both demographic and driving characteristics and risky driving behaviors. A significant relationship was observed between, driving hours, driving experience, and smoking, respectively, with a mistake, slip, and risky violation. There was a negative correlation between education and all four sub-scales of risky driving behaviors.

Conclusions

In order to reduce traffic fines, training courses on increasing attention and precision in drivers' observations and judgments are useful. The courses can decrease traffic violations by trying to change beliefs, attitudes, and social norms. It is therefore helpful to understand the ways to change the drivers' attitudes.

Traffic costs of air pollution: the effect of PM2.5 on traffic violation

Although emerging studies have investigated the effect of air pollution on traffic crashes, it is unclear to scholars whether air pollution affects another road safety problem-traffic violations. To address this gap, the current paper constructs a data set from 1,390,221 traffic violation records of 640,971 drivers from the Wuhan Traffic Management Bureau between January 2018 and December 2018. An ordered logistic regression was conducted to verify our hypotheses. The result shows that PM_2.5 has no overall impact on the severity of traffic violations, but each 1% increase in the daily concentration of PM_2.5 leads to a 1.02-fold increase in the odds of serious inexperience-related violations and a 0.99-fold decrease in the odds of serious overconfidence-related violations. This effect is the strongest in PM_2.5, followed by NO₂, and has not been observed in CO and O₃. In addition, robustness tests indicate that the relationship between air pollution and traffic violations is consistent among the different subsets (e.g., clear weather, no rain and snow, and good visibility). We also provide valuable practical advice for drivers and traffic authorities.

A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records

In recent years, individual drivers' crash risk assessments have received much attention for identifying high-risk drivers. To this end, we propose a probabilistic assessment method of crash risks with a reproducible long-term dataset (i.e., traffic violations, license, and crash records). In developing this method, we used 7.75 million violations and crashes of 5.5 million individual drivers in Seoul, South Korea, from June 2013 to June 2017 (four years). The stochastic process of the Bayesian network (BN), whose structure is optimized by tabu-search, successfully evaluates individual drivers' crash and violation probability. In addition, the cluster analysis classifies drivers into five distinctive groups according to their estimated violation and crash probabilities. As a result, this study found that the estimated average crash rate within a cluster converges with the actual crash rate by the proposed framework without privacy issues. We also confirm that violation records and expected crash probability are strongly correlated, and there is a direct relationship between a driver's previous violations and crash record and the future at-fault crash. The proposed assessment method is valuable in developing proactive driver education programs and safety countermeasures, including adjusting the penalty system and developing user-based insurance by recognizing dangerous drivers and identifying their properties.

Risky behavior analysis for cross-border drivers: A logit model and qualitative comparative analysis of odds of fault and injury vulnerability in Guangdong, Hong Kong and Macau

INTRODUCTION

Due to globalization and the acceleration of cross-border exchanges, cross-border risk behaviors have received widespread attention. Previous research has concluded that foreign cross-border drivers engage in relatively more risk-taking behavior patterns and are likely to experience a higher crash rate or be more inclined to cause severe crashes. However, there is little evidence on the comparison of drivers who belong to the same ethnic group driving across within-country borders.

METHOD

Based on the cross-border motor-vehicle crash reports in 2006-2010 from the Road Traffic Accident Database of the China Ministry of Public Security, this paper examines the risk factors of being at fault and getting killed or seriously injured in cross-border traffic crashes and casual paths toward crash liability and injury severity for Hong Kong and Macao drivers driving in the Chinese mainland.

RESULTS

There are extremely complex factors behind drivers from Hong Kong and Macao causing at-fault crashes or sustaining fatal and serious injuries in the Chinese mainland. Factors such as gender, age, illumination, and weather conditions do not individually affect the risk of driver at-fault crashes or severe casualties in the crashes among Hong Kong and Macao drivers driving in the Chinese mainland. Nonetheless, collectively, these factors influence them along with different vehicle types, roads, and environmental factors.

CONCLUSIONS

This paper provides more theoretical findings for understanding the compound effect of multiple risk factors involving cross-border at-fault crashes or serious casualties. The conclusions of this research are valuable as representative references for cross-border risk management policies.

PRACTICAL APPLICATIONS

To reduce the effects of different factors on cross-border risky driving behaviors and/or injurious crashes, various measures should be focused on, including specialized driver training, enhancement of the roads/environment, development of effective road safety campaigns, and directives facilitating cross-border cooperation in the field of road safety.

Association between ambient temperature and ambulance dispatch: a systematic review and meta-analysis

Previous studies have quantified the associations between ambient temperature and dispatch of ambulances, but the conclusions are still controversial. Therefore, a systematic review and meta-analysis were conducted to summarize all the current evidence. A systematic review of published literature was undertaken to characterize the effect of temperature on ambulance dispatch. We completed the literature search by the end of January 5, 2022. The pooled estimates for different temperature exposures were calculated using a random effects model. Differences among temperature pooled estimates were determined using subgroup analysis. This study was registered with PROSPERO under the number CRD42021284434. This is the first meta-analysis investigating the association between temperature and ambulance dispatch. A total of 25 studies were eligible for this study. The overall increased risks of high temperature, expressed as relative risks, were 1.734 (95% CI: 1.481-2.031). Subgroup analysis found that for the study using daily mean temperature, the high temperature increased the risk of ambulance dispatch by 15.2% (RR = 1.152, 95%CI: 1.081-1.228). In the ambulance dispatch of all-cause subgroups, the RR was 1.179 (95% CI: 1.085-1.282). The results also reported a significant association between low temperature and ambulance dispatch (RR = 1.130, 95% CI: 1.052-1.213). In the subgroup, the RR for cardiovascular disease was 1.209 (95% CI: 1.033-1.414), and respiratory disease was 1.126 (95% CI: 1.012-1.253). Sensitivity analysis indicated that the results were robust, and no obvious publication bias was observed. High temperature and low temperature are important factors influencing the dispatch of ambulances. These findings help improve the understanding of temperature effect on ambulance dispatch, demonstrating the need to consider wider surveillance of acute health outcomes in different environments.

The relationship between ambient temperatures and road traffic injuries: a systematic review and meta-analysis

Traffic accidents cause considerable economic losses and injuries. Although the adverse effects of a change in ambient temperatures on human health have been widely documented, its effects on road traffic safety are still debated. This systematic review and meta-analysis was performed to synthesize available data on the association between ambient temperature and the risks of road traffic accidents (RTAs) and traffic accident injuries (TAIs). We searched 7 different databases to locate studies. The subgroup analyses were stratified by temperature type, temperature exposure, region, mean temperature, mortality, study period, statistical model, and source of injury data. This study was registered with PROSPERO under the number CRD42021264660. This is the first meta-analysis to investigate the association between ambient temperature and road traffic safety. A total of 34 high-temperature effect estimates were reported, and two additional studies reported the relationship between low temperatures and TAI risk. The meta-analysis results found a significant association between the high temperature and RTAs, and the pooled RR was 1.025 (95%CI 1.014, 1.035). The risk of TAI was also significantly associated with temperature increases. Subgroup analyses found that using daily mean temperatures, the RR value of road traffic accidents was 1.024 (95%CI 0.939, 1.116), and the RR value of road traffic injuries was 1.052 (95%CI 1.024, 1.080). Hourly temperatures significantly increased the risk of RTA, while the risk of TAI was not significantly increased by hourly temperature. The sensitivity analysis indicated that the results were stable, and no obvious publication bias was detected. The results of this systematic review and meta-analysis suggest that increases in ambient temperature are associated with an increased risk of RTAs and TAIs. These findings add to the evidence of the impact of ambient temperature on road traffic safety.

Application of nanoindentation technology in testing the mechanical properties of skull materials

Three-point bending test, compression test and tensile test can detect the mechanical properties of the whole layer of skull, but cannot detect the mechanical properties of the inner plate, the diploe and the outer plate of the skull. In this study, nanoindentation technology was applied to detect mechanical properties of micro-materials of the skull, and differences in micro-mechanical properties of the inner, diploe and outer plates of the skull and cranial suture of human carcasses at different ages were analyzed. The differences in hardness (HIT) and modulus of elasticity (E) were statistically significant among different age groups (P < 0.01). In terms of structure, the E of diploe was higher than that of other structures, while HIT had no significant statistical difference. In terms of location, both HIT and E showed that left frontal (LF) was significantly higher than coronal suture (CS). The above results were consistent with the multi-factor ANOVAs. In addition, the multi-factor ANOVAs further explained the interaction of HIT and E with age, location and structure. It was believed that the nanoindentation technique could be used to analyze laws of micromechanical properties of different structures of human cadaveric skull and cranial suture.

Relationship between Vehicle Safety Ratings and Drivers' Injury Severity in the Context of Gender Disparity

Previous studies have analyzed the relationship between vehicle safety ratings from impact tests and actual crash injury severity. Nevertheless, no study has investigated the relationship in the context of gender disparity. The main objective of this paper is to explore the validity of the 5-star ratings of the U.S. National Highway Traffic Safety Administration, which describes vehicles’ protectiveness, using actual traffic crash data by gender. Random parameter models are developed using 2015–2020 two-vehicle crash data from Maryland, United States. According to the data, over 90% of vehicles have 4–5 stars in overall, front-impact, and side-impact 5-star ratings. After controlling other factors, it is shown that woman drivers are more likely to be seriously injured in two-vehicle crashes than men drivers when using vehicles with the same 5-star safety ratings. Moreover, there is significant individual heterogeneity in the effect of vehicles with different 5-star safety ratings on driver injury severity. Using vehicles with more stars can reduce the risk of being seriously injured for most man drivers. However, the probability of woman drivers being seriously injured is reduced by approximately 5% on average by using vehicles with higher star ratings in the overall and front-impact 5-star rating, and individual heterogeneity shows a difference of nearly 50% in positive and negative effects. The overall and front-impact 5-star ratings of vehicles could not provide reasonable information as the safety performance of vehicles in traffic crashes for woman drivers. On the other hand, drivers’ residence, driving characteristics, crash types, and environmental characteristics are significantly associated with the injury severity. It is expected that the results from this study will contribute to guide a better vehicle safety design for both men and women.

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011–2021). A number of digital databases (i.e., IEEE Xplore^®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area.

An Integrated Fuzzy Analytic Hierarchy Process (AHP) Model for Studying Significant Factors Associated with Frequent Lane Changing

Frequent lane changes cause serious traffic safety concerns, which involve fatalities and serious injuries. This phenomenon is affected by several significant factors related to road safety. The detection and classification of significant factors affecting lane changing could help reduce frequent lane changing risk. The principal objective of this research is to estimate and prioritize the nominated crucial criteria and sub-criteria based on participants’ answers on a designated questionnaire survey. In doing so, this paper constructs a hierarchical lane-change model based on the concept of the analytic hierarchy process (AHP) with two levels of the most concerning attributes. Accordingly, the fuzzy analytic hierarchy process (FAHP) procedure was applied utilizing fuzzy scale to evaluate precisely the most influential factors affecting lane changing, which will decrease uncertainty in the evaluation process. Based on the final measured weights for level 1, FAHP model estimation results revealed that the most influential variable affecting lane-changing is ‘traffic characteristics’. In contrast, compared to other specified factors, ‘light conditions’ was found to be the least critical factor related to driver lane-change maneuvers. For level 2, the FAHP model results showed ‘traffic volume’ as the most critical factor influencing the lane changes operations, followed by ‘speed’. The objectivity of the model was supported by sensitivity analyses that examined a range for weights’ values and those corresponding to alternative values. Based on the evaluated results, stakeholders can determine strategic policy by considering and placing more emphasis on the highlighted risk factors associated with lane changing to improve road safety. In conclusion, the finding provides the usefulness of the fuzzy analytic hierarchy process to review lane-changing risks for road safety.

Magnitude of death and associated factors among road traffic injury victims admitted to emergency outpatient departments of public and private hospitals at Adama Town, East Shewa Zone, Ethiopia

Objectives:

Road traffic injuries, disabilities, and deaths have been a major public health problem worldwide and in Ethiopia. Globally, around 1.35 million people die every year on the roads and 20–50 million sustain nonfatal injuries as a result of road traffic crashes. This study aimed to assess the magnitude of deaths and associated factors among road traffic injury victims admitted to emergency outpatient departments of public and private hospitals at Adama town, East Shewa Zone, Ethiopia.

Methods:

Institution-based cross-sectional study was conducted among 381 road traffic injury victims admitted to hospitals in Adama town, East Shewa, Ethiopia, from 14 December 2019 to 29 February 2020. Data were collected using interviewer-administered structured questionnaires. Data were entered into EpiData version 4.6.0.2 and analyzed using SPSS version 21. Bivariable and multivariable logistic regressions were fitted to identify variables significantly associated with road traffic injury–related deaths and the results were presented with adjusted odds ratios and 95% confidence interval. Statistical significance was declared at p-value < 0.05.

Results:

The magnitude of deaths among road traffic injury victims were 12.9%. Age (25–44 years) (adjusted odds ratio = 4.24, 95% confidence interval = 1.70–10.61), rural resident (adjusted odds ratio = 2.26, 95% confidence interval = 1.11–4.55), pedestrian (adjusted odds ratio = 3.72, 95% confidence interval = 1.67–7.99), night-time injury (adjusted odds ratio = 5.29, 95% confidence interval = 2.52–11.10), injuries on weekends (adjusted odds ratio = 2.32, 95% confidence interval = 1.12–4.80), not getting first aid at injury site (adjusted odds ratio = 2.64, 95% confidence interval = 1.02–6.84), and known comorbidity conditions (adjusted odds ratio = 3.01, 95% confidence interval = 1.23–7.38) were significantly associated with road traffic injuries–related deaths.

Conclusion:

A significant proportion of road traffic injuries resulted in death. Age, place of residence, pedestrians, night-time injury, and not getting first aid were associated with road traffic injuries–related deaths. Preventive strategies that focus on young adults, rural residents, pedestrians, and people with comorbidities would minimize road traffic injuries–related deaths. Moreover, strict supervision on weekend and night-time drives, and providing accessible lifesaving first aid services would have significant importance.

Identification of Factors Affecting Road Traffic Injuries Incidence and Severity in Southern Thailand Based on Accident Investigation Reports

Thailand has the second-highest rates of road traffic mortality globally. Detailed information on the combination of human, vehicle, and environmental risks giving rise to each incident is important for addressing risk factors holistically. This paper presents the result of forensic road traffic investigation reports in Thailand and determines risk factor patterns for road traffic injuries. Detailed forensic reports were extracted for 25 serious traffic accident events. The Haddon matrix was used to analyze risk factors in three phases stratified by four agents. The 25 events analyzed involved 407 victims and 47 vehicles. A total of 65.8% of victims were injured, including 14.5% who died. The majority (66.1%) of deaths occurred at the scene. Human-error-related factors included speeding and drowsiness. Passenger risks included not using the seat belt, sitting in the cargo area and the cab of pickups. Overloaded vehicles, unsafe car modifications, no occupant safety equipment and having unfixed seats were vehicular risks. Environmental risks included fixed objects on the roadside, no traffic lights, no guard rails, no traffic signs, and road accident black spots. At present, traffic accidents cause much avoidable severe injury and death. The outcome of this paper identifies a number of preventable risk factors for traffic injury, and importantly examines them in conjunction. Road traffic safety measures need to consider how human, vehicle, and environmental risks intersect to influence injury likelihood and severity. The Haddon matrix is useful in identifying these pre- and post-accident risk factors. Furthermore, the sustainable preventions of road traffic injury need to address these risks together with active law enforcement.

Traffic accident severity analysis in Barcelona using a binary probit and CHAID tree

Traffic accidents are still wide causation for fatalities around the globe. The set of alarm for this cause of deaths is still on, since the number of fatalities is still representing an enormous issue and a challenge for most governments. In Barcelona, similar to the rest of the world, traffic accidents are threatening lives and raising the need to lessen the number of both fatalities and severities. This study is conducted to grasp the correlations between different classification factors with accident severities and fatalities. A total of 47,153 traffic accident cases that occurred between 2016 and 2019 are utilized. Then, a binary probit model and Chi-square automatic interaction detector are exploited to grasp the impact of several risk factors. The results confirmed that males and 65 years and older injured persons are more exposed to severe or fatal injuries compared to other categories. Pedestrians and drivers are found to have higher probabilities compared to passengers in being involved in severe or fatal injuries. Weekends, afternoon, night timings all have higher odds of having severe or fatal traffic accidents. The findings of this study can help road authorities in targeting these risk factors to mitigate their impact to achieve Vision Zero.

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.

Analysis and prediction of intersection traffic violations using automated enforcement system data

The automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections. The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model's prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data. The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.

The evolution of safety-adjusted transportation efficiency for the road system in China

Traffic accident is a grievous problem that costs more than one million lives worldwide every year, but remains understudied in transportation efficiency literature. This paper develops safety-adjusted transportation efficiency to account for the negative outcomes in transportation including accidents, fatalities, injuries and property loss. We model the transportation efficiency under the data envelopment analysis (DEA) framework by treating the accident-related negative outcomes as undesirable outputs. Two DEA models, based on radial and non-radial structures respectively, are proposed for panel data. We apply the methods to 31 provinces in China over a 20-year horizon 1998-2017. We find that the evolution of China's overall safety-adjusted transportation efficiency follows a U-shaped path: It deteriorated between 1998 and 2002, steadily improved from 2002 to 2012, and stabilized during 2012-2017. The majority of the provinces improved their safety-adjusted transportation efficiency from 1998 to 2017, except for one province that maintained the status quo and three provinces that experienced a decline in performance. Improvement analysis is carried out to identify gaps in accident-related factors that each province should close to attain best-practice. Further, we find strong evidence of unconditional β-convergence and σ-convergence in safety-adjusted transportation efficiency, indicating that the provinces with low initial efficiency generally grew more rapidly and the dispersion of provincial efficiency levels diminished. The main findings are substantially different from the regular transportation efficiency analysis that does not consider the accident-related undesirable factors. The safety-adjusted transportation efficiency can convey important information that the regular transportation efficiency fails to capture.

Epidemiological and clinical characteristics of road traffic crashes related thoracic traumas: analysis of 5095 hospitalized chest injury patients

BACKGROUND

Road traffic crashes related (RTCR) chest traumas remain important global public health challenge. The impact of boosting market of automobile vehicles in China during last decade on thoracic injury needs to be defined. This study aimed to review and analyze the demographic and clinical characteristics of RTCR thoracic injuries in China.

METHODS

Clinical records of patients with thoracic trauma admitted to thoracic surgery department between January 2003 and June 2020 were retrospectively retrieved and reviewed. Patients' profiles and clinical characteristics were comparatively analyzed between road traffic crashes caused injury and other injury mechanisms, and in RTCR chest trauma patients before January 2011 (2003 group), and after January 2011 (2011 group), when is considered as the beginning year of Chinese household vehicle era.

RESULTS

The study included 5095 thoracic trauma patients with mean age of 50.2 years, of whom 79.4% were male. Most of the patients (70.3%, n = 3583) had rib fractures. Associated injuries were present in 52.0% of the patients, of them 78.5% (n = 2080) were extremity fractures. Road traffic crashes accounted for 41.4% (n = 2108) of the injuries, most of them (98.0%) were related to motor vehicles. In comparison with other chest trauma mechanisms, RTCR chest injuries affected females and older males more frequently, with a higher incidence of rib fractures and sternum fractures, and higher injury severity scores (ISS) (all p < 0.05). Surgeries were required in 1495 (70.9%) patients of the RRTCR chest traumas, while the majority of non-RTCR thoracic injuries were managed conservatively or with tube thoracostomy (30.2%, n = 901). RTCR chest traumas caused longer hospital stay (13.0 ± 9.6 days vs. 11.8 ± 7.4 days, p = 0.001), higher ICU usage (30.7% vs. 19.6%, p = 0.001), higher rate of ventilator support (12.9% vs. 7.5%, p = 0.001), and higher mortalities (3.8% vs. 1.6%, p = 0.005) than that of non-RTRA chest injuries. For RTCR patients, when compared with 2003 group, 2011 group had similar patterns in terms of accident category, associated injury and treatment. However, 2011 group had more females (38.5% vs. 18.0%, p = 0.001) and older males (50.6 ± 9.7 vs. 47.9 ± 17.2, p = 0.001), with a higher ISS (18.3 ± 10.2 vs. 17.1 ± 8.9, p = 0.004), and fewer were managed with chest tubes (25.0% vs. 29.2%, p = 0.031). Clinical outcomes were not significantly different between the groups in terms of hospital length of stay, intensive care unit (ICU) usage, ICU length of stay, duration of ventilator hours and mortality. However, the 2011 group had more patients requiring ventilator support (14.4% vs. 10.6%, p = 0.011).

CONCLUSIONS

Road traffic crashes remain to be the major etiology of thoracic injuries in China, which usually affects middle-aged males, causing rib fractures with concomitant injuries frequently occurring to other organ systems. Treatments mainly include tube thoracotomy and surgical procedures. Although the clinical characteristics and outcomes of traffic accident related chest traumas are largely unchanged in spite of the rapid increasing numbers of motor vehicles, variations in the pattern of injuries by gender, age, injury severity and ventilator usage may still provide important information for targeted management.

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.

Can the effectiveness of driver education be sustained? Effects of driving breaks on novice drivers' traffic violations

Prior studies have shown that driver education can reduce traffic violations. However, few studies have examined how driving break between driver education and owning a car influences novice drivers' traffic violations. The main objective of this study is to examine the association between driving break and traffic violations. Data from 356,786 drivers with a total of 978,855 violations during their first year of driving were extracted from the Wuhan Traffic Management Bureau. Specifically, we focused on three outcome measures: time length of first traffic violation, severity of first traffic violation, and number of traffic violations in the first year of driving. The results indicated that driving break accelerated the occurrence of the first traffic violation but reduced its severity. The results also showed that driving break was significantly related to an increase in traffic violations during the first year of driving. The detrimental effects of driving break on the time length of first traffic violation and the number of traffic violations in the first year of driving were attenuated in older age groups. The inhibitory effect of driving break on serious violations was stronger in older age groups. The findings support that the effectiveness of driver education will fade over time if one does not consolidate the learned knowledge and skills through practice.

Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

In previous research, the effects of commercial vehicle proportions (CVP) on overall crash propensity have been found to be significant, but the results have been varied in terms of the effect direction. In addition, the mediating or moderating effects of roadway attributes on the CVP-vs-safety relationships, have not been investigated. In addressing this gap in the literature, this study integrates databases on crashes, traffic, and inventory for Hong Kong road segments spanning 2014-2017. The classes of commercial vehicles considered are public buses, taxi, and light-, medium- and heavy-goods vehicles. Random-parameter Tobit models were estimated using the crash rates. The results suggest that the CVP of each class show credible effects on the crash rates, for the various crash severity levels. The results also suggest that the interaction between CVP and roadway attributes is credible enough to mediate the effect of CVP on crash rates, and the magnitude and direction of such mediation varies across the vehicle classes, crash severity levels, and roadway attribute type in four ways. First, the increasing effect of taxi proportion on slight-injury crash rate is magnified at road segments with high intersection density. Second, the increasing effect of light-goods vehicle proportion on slight-injury crash rate is magnified at road segments with on-street parking. Third, the association between the medium- and heavy-goods vehicle proportion and killed/severe injury (KSI) crash rate, is moderated by the roadway width (number of traffic lanes). Finally, a higher proportion of medium- and heavy-goods vehicles generally contributes to increased KSI crash rate at road segments with high intersection density. Overall, the findings of this research are expected not only to help guide commercial vehicle enforcement strategy, licensing policy, and lane control measures, but also to review existing urban roadway designs to enhance safety.

The role of gender and temporal instability in driver-injury severities in crashes caused by speeds too fast for conditions

The effect of inappropriate speed adjustment to adverse conditions on crash-injury severities, and how this effect might vary across male and female drivers, and over time, is not well understood. To study this, single-vehicle crashes occurring in rainy weather, where speed too fast for conditions is a driver action identified as a contributing factor to the crash, were considered. The differences between the resulting crash-injury severities of male and female drivers (and how these differences change over time) is then studied utilizing three years of Florida crash data and estimating random parameters multinomial logit models of driver injury severity while considering potential heterogeneity in the means and variances of parameter estimates. Model estimation results show that there were significant differences in the driver-injury severities of male and female drivers, and that the effect of factors that determine injury severities varied significantly over time (statistically significant temporal instability). This suggests that male and female drivers generally perceive and react to rainy weather conditions in fundamentally different ways, and that their responses, as reflected by the effect that explanatory variables have on injury severity probabilities, change over time. However, there were two explanatory variables that had relatively stable effects on injury-severity probabilities over time and across genders: an indicator variable for crashes involving non-collision factors (including overturn/rollover crashes) and an indicator variable for restraint usage. Policies that target these two variables could produce long-term reductions in crash-injury severities under adverse conditions.

Driving simulators to evaluate road geometric design effects on driver behaviour: A systematic review

Several factors can influence driver behaviour, and road geometry is one of them. A better understanding of driver-roadway interaction can enhance road design to create a safer traffic system. In this context, driving simulators are powerful tools that combine convenience and effectiveness in identifying drivers' responses to different geometry factors. In this paper, a systematic review following a Prisma guideline was conducted on driving simulator studies that examined the effects of road geometry on driver behaviour to reveal the current procedures adopted in this field and their main findings. A systematic search of eleven databases was performed covering six years of research results. Inclusion of relevant studies focused on horizontal curves, a topic identified as the most cited, extended this period. The results revealed significant heterogeneity in relation to the measured variables and deficiencies when reporting the experiment, which prevented a meta-analysis of the studies' outcomes. Despite this, a discussion of the potential of driving simulators to contribute to several road safety research gaps is presented. In addition, problems of a lack of standardisation in the performance of the experiments were detected, potentially influencing the findings of the studies. However, the results also suggest that experiments that followed good experimental practices observed effects on driver performances not detected by other studies.

Association between ambient temperature and injury by intentions and mechanisms: A case-crossover design with a distributed lag nonlinear model

Although injury is a leading cause of death worldwide, the association between ambient temperature and injury has received little research attention compared to the association of temperature with mortality and morbidity from non-external causes. With current climate change and increases in weather extremes, assessing the association between temperature and injury is important for determining public health priorities. Therefore, the present study examined the association between ambient temperature and injury risk with a focus on the intentions and mechanisms of injury. Using the national emergency database, we identified a total of 703,503 injured patients who had visited emergency departments in Seoul, South Korea from 2008 to 2016. We conducted a time-stratified case-crossover study using a conditional Poisson regression model, and applied a distributed lag nonlinear model to explore possible nonlinear and delayed effects of daily mean temperature on injury risk. Injury risk was significantly associated with ambient temperature, and temperature-injury association curves markedly differed with respect to intentions and mechanisms of injury. Although unintentional injuries increased significantly at both high and low temperatures, intentional injuries - including self-harm and assault - significantly increased only at high temperatures. The mechanism-specific analyses showed that injuries caused by traffic accidents and burns significantly increased at both high and low temperatures. However, injuries caused by all other mechanisms (i.e., fall, blunt object, machinery, penetration, and poisoning) significantly increased only at high temperatures, while injury due to slipping increased at low temperatures. Our study provides evidence that ambient temperature is associated with risk of injury, and this association differs depending on the intentions and mechanisms of injury. Overall, our findings help foster a more comprehensive understanding of the association between temperature and injury that can be used to establish appropriate public health policies and targeted interventions.

Investigating influence factors of traffic violation using multinomial logit method

Deaths and injuries resulted from road traffic crashes remain a serious problem globally, and current trends suggest that this will continue to be the case in the foreseeable future mainly in developing countries. Among diverse cause of traffic safety challenges, traffic violation has been considered as one of the noticeable contributing factors. The main aim of the study is to identify and evaluate the major traffic violation with related risk factors using multinomial logit model. Traffic violation data of Luzhou were collected from Sichuan Province Public Security Department, China. The study result revealed six major traffic violations, including traffic light violation, illegal parking, wrong-way driving, speeding, and NOT wearing a seat belt. Urban roads classified with congested driving and severe weather conditions were the major risk factors. Among different vehicle types and use, those small car/automobile categories with private purpose use exhibit statistically significant association (p-value < 0.05) with the aforementioned traffic violations. Taking into consideration these risky contributing factors during the development of traffic regulations and enforcement will help to reduce traffic violations and create a smooth/healthy driving condition with improved traffic safety and will also increase the performance of driving in general.

Why the government should be blamed for road safety

The government plays an important role in road safety. However, the effectiveness of the government in the context of road traffic accidents (RTAs) is rarely measured quantitatively. This study aims to quantitatively examine the effects of government regulation on human and organizational factors. A contributing factors classification framework of RTAs is presented based on the human factors analysis and classification system, one of the most popular systems approaches. A total of 405 major RTAs was collected over a 20-year period (1997-2017) in China and analyzed through the structural equation model. The results lead to two main conclusions: the frequency of inadequate regulation, which has reached 343, is the highest frequency among all contributing factors; government regulation exhibits significant effects on organizational influences, unsafe supervision and unsafe behaviors. These findings provide a new perspective for accident prevention that can be initiated by the government in policy-making and regulatory activities.

Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection

Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions.

The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms

In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.

Driving-Related Adverse Events in the Elderly Men: A Population-Based Prospective Cohort Study

OBJECTIVE

This study estimated the incidence of driving-related adverse events and examined the association of cognitive function with the risk of future driving-related adverse events in the elderly Korean male population.

METHODS

We analyzed 1,172 male drivers aged 60 years or older in the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD). Using the data from Korean National Police Agency, we classified the participants into three groups: safe driving (drove for 2 years after baseline without a traffic accident or repeated violations), driving cessation (stopped driving), and risky driving (one or more traffic accidents or repeated violations). We estimated the incidences of driving cessation and risky driving, and examined the effect of cognitive function on their risks.

RESULTS

The incidence of driving cessation and risky driving in the Korean male drivers aged 60 years or older was 19.3 and 69.9 per 1,000 person-years respectively and increased in the late 80s. Drivers with better baseline Word List Memory Test scores showed less risky driving (OR=0.94, p=0.039).

CONCLUSION

Driving-related adverse events increased in late 80s, and better memory function was protective against these events.