The role of traditional analysis methods in improving complex socio-technical system safety has reached a ceiling, and thus systems theory has been utilised to support the investigations and ...countermeasures for road traffic accidents. As two widely applied systems accident analysis models, STAMP (systems theoretic accident model and process) and HFACS (human factors analysis and classification system) have their own advantages in accident analysis and safety improvement. Therefore, this study develops a new hybrid systems method integrating STAMP and HFACS for road traffic accident (SH-RTA), which can adopt HFACS to enhance the identification and analysis ability of STAMP for human factors and employ control concepts and elements of STAMP to cement the characteristic of HFACS. To illustrate the applicability of the hybrid method, a case study of '9·22' major road traffic accident in China is thoroughly analysed. Finally, preventive countermeasures and suggestions are presented.
Practitioner Summary: This paper proposes a new hybrid systems method integrating STAMP and HFACS for road traffic accident. The new method reveals dysfunctional interactions within the parallel level and across levels, and identifies additional human and organisational factors. The recommendations for preventing road traffic accident are provided from higher levels of system.
Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount ...of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
Abstract
Death from acute hemorrhage is a major problem in military conflicts, traffic accidents, and surgical procedures, et al. Achieving rapid effective hemostasis for pre-hospital care is ...essential to save lives in massive bleeding. An ideal hemostasis material should have those features such as safe, efficient, convenient, economical, which remains challenging and most of them cannot be achieved at the same time. In this work, we report a rapid effective nanoclay-based hemostatic membranes with nanoclay particles incorporate into polyvinylpyrrolidone (PVP) electrospun fibers. The nanoclay electrospun membrane (NEM) with 60 wt% kaolinite (KEM1.5) shows better and faster hemostatic performance in vitro and in vivo with good biocompatibility compared with most other NEMs and clay-based hemostats, benefiting from its enriched hemostatic functional sites, robust fluffy framework, and hydrophilic surface. The robust hemostatic bandages based on nanoclay electrospun membrane is an effective candidate hemostat in practical application.
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.
Graphical abstract
Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are ...proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.
Abnormal traffic incidents such as traffic accidents have become a significant health and development threat with the rapid urbanization of many countries. Thus it is critically important to ...accurately forecast the traffic accident risks of different areas in a city, which has attracted increasing research interest in the research area of urban computing. The challenges of accurate traffic risk forecasting are three-fold. First, traffic accident data in some areas of a city is sparse, especially for a fine-grained prediction, which may cause the zero inflation problem during model training. Second, the spatio-temporal correlations of the traffic accidents occurring in different areas are rather complex and non-linear, which is difficult to capture by existing shallow models like regression. Third, the occurrence of traffic accidents can be significantly affected by various context features including weather, POI and road network features. It is non-trivial to capture the complex associations between the diverse context features and traffic accident risks for building an accurate prediction model. To address the above challenges, this paper proposes a Multi-View Multi-Task Spatio-Temporal Networks (MVMT-STN) model to forecast fine- and coarse-grained traffic accident risks of a city simultaneously. Specifically, to address the data sparsity issue in a fine-grained prediction, we adopt a multi-task learning framework to jointly forecast both fine- and coarse-grained traffic accident risks by considering their spatial associations. For each granularity prediction, we design the channel-wise CNN and multi-view GCN to capture the local geographic dependency and global semantic dependency, respectively. In order to obtain the diverse impacts of the context features on traffic accidents, we also introduce a fusion learning module that integrates the channel-wise and multi-view features learned from different types of the external factors. We conduct extensive experiments over two large real traffic accident datasets. The results show that MVMT-STN improves the performance of traffic accident risk prediction in both fine- and coarse-grained prediction by a large margin compared with existing state-of-the-art methods.
•In this systematic review and meta-analysis, we extracted pooled odds ratio to assess the strength of relation between drowsy driving and traffic injury.•Based on the results, drowsy driving can ...increase the chance of road crashes occurrence by 1.29 to 1.34 times higher than driving without sleepiness.
To assess whether drowsy driving can increase road traffic accident related deaths and injuries.
Systematic review and meta-analysis.
Cochrane Injuries Group Specialized Register, Cochrane Central Register of Controlled Trials, EMBASE, Medline, National Technical Information service, Psychlit, International Road Research Documentation, Transport Research Information Service, and web sites related to the road safety organization were searched; experts were contacted, conference proceedings were hand searched, and relevant reference lists were checked.
We sought to identify all epidemiological studies, published in English language, which assessed the association between fatigued or sleepy driving and the occurrence of car crashes lead to death or injury.
We conducted a systematic literature review with meta-analysis using PubMed, Google scholar and other valid databases to search for articles published from January 1980 through September 2016 to identify precise effect of drowsy driving on road traffic accidents. For each study odds ratio was calculated, the ratio of event odds in the drivers with drowsy driving divided by the drivers without drowsy driving, which were pooled to obtain an overall estimate using a fixed and random effects models.
Fourteen articles satisfying inclusion criteria were identified that all of them were included in quantitative synthesis. Pooled odds ratio obtained by fixed and random effect models was 1.29 (95% CI 1.24 to 1.34) and 1.34 (95% CI 1.25 to 1.43), respectively.
Our findings that obtained from meta-analysis (with high level of evidence) suggest a significant association between crash involvement and drowsy driving. It seems that establishment of strategies to reduce any risk factors of road traffic accident such as drowsy driving can be effective in decreasing traffic crashes.
This research aims to estimate the relative risks of responsibility for a fatal accident linked to driving under the influence of cannabis or alcohol, the prevalence of these influences among drivers ...and the corresponding attributable risk ratios. A secondary goal is to estimate the same items for three other groups of illicit drugs (amphetamines, cocaine and opiates), and to compare the results to a similar study carried out in France between 2001 and 2003.
Police procedures for fatal accidents in Metropolitan France during 2011 were analyzed and 300 characteristics encoded to provide a database of 4,059 drivers. Information on alcohol and four groups of illicit drugs derived from tests for positivity and potential confirmation through blood analysis. The study compares drivers responsible for causing the accident, that is to say having directly contributed to its occurrence, to drivers involved in an accident for which they were not responsible, and who can be assimilated to drivers in general.
The proportion of persons driving under the influence of alcohol is estimated at 2.1% (95% CI: 1.4-2.8) and under the influence of cannabis at 3.4% (2.9%-3.9%). Drivers under the influence of alcohol are 17.8 times (12.1-26.1) more likely to be responsible for a fatal accident, and the proportion of fatal accidents which would be prevented if no drivers ever exceeded the legal limit for alcohol is estimated at 27.7% (26.0%-29.4%). Drivers under the influence of cannabis multiply their risk of being responsible for causing a fatal accident by 1.65 (1.16-2.34), and the proportion of fatal accidents which would be prevented if no drivers ever drove under the influence of cannabis is estimated at 4.2% (3.7%-4.8%). An increased risk linked to opiate use has also been found to be significant, but with low prevalence, requiring caution in interpreting this finding. Other groups of narcotics have even lower prevalence, and the associated extra risks cannot be assessed.
Almost a decade separates the present study from a similar one previously conducted in France, and there have been numerous developments in the intervening years. Even so, the prevalence of drivers responsible for causing fatal accidents under the influence of alcohol or narcotics has stayed remarkably stable, as have the proportion of fatal accidents which could in theory be prevented if no drivers ever exceeded the legal limits. The overall number of deaths from traffic accidents has dropped sharply during this period, and the number of victims attributable to alcohol and/or cannabis declined proportionally. Alcohol remains the main problem in France. It is just as important to note that one in two drivers considered to be under the influence of cannabis was also under the influence of alcohol. With risks cumulating between the two, it is particularly important to point out the danger of consuming them together.
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