Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials

IntroductionThe effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece.MethodRandom Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively.ResultsRegarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity.ConclusionsThe study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms.Practical applicationThe identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials.     

Applicability of accident analysis methods to Chinese construction accidents

IntroductionIt is necessary to clearly understand construction accidents for preventing a rise in Chinese construction accidents and deaths. Better analysis methods are required for Chinese construction sector accidents.MethodsChoosing and analyzing a typical construction accident based on four popular contemporary accident causation models: STAMP, AcciMap, HFACS, and the 2-4 Model. Then we evaluated the models' applicability to construction accidents, including their usability, reliability, and validity.ResultsSTAMP addressed how complexity within the accident system influenced the accident development, and its output makes the responsibilities clearer for the accident. AcciMap described the entire system's failure, the entire accident's trajectory, and the relationship between them. AcciMap showed that the accident was a dynamic developing process, and this method has a high usability. The taxonomic nature of HFACS is an important feature that provides it with a high reliability. In the accident reviewed here, we found that poor management was a critical factor rather than the individual factor in the accident. The 2-4 Model provided detailed causes of the accident and established the relationship among the accident causes, the safety management system, and the safety culture. It also avoided capturing all of the complexity in the large sociotechnical system and revealed a dynamic analysis and developing process. We confirmed that it has a high usability and validity. Therefore, the 2-4Model is recommended for future Chinese construction accident analysis efforts.Practical ApplicationsThe study provides a useful, reliable, and effective analysis method for Chinese construction accidents.     

Risk of repeat accidents by economic activity in Italy

RationaleThe phenomenon of repeat work-related accidents has not been sufficiently studied.ObjectivesTo evaluate the role of work-related factors in the occurrence of repeat accidents in Italy, using economic activity and the size of the enterprise as proxies and basing the analysis on available administrative data.MethodsThe data, provided by the Italian Workers’ Compensation Authority (INAIL), refer to accidents in the industry and artisan sectors and in the service industry between 1994 and 1999. We selected the data on accidents among 633,735 persons 25–55 years of age with a first accident in 1996–1997, and we determined the occurrence of further accidents within two years in the same economic activity, taking into account job mobility and factors associated with underreporting (geographic area, age, and size of the enterprise). The gender-specific relative risk was computed for each economic activity, given by the ratio between the risk of repeat accidents in a given economic activity and the risk in all of the other activities.ResultsIndividuals with a single accident differed from those with a repeat accident in terms of the distribution by gender, age-class, and severity of injury. The economic activities with the highest relative risk were generally those known to represent a risk of accidents in general: metal ore and coal mining, shipbuilding, the manufacturing of railroad equipment, building construction, road and railway construction, the primary iron and steel industry, foundry work, logging/wood manufacturing, slaughtering, and agriculture. However, certain activities not generally considered as hazardous showed a high risk of repeat accidents (e.g., public hygiene, and the manufacturing of bricks, pottery, and glass).ConclusionsThe epidemiological pattern of repeat accidents by economic activity reflects that of accidents in general, though with exceptions. Results suggest that factors associated with specific technical aspects and production processes are important determinants of safety. The differences between single accidents and repeat accidents by gender, age class, and severity of injury suggest that studying repeat accidents separately from first accidents is quite useful.     

Understanding factors associated with misclassification of fatigue-related accidents in police record

IntroductionFatigue is one of the riskiest causes of traffic accidents threatening road safety. Due to lack of proper criteria, the identification of fatigue-related accidents by police officers largely depends on inferential evidence and their own experience. As a result, many fatigue-related accidents are misclassified and the harmfulness of fatigue on road safety is misestimated.MethodIn this paper, a joint model framework is introduced to analyze factors contributing to misclassification of a fatigue-related accident in police reports. Association rule data mining technique is employed to identify the potential interactions of factors, and logistic regression models are applied to analyze factors that hinder police officers' identification of fatigue-related accidents. Using the fatigue-related crash records from Guangdong Province during 2005–2014, factors contributing to the false positive and false negative detection of the fatigue-related accident have been identified and compared.ResultsSome variables and interactions were identified to have significant impacts on fatigue-related accident detection.ConclusionsBased on the results, it can be inferred that the stereotype of certain groups of drivers, crash types, and roadway conditions affects police officers' judgment on fatigue-related accidents.Practical applicationsThis finding can provide useful information for training police officers and build better criteria for fatigue identification.     

Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model

ProblemThe severity of motorway accidents that occurred on the hard shoulder (HS) is higher than for the main carriageway (MC). This paper compares and contrasts the most important factors affecting the severity of HS and MC accidents on motorways in England.MethodUsing police reported accident data, the accidents that occurred on motorways in England are grouped into two categories (i.e., HS and MC) according to the location. A generalized ordered logistic regression model is then applied to identify the factors affecting the severity of HS and MC accidents on motorways. The factors examined include accident and vehicle characteristics, traffic and environment conditions, as well as other behavioral factors.ResultsResults suggest that the factors positively affecting the severity include: number of vehicles involved in the accident, peak-hour traffic time, and low visibility. Differences between HS and MC accidents are identified, with the most important being the involvement of heavy goods vehicles (HGVs) and driver fatigue, which are found to be more crucial in increasing the severity of HS accidents.Practical applicationsMeasures to increase awareness of HGV drivers regarding the risk of fatigue when driving on motorways, and especially the nearside lane, should be taken by the stakeholders.     

Worker safety and injury severity analysis of earthmoving equipment accidents

Introduction: Research on construction worker safety associated with construction equipment has mostly focused on accident type rather than injury severity and the embedded factor relationships. Significant variables and their effects on the degree of injury are examined for earthmoving equipment using data from OSHA. Four types of equipment, backhoe, bulldozer, excavator, and scraper are included in the study. Accidents involving on-foot workers and equipment operators are investigated collectively, as well as separately. Methods: Cross tabulation analysis was conducted to establish the associations between selected categorical variables, using degree of injury as a dichotomous dependent variable (fatal vs. nonfatal) and a number of independent variables having different values. Odds ratios were calculated to determine how much a certain variable/factor increases the odds of fatality in an accident, and the odds ratios were ranked to determine the relative impact of a given factor. Results: It was found that twelve variables were significantly associated with injury severity. Rankings based on odds ratios showed that inadequate safety training (2.54), missing equipment protective system (2.38), being a non-union worker (2.26), being an equipment operator (1.93), and being on or around inadequately maintained equipment (1.58) produced higher odds for fatality. Conclusion: A majority of the earthmoving equipment accidents resulted in fatality. Backhoes were the most common equipment involved in accidents and fatalities. Struck-by accidents were the most prevalent and most fatal. Non-OSHA compliant safety training, missing seatbelt, operator not using seatbelt, malfunctioning back-up alarms, and poorly maintained equipment were factors contributing to accidents and fatalities. On-foot workers experienced a higher number of accidents than operators, while fatality odds were higher for the operators. Practical applications: Safety professionals should benefit from our findings in planning and delivering training and providing oversight to workers in earthmoving equipment operations.     

The impact of the economic crisis on occupational injuries

IntroductionThe potential influence of the current economic crisis on occupational accident rates and accident severity is studied in an analysis of all workplace accidents that occurred in Spain throughout the period 2000–2009.Method and resultsThe investigation confirms that occupational accidents in Spain are affected by the current economic crisis, which has provoked a sharp fall in both the number of accidents and the probability of having one. This may be justified by certain factors such as age, gender, length of service, size of the firm, and the employment stability of the injured worker. The influence of these factors is analyzed.Practical applicationsThe economic crises seems to provoke a sort of “natural selection” in the labor market and only the best adapted tend to remain (older workers, with more experience, a higher percentage of women, more workers in larger companies and permanent contracts), all of which means that the probability of workers having an injury is considerably reduced.     

The development and evaluation of accident prevention routines: A case study

Information resulting from the occurrence of accidents and near-accidents is the basis for most accident prevention efforts. However, such information may not normally be incorporated into local safety activities. A group to assist supervisors in their investigations of accidents and a procedure to prompt the reporting of near-accidents by employees were tested at one company as means of improving local safety activities. The accident investigation group was in operation in all departments and was associated with improved accident reporting and prevention activities as well as a reduction in accident severity. The near-accident reporting procedure was tested in one department and led to improved knowledge about risks, although no reductions in accident frequency and severity were shown. On the basis of these results, the company safety committee decided at the end of the first year to extend the mandate of the investigation group but not to continue with near-accident reporting. Follow-ups after 2 and 3 years indicated that improvements in prevention activities had been maintained and that accident severity had been further reduced.     

Commercial truck crash injury severity analysis using gradient boosting data mining model

IntroductionTruck crashes contribute to a large number of injuries and fatalities. This study seeks to identify the contributing factors affecting truck crash severity using 2010 to 2016 North Dakota and Colorado crash data provided by the Federal Motor Carrier Safety Administration.MethodTo fulfill a gap of previous studies, broad considerations of company and driver characteristics, such as company size and driver’s license class, along with vehicle types and crash characteristics are researched. Gradient boosting, a data mining technique, is applied to comprehensively analyze the relationship between crash severities and a set of heterogeneous risk factors.ResultsTwenty five variables were tested and 22 of them are identified as significant variables contributing to injury severities, however, top 11 variables account for more than 80% of injury forecasting. The relative variable importance analysis is conducted and furthermore marginal effects of all contributing factors are also illustrated in this research. Several factors such as trucking company attributes (e.g., company size), safety inspection values, trucking company commerce status (e.g., interstate or intrastate), time of day, driver’s age, first harmful events, and registration condition are found to be significantly associated with crash injury severity. Even though most of the identified contributing factors are significant for all four levels of crash severity, their relative importance and marginal effect are all different.ConclusionsFor the first time, trucking company and driver characteristics are proved to have significant impact on truck crash injury severity. Some of the results in this study reinforce previous studies’ conclusions.Practical applicationsFindings in this study can be helpful for transportation agencies to reduce injury severity, and develop efficient strategies to improve safety.     

Off-road truck-related accidents in U.S. mines

IntroductionOff-road trucks are one of the major sources of equipment-related accidents in the U.S. mining industries. A systematic analysis of all off-road truck-related accidents, injuries, and illnesses, which are reported and published by the Mine Safety and Health Administration (MSHA), is expected to provide practical insights for identifying the accident patterns and trends in the available raw database. Therefore, appropriate safety management measures can be administered and implemented based on these accident patterns/trends.MethodsA hybrid clustering-classification methodology using K-means clustering and gene expression programming (GEP) is proposed for the analysis of severe and non-severe off-road truck-related injuries at U.S. mines. Using the GEP sub-model, a small subset of the 36 recorded attributes was found to be correlated to the severity level.ResultsGiven the set of specified attributes, the clustering sub-model was able to cluster the accident records into 5 distinct groups. For instance, the first cluster contained accidents related to minerals processing mills and coal preparation plants (91%). More than two-thirds of the victims in this cluster had less than 5 years of job experience. This cluster was associated with the highest percentage of severe injuries (22 severe accidents, 3.4%). Almost 50% of all accidents in this cluster occurred at stone operations. Similarly, the other four clusters were characterized to highlight important patterns that can be used to determine areas of focus for safety initiatives.ConclusionsThe identified clusters of accidents may play a vital role in the prevention of severe injuries in mining. Further research into the cluster attributes and identified patterns will be necessary to determine how these factors can be mitigated to reduce the risk of severe injuries.Practical applicationAnalyzing injury data using data mining techniques provides some insight into attributes that are associated with high accuracies for predicting injury severity.     

Safety in high-risk helicopter operations: The role of additional crew in accident prevention

ObjectivesTo examine the causes associated with high risk helicopter operations, in particular external load and aerial application flight, and determine the role of crew management in accident mitigation strategies.MethodsData from the US National Transportation Safety Board online database were analyzed. Flight phases and types of rotor strike were added categories extracted from the narrative statements included in the accident reports.Results and recommendationsAerial application and external load accidents are characterized by high numbers of rollovers, rotor strikes and errors during preflight checks. Additional flight or ground crew may assist the pilot in maintaining a visual lookout. The number of accidents can be further reduced with adequate fuel management in which both pilot and ground crew can contribute. Although the ground crew can be both victim and cause of an accident, their supporting role has much to contribute to safety in high risk helicopter operations.     

Working conditions,psychological/physical symptoms and occupational accidents. Bayesian network models

Each day thousands of workers suffer occupational accidents of varying degrees of severity. Accidents at work render workers incapable of carrying out their day to day activities, either temporarily or permanently, and they also have detrimental effects on family life, the company, and the general public. In order to reduce the occupational accident rate, it is necessary to determine the causes of those accidents. Although there are many different types of accidents, they generally stem from poor working conditions. The purpose of this study was to analyze the influence of working conditions on occupational accidents from data gathered in the VI National Survey of Working Conditions (VI NSWCs) in 2007. This survey utilized a random sample of the active population of Spain. The sample comprised 11,054 people (5917 males and 5137 females). In order to carry out the study, a probabilistic model was built using Bayesian networks. The model included the following variables: hygiene conditions, ergonomic conditions, job demands, physical symptoms, psychological symptoms, and occupational accidents. The study demonstrated that there were strong relationships between hygiene conditions and occupational accidents; it has been shown that poor hygienic conditions duplicate the probability of accident. Physical symptoms increased almost 50% due to poor ergonomic conditions. And finally, high job demands almost duplicated the psychological symptoms. The investigation also showed a high degree of interdependence between physical and psychological symptomatologies and the relationship between these and occupational accidents.     

Acute injuries resulting from accidents involving powered mobility devices (PMDs)—Development and outcomes of PMD-related accidents in Sweden

Objective: Powered mobility devices (PMDs) are commonly used as aids for older people and people with disabilities, subgroups of vulnarable road users (VRUs) who are rarely noted in traffic safety contexts. However, the problem of accidents involving PMD drivers has been reported in many countries where these vehicles have become increasingly popular.

The aim of this study is to extract and analyze national PMD-related accident and injury data reported to the Swedish Traffic Accident Data Acquisition (STRADA) database. The results will provide valuable insight into the risks and obstacles that PMD drivers are exposed to in the traffic environment and may contribute to improving the mobility of this group in the long term.

Methods: The current study is based on data from 743 accidents and 998 persons. An analysis was performed on a subset of data (N?=?301) in order to investigate the development of accidents over a period of 10 years. Thereafter, each accident in the whole data set was registered as either single (N?=?427) or collision (N?=?315).

Results: The results show that there was a 3-fold increase in the number of PMD-related accidents reported to STRADA during the period 2007–2016.

With regard to single accidents, collisions, as well as fatalities, the injury statistics were dominated by males. Single accidents were more common than collisions (N?=?427 and N?=?316, respectively) and the level of injury sustained in each type of accident is on par.

The vast majority of single accidents resulted in the PMD driver impacting the ground (87%), due to either PMD turnover (71%) or the driver falling out of the PMD (16%). The reason for many of the single accidents was a difference in ground level (34%, typically a curb).

Cars, trucks, or buses were involved in 67% of collision events; these occured predominantly at junctions or intersections (70%).

Abbreviated Injury Scale (AIS) 3+ injuries were dominated by hip and head injuries in both single accidents and collision events.

Conclusions: The present study shows that further research on PMD accidents is required, with regard to both single accidents and collision events. To ensure that appropriate decisions are made, future work should follow up on injury trends and further improve the quality of PDM-related accident data. Improved vehicle stability and design, increased usage of safety equipment, proper training programs, effective maintenance services, and development of a supporting infrastructure would contribute to increased safety for PMD drivers.     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

Risk factors associated with bus accident severity in the United States: A generalized ordered logit model

IntroductionRecent years have witnessed a growing interest in improving bus safety operations worldwide. While in the United States buses are considered relatively safe, the number of bus accidents is far from being negligible, triggering the introduction of the Motor-coach Enhanced Safety Act of 2011.MethodThe current study investigates the underlying risk factors of bus accident severity in the United States by estimating a generalized ordered logit model. Data for the analysis are retrieved from the General Estimates System (GES) database for the years 2005–2009.ResultsResults show that accident severity increases: (i) for young bus drivers under the age of 25; (ii) for drivers beyond the age of 55, and most prominently for drivers over 65 years old; (iii) for female drivers; (iv) for very high (over 65 mph) and very low (under 20 mph) speed limits; (v) at intersections; (vi) because of inattentive and risky driving.     

Fatal injuries among Hispanic workers in the U.S. construction industry: Findings from FACE investigation reports

IntroductionIdentifying and understanding the characteristics of workplace accidents can provide vital information required to clarify their causes and prevent similar accidents from happening in the future. The Hispanic workforce represents a significant segment of the U.S. construction labor force that is projected to continue increasing in population. The government statistical data show higher rates of fatalities among Hispanic workers when compared to other ethnic groups.MethodThis study aims to provide details about the trends of fatal injuries among Hispanic workers. The study examined 92 government investigation reports to reveal the general trends, then an examination of fatal fall injuries within the study sample was conducted since falling is the predominant cause of fatal injuries.ResultsThe findings suggest differences in accident characteristics between Hispanic workers and all workers, which could indicate a need for different interventions to improve the overall site safety. The study also revealed the dire need to propose revised investigation procedures that would help identify the root causes of accidents, which in turn leads to better recommendations and interventions.     

石化企业安全文化建设

随着国企改制的不断深化,安全管理显现出许多薄弱环节,尤其是在能源勘探开发过程中发生的重特大事故,不断给安全生产发出了黄牌警告.近年来,多种因素导致员工"三违",造成恶性事故不断,其原因是由于企业缺乏安全文化氛围和员工缺乏必要的自我安全保护意识,盲目蛮干导致事故高发.因此,加强企业安全文化建设是提高全员安全意识的一种行之有效的方法,并可以进一步促进和提高员工的自我安全保护意识和安全技术素质.本文阐述了企业安全文化建设在新形势下的重要意义,并针对石化企业开展安全文化建设提出几点建议.     

The Driver Behaviour Questionnaire as accident predictor; A methodological re-meta-analysis

IntroductionThe Manchester Driver Behaviour Questionnaire (DBQ) is the most commonly used self-report tool in traffic safety research and applied settings. It has been claimed that the violation factor of this instrument predicts accident involvement, which was supported by a previous meta-analysis. However, that analysis did not test for methodological effects, or include unpublished results.MethodThe present study re-analysed studies on prediction of accident involvement from DBQ factors, including lapses, and many unpublished effects. Tests of various types of dissemination bias and common method variance were undertaken.ResultsOutlier analysis showed that some effects were probably not reliable data, but excluding them did not change the results. For correlations between violations and crashes, tendencies for published effects to be larger than unpublished ones and for effects to decrease over time were observed, but were not significant. Also, using the mean of accidents as proxy for effect indicated that studies where effects for violations are not reported have smaller effect sizes. These differences indicate dissemination bias. Studies using self-reported accidents as dependent variables had much larger effects than those using recorded accident data. Also, zero-order correlations were larger than partial correlations controlled for exposure. Similarly, violations/accidents effects were strong only when there was also a strong correlation between accidents and exposure. Overall, the true effect is probably very close to zero (r < .07) for violations versus traffic accident involvement, depending upon which tendencies are controlled for.ConclusionsMethodological factors and dissemination bias have inflated the published effect sizes of the DBQ. Strong evidence of various artefactual effects is apparent.Practical applicationsA greater level of care should be taken if the DBQ continues to be used in traffic safety research. Also, validation of self-reports should be more comprehensive in the future, taking into account the possibility of common method variance.     

The potential of vehicle and road infrastructure interventions in fatal bicyclist accidents on Swedish roads—What can in-depth studies tell us?

Abstract

Objective: The objective of this article is to describe the characteristics of fatal crashes with bicyclists on Swedish roads in rural and urban areas and to investigate the potential of bicycle helmets and different vehicle and road infrastructure interventions to prevent them. The study has a comprehensive approach to provide road authorities and vehicle manufacturers with recommendations for future priorities.

Methods: The Swedish Transport Administration’s (STA) in-depth database of fatal crashes was used for case-by-case analysis of fatal cycling accidents (2006–2016) on rural (n?=?82) and urban (n?=?102) roads. The database consists of information from the police, medical journals, autopsy reports, accident analyses performed by STA, and witness statements. The potential of helmet use and various vehicle and road infrastructure safety interventions was determined retrospectively for each case by analyzing the chain of events leading to the fatality. The potential of vehicle safety countermeasures was analyzed based on prognoses on their implementation rates in the Swedish vehicle fleet.

Results: The most common accident scenario on rural roads was that the bicyclist was struck while cycling along the side of the road. On urban roads, the majority of accidents occurred in intersections. Most accidents involved a passenger car, but heavy trucks were also common, especially in urban areas. Most accidents occurred in daylight conditions (73%). Almost half (46%) of nonhelmeted bicyclists would have survived with a helmet. It was assessed that nearly 60% of the fatal accidents could be addressed by advanced vehicle safety technologies, especially autonomous emergency braking with the ability to detect bicyclists. With regard to interventions in the road infrastructure, separated paths for bicyclists and bicycle crossings with speed calming measures were found to have the greatest safety potential. Results indicated that 91% of fatally injured bicyclists could potentially be saved with known techniques. However, it will take a long time for such technologies to be widespread.

Conclusions: The majority of fatally injured bicyclists studied could potentially be saved with known techniques. A speedy implementation of important vehicle safety systems is recommended. A fast introduction of effective interventions in the road infrastructure is also necessary, preferably with a plan for prioritization.