Risk Indicators and Road Accident Analysis for the Period 2012–2016

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.

Examining the Impact of Service Quality on Passengers' Intentions to Utilize Rail Transport in the Post-Pandemic Era: An Integrated Approach of SERVQUAL and Health Belief Model

The COVID-19 pandemic has significantly disrupted railway transportation in developing countries, resulting in reduced passenger demand and economic activity. As a result of the pandemic effect, there is an increased focus on health and safety among potential passengers. To address this issue, the present study aimed to investigate the fundamental factors that contribute to railway transportation service quality (SERVQUAL) and the intention to use intercity train services in Thailand using the health belief model (HBM), with 1600 passenger participants using structural equation modeling (SEM). The findings demonstrated that train operators' service standards remain vital to passengers, and the HBM had a statistically significant impact on shaping passengers' intentions to use train services after an epidemic. These results can inform rail agencies and health authorities when developing policies and strategies to prioritize both business and passenger safety on intercity trains.

Healthcare Supply Chain Reliability: The Case of Medical Air Transport

The principal task of national healthcare systems is to provide health services that are safe, accessible, high-quality and people-oriented. To ensure the continuity of healthcare, support activities related to patient transfer and logistics are necessary. Effective implementation of transport processes is a factor affecting the possibility of medical intervention, in terms of both planned and life-saving care. The reliability of the healthcare supply chain is a key factor in patient health. In our research, we have used the method of a single case study of a public regional hospital in Poland providing comprehensive medical services. The aim of the research is to identify the factors that affect the reliability of the healthcare supply chain in relation to the interhospital air transport of patients. Our qualitative research using process mapping reveals what factors affect the reliability of interhospital medical air transport. The analysis of 100 interhospital medical air transport cases has allowed us to create a general procedure related to the movement of patients between the facilities of the healthcare system in Poland. Our research shows that the key factor of reliability is the coherent and integrated cooperation of institutions involved in transport processes.

The Causes of Occupational Accidents and Injuries in Romanian Firms: An Application of the Johansen Cointegration and Granger Causality Test

Organizational risks are present in any activity, so it is important to manage them properly. The jobs are dynamic and involve a series of processes and activities. The entire human resource is exposed to several risks. If these risks are approached correctly, the organizational capacity to achieve its objectives and vision will increase considerably. This paper aims to investigate the relationships between work accidents (fatal and non-fatal) and the causes that contribute to their occurrence (causes dependent on the executor, causes dependent on the means of production, workload-dependent causes, and work-dependent causes—the work environment). The augmented Dickey–Fuller (ADF) test is employed to check the data stationarity series, while the Johansen test determines the cointegration relation of variables. The data have been collected from Romanian organizations. The vector error correction model (VECM) and Granger causality test are applied for speed of adjustment, nature, and direction of variables’ relationship. This research demonstrated that both data series are free from the unit-root problem at first difference. The lag length criterions select the third lag for model fitness, and Johansen cointegration declares that variables are cointegrated for the long term. The vector error correction model shows the speed of adjustment from the short to the long run is 83.35% and 42.60% for work and fatal accidents. The study results show that fatal accidents have a series relationship with selected cases for the short run and have a long-run relationship with the means of production. Fatal accidents are directly related to means of production. Fatal accidents are not designed by executors, workload-dependent causes, or work environments in the short run. Fatal accidents are directly related to the means of production and sudden incidents happening in the long run. Fatal accidents are considered by executors, workload-dependent causes, or work environments in the short run. In the long run, fatal accidents are directly related to the means of production and sudden incidents happening.

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.

Methodologies for Determining the Service Quality of the Intercity Rail Service Based on Users’ Perceptions and Expectations in Thailand

There is a significant need to change people’s travel mode from personal cars to public rail, because rail transport is a more environmentally friendly travel mode. Over the past decade, the number of rail passengers has reduced because of service quality problems. Thus, this study aims to propose guidelines for precise service quality (SQ) improvements of intercity rail services in Thailand. Data were collected from 615 train passengers by distributing questionnaires at train stations in six provinces, covering all regions of Thailand. Cluster analysis (CA), factor analysis (FA), and importance-performance analysis (IPA) were applied in this research, which were used based on gap analysis. As a result of CA and FA, the 45 quality indicators were grouped into four factors, namely, vehicles, staff, services, and infrastructures/stations. The FA results seem more appropriate than those of CA in terms of providing factor loadings that indicate the importance of each indicator. The results of IPA show that the seven indicators that were analyzed fell into the “concentrate here” quadrant. To summarize the current policy, the factor most in need of rapid improvement in order to increase the quality of the intercity rail service in Thailand is that of the train car variables group; on the other hand, the main strength of the current services relates to the services provided by staff.

Measuring the Implications of Sustainable Manufacturing in the Context of Industry 4.0

Sustainability is increasingly being addressed globally. The manufacturing industry faces various constraints and opportunities related to sustainable development. Currently, there are few methodological frameworks for evaluating sustainable organizational development. Assessing and improving organizational capacity is important for producers and researchers in the field and local, national, and international authorities. This research proposes a hierarchical framework for sustainability assessment of manufacturing industry in Romania. The proposed framework integrates performance elements and measures to improve all the processes and activities from the triple perspective of sustainability. Sustainability assessment captures the entire supply chain of the organization, including stakeholder interests and end-of-life directions for products. To establish the elements to be integrated in the development of the proposed framework, market research (online questionnaire-for the characterization of Industry 4.0) and the Delphi method were used to identify the categories of performance indicators that must be measured to identify organizational capacity for sustainable development. The framework was tested by an automotive manufacturing organization. A number of improvements have been identified that relate to Industry 4.0 facilities and the application of the facilities related to recovering the value of the product at the end of its life cycle. This hierarchical framework can be customized in detail for the specific of each organization and can be adapted in other industries, including banking, retail, and other services. It can be observed that waste management and the interests of the stakeholders are major implications that must be measured and properly motivated.

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

Empirical study of gap and correlation between philosophies Safety-I and Safety-II: A case of Beijing taxi service system

In contrast to the conventional safety philosophy (Safety-I) which focuses on 'what goes wrong', a newborn one (Safety-II) focusing on 'what goes right' endows people with more opportunities to realize productive safety in complex socio-technical systems. Yet, it is challenging to make the best of both the philosophies in a period of knowledge transition when they may have to coexist. This work investigates how Safety-II may resemble, differ from, and correlate to Safety-I. From individual, environmental and organizational aspects, 9 impacting factors are identified and expounded comparatively in the two philosophies. To examine impact of the factors on accidents and resilience respectively, an empirical approach is presented in the context of Beijing taxi service system (BTSS). Multiple means such as questionnaire surveys, semi-structured interviews, and statistical analysis with bi-method (Correlation Analysis, and Data Envelopment Analysis) cross-checking are utilized comprehensively to support the empirical study. The results show that: a) individual factors play a dominant role in system risk/performance management, in respect to views of both Safety-I and II; and b) organizational factors are more influential in creating and maintaining system resilience. Based on the findings, possible patterns of integrating the two philosophies are instantiated through mutually complementary application to BTSS. Despite the context of BTSS, this work provides a feasible way of comparing between Safety-I and Safety-II, for beneficial reference of other socio-technical systems.

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

Minimizing the Risks of Highway Transport of Hazardous Materials

Many key industrial and scientific processes, such as the generation of nuclear energy, are of enormous social benefit as energy demand and consumption grow over time. However, a drawback of several such processes is the production of hazardous waste materials, which often requires transportation along highway networks to treatment or disposal facilities. This waste can represent a safety hazard to civilians located along the transportation route. Most prior literature in this domain considers risk within only a single facet, and thus several important risk factors may not be considered. In our paper, we propose a multi-objective program to allow for the analysis and selection of minimally risky routes for hazardous materials transportation. The model assesses risk factors including the length of the selected route, the total population in areas surrounding the selected route, and the likelihood of an accident occurring along the selected route. Our paper uniquely uses geographic information systems (GIS) technology to model this optimization problem. This approach allows us to model risk along multiple dimensions simultaneously. We collect empirical data to test the model and present a case study for risk mitigation using a study area located in California. We show that our multi-objective approach is effective in presenting the decision-maker with a portfolio of solutions that perform well via each factor.

Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

Crashes that occur on curved roadways are often more severe than straight road accidents. Previously, most studies focused on the associations between curved sections and roadway geometric characteristics. In this study, significant factors such as driver behavior, roadway features, vehicle factors, and environmental characteristics are identified and involved in analyzing traffic accident severity. Bayesian network analysis was conducted to deal with data, to explore the associations between variables, and to make predictions using these relationships. The results indicated that factors including point of impact, site of location, accident side of road, alcohol/drugs condition, etc., are relatively critical in crashes on horizontal curves. Accident severity increases when crashes occur on bridges. The sensitivity of accident severity to vehicle use, traffic control, point of impact, and alcohol/drugs condition is relatively high. Moreover, a combination of negative factors will aggravate accident severities. The results also proposed some suggestions regarding the design of vehicles, as well as the construction and improvement of curved roadways.

Occupational Accidents Assessment by Field of Activity and Investigation Model for Prevention and Control

The rate of occupational accidents is increasing, leading to a number of organizational deficiencies. For European Union (EU), the non-fatal accident number in 2017 was 3,315,101. An increase in the number of accidents is recorded in many of the member states. In addition, the increase in accidents tends to focus on certain sectors and is due more to the increase in the incidence rate than the increase in the workforce. Companies in these industry sectors have also implemented less intensive prevention practices than firms in other sectors. Performing a statistical evaluation of non-fatal and fatal accidents is an important one. This assessment helps managers understand the importance of implementing prevention and control methods across organizations. For this research, we used series of data obtained from the Romanian National Institute of Statistics (NIS), Labor Inspection in Romania, and Eurostatof the European Commission. Data series evaluations were conducted for the EU and Romania. A qualitative assessment of the industry data series had been carried out. Furthermore, T-tests and analysis of variance analysis (ANOVA) were performed to identify the relationships between the frequency index of fatal and non-fatal accidents, and the categories chosen. The values obtained for men were significantly higher than those of female workers. Based on the results of the qualitative assessment and European and national strategies, an experimental model for the prevention and control of occupational accidents is proposed. At the end of the paper, the situation of labor accidents in Romania and Bulgaria, two EU member states, is assessed in the agriculture, forestry and fishing, manufacturing, construction and transport, and storage sectors. A series of trends are presented for the period 2018–2020. The results obtained from the evaluation of the data series represent an important core of the Romanian Labor Inspectorate for the development of strategic actions.

Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang.