Road Transport Accident Analysis from A System-Based Accident Analysis Approach Using Swiss Cheese Model

Road safety has become a major concern to both developed and developing countries due to its negative economic impacts. Although, numerous approaches of accident analysis have been conducted, there seem to be an increase in road crashes every year. The main aim of this study is to analyse a driving school accident using a system-based accident analysis approach. The data for the study was collected using an interview. A Swiss Cheese Accident Causation Model was used to identify the factors that contributed to the accident. The study identified four weaknesses in the system defences of the driving school that created a possible accident trajectory. It is concluded that adopting system-based accident analysis approach in analysing road transport accidents, could lead to a greater understanding of latent conditions and road user error, which in turn could inform the development of intervention strategies within a road transport domain as suggested by other studies. Finally, the short falls of using only person approach of accident analysis in road transport domain are also highlighted

Chronic kidney disease in type 2 diabetes mellitus patients: Comparison of KDIGO and KDOQI guidelines

Background: Chronic kidney disease (CKD), has become a public health concern as it has been reported to cause adverse outcomes such as kidney failure and premature death. This cross sectional study compared the Kidney Disease: Improving Global Outcomes (KDIGO) and Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines in assessing the prevalence of CKD in Type 2 diabetes Mellitus (T2DM) patients.Methods: We consecutively sampled a cross-section of 202 T2DM patients from the Ho municipality in the Volta region (Ghana). Structured pre-tested questionnaires were administered to obtain information on gender, age, body mass index (BMI), systolic and diastolic blood pressure, medication used, duration on medication, and duration of diabetes. Serum creatinine and urine protein were estimated using standard protocols and CKD was classified according to KDIGO and KDOQI guidelines.Results: The prevalence of CKD was 63.4% and 58.4% using the KDIGO and KDOQI guidelines respectively. The prevalence of mildly decreased renal function or worse (eGFR < 60/ml/min/1.73 m2) was 10.4% for KDIGO guideline and 7.9% for KDOQI guidelines with an excellent agreement between both definitions showing bias = -0.129, 95%CI = (-0.17 to -0.08) on Bland-Altman analysis. Participants older than 70 years were more likely to have CKD when KDIGO criteria was used (P = 0.018). The prevalence of albuminuria was 47.0% with 21.9% presenting with 1+ and 2+ grades.Conclusion: KDIGO guideline estimates higher prevalence of CKD than KDOQI guidelines in the same study population. KDIGO guideline might help in early detection and proper classification of CKD which will illicit stage-specific treatment.Keywords: Type 2 diabetes mellitus, Chronic kidney disease, Estimated glomerular filtration rate, Albuminuri

An integrated approach of multiple correspondences analysis (MCA) and fuzzy AHP method for occupational health and safety performance evaluation in the land cargo transportation

Land cargo transportation is one of the components of the logistics chain with high impact on economic and social development worldwide. However, problems such as top logistics costs, deficiencies in transportation infrastructure and the failure to adopt good operating practices in aspects such as quality, environment, and occupational safety and health affect the ability of companies to comply with the agreements, requirements, and regulations of the clients and other interested parties. One of the most relevant problems for the sector is associated with the high accident rates that make this medium less advantageous compared to other means of transport with impact on operational costs, on logistics indicators, on compliance with legal regulations and customer satisfaction. However, although there are legal standards and management standards in occupational safety and health, evaluating performance can become a difficult and subjective process, due to the complexity of the land cargo transportation and the different interest groups involved. Besides, there is little information in the literature that provides solutions for the industry. Therefore, this document presents an integrated approach between multi-criterion decision making models (MCDM) and the Multiple Correspondences Analysis (MCA) to facilitate the evaluation and improvement of occupational health and safety performance, with a logical process, objective, robust and using both qualitative and quantitative techniques, with real application in the land cargo transportation sector. First, the multivariate method of Multiple Correspondences Analysis (MCA) was used for the evaluation of a sample of companies in the industry, considering the factors and sub-factors identified in the first stage and performing correlational analyzes among the variables. Subsequently, a multicriteria decision-making model was designed to determine the factors and sub-factors that affect occupational health and safety performance through the technique of the Fuzzy Analytic Hierarchy Process (FAHP). Finally, improvement strategies are proposed based on the approaches suggested in this document

Synergy effects of perceived enjoyment and behavioural factors on high speed selection and crash involvement: Adaptive cruise control perspective

© 2019, © 2019 Taylor & Francis Group, LLC and The University of Tennessee.High speed selection and road traffic crashes are related to behavioural perceptions. These are the main cause of deaths or serious injuries. There is evidence to suggest that adaptive cruise control (ACC) increases the driver’s perception of having fun while driving, which affects behavioural factors. Thus, the study aims to examine the interaction effects of perceived enjoyment and behavioural factors towards the use of ACC on high speed and crash involvement. The study model was validated using a sample collected from 321 male and 211 female drivers of vehicles equipped with ACC on two occasions separated by 2 months interval. The results show that age and mileage were positively related to crash involvement, whereas gender was negatively related to high speed selection (HSS). The predictors of HSS were intention, Perceived behavioral control (PBC), attitude towards the use of ACC, and perception of enjoyment from the use of ACC. In addition, the positive predictors of the crash involvement were intention and attitude toward the use of ACC. Perception of enjoyment from the use of ACC negatively predicted crash involvement. Interaction relations between perceived enjoyment and intention towards the use of ACC predicted HSS. Finally, the interaction relation between enjoyment from the use of ACC and PBC predicted crash involvement

Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxis

Abstract Purpose The main congestion on roads occur during peak hours, apart from incidents such as road accidents and construction works. Although there have been studies on peak period travels, these studies have only implicitly considered weekday, weekend and road type in their investigations. In this paper, it is proposed to investigate explicitly, the effect of weekday and weekend travel variability and road type on peak hour vehicular movement which leads to congestion. A study of vehicular movement patterns during these times can influence and impact on planning decisions for transportation engineers. Methods This study utilizes structural equation model (SEM) to investigate the vehicular movements influence of weekdays, weekends, road type choice and car type on two peak hour periods 6 am to 9 am and 4 pm to 7 pm and one off-peak hour 9 am to 12 noon. Results Using vehicular movement data from Radio Frequency Identification for Nanjing, China, for the month of May 2014, it was revealed that in most of the cases, weekday travels influence peak hour travels more than weekends and that off-peak hour travels for both weekdays and weekends show little variations. The study also discovered that choice of road type and car type, have varying influence on peak hour travels. Conclusions The high significance ratios of results prove that these chosen variables are suitable for investigations into peak hour travel pattern studies. The study has also proved the viability of this modeling method to investigate policy measures to reduce peak period congestion

COVID-19 Safety Protocols: Do Commuters Prefer Public Transport after Relaxation of Safety Protocol Enforcement?

There is a current discourse on how COVID-19 will impact future use of public services by people. At the time of writing this paper, most countries around the globe had relaxed safety protocol enforcement. This may change individual use of public transport, and policy implementations. The study mainly used Multinomial Logistic Regression (MLR) to examine the use of public transport ridership after the relaxation of COVID-19 safety protocol enforcement. A survey was used to collect data from 1692 respondents across Ghana partly online and partly face-to-face interviews from April 20th, 2022 to June 5th, 2022. The preliminary findings show that the use of private cars declined during the enforcement of safety protocols. However, after relaxation of safety protocol enforcement, the use of private transport increased more than public transport. The Relative Importance Index revealed that 'facemask wearing covering both nose and mouth', 'reduction in the number of occupants per vehicle', 'the use of alcohol-based hand sanitizer', and 'vehicles cleaned after every trip' were the most important safety protocols perceived to prevent infection of the virus. However, the MLR model shows that largely, relaxation of mandatory facemask wearing, social distancing, hand hygiene, and disinfection of transport could decrease public transport ridership. These findings suggest that the COVID-19 infection anxiety had not faded and could decrease public transport ridership. To relieve the anxiety regarding virus infection through the use of public transportation, the government needs to take appropriate measures to lower the perceived risk of infection

Estimating injury severity for motorized and non-motorized vehicle-involved crashes: Insights from random-parameter ordered probit model with heterogeneity in means and variances

The use of advanced models to investigate the determinants of injury severity outcomes for motorized and non-motorized-involved crashes are sparse. Therefore, random-parameter ordered probit models with heterogeneity in means and variances were developed to estimate factors affecting injury severity for motorized and non-motorized-involved crashes. Data covering a five-year period comprising 5976 and 634 cases for motorized and non-motorized-involved crashes respectively, was retrieved from the database of the National Road Safety Authority, State Insurance Company and Driver and Vehicle Licensing Authority in Ghana and used for model estimation. The results show that factors have varying significant effects on injury severity outcomes for motorized and non-motorized models. Marginal effects indicate that old age occupants, head-on-collision, exceeding a posted speed limit of 100 km/h and crash during weekends contributed greatly to the likelihood of severe injury outcomes in motorized model. Additionally, male non-motorists, non-use of helmet, rear-end collision, right-angle collision and crash on urban roads and during weekends, contributed significantly to the severe injury outcomes of non-motorized models. The direction of effect of the factors on severe injury was observed to have varying degrees of estimated coefficients. The difference in estimated coefficients shows that crashes involving non-motorized vehicles were more likely to result in severe injury compared to motorized vehicles. The motorized model had heterogeneity in means of five (5) random parameters observed, while the non-motorized model had heterogeneity in means of four (4) random parameters observed with two variables affecting the variance of three random parameters. Based on the results, various countermeasures were proposed to enhance road traffic safety

Explorative analysis of vehicular movement patterns using RFID-based transport data : an eulerian perspective

The advancement in technology on data capture procedures has overcome many of the challenges associated with data acquisition for transportation studies. The use of Radio Frequency Identification (RFID) technology is increasingly becoming significant in transport application domains where there is the need to track and analyze patterns of vehicles movement. In this paper, we explore the efficacy of RFID technology, a eulerian perspective on movement, to extract spatial and temporal rhythms of vehicular movements in, Nanjing, China for road traffic analysis. Data mining and geo-computation methods were used to mine and extract vehicular movement. The count data, statistical, visual analytics and Geographic Information System (GIS) methods were used to determine spatial and temporal patterns of vehicular movement. Global Moran's I, hot spot analysis and kernel density estimations were the spatial statistical methods used to determine spatial patterns of vehicular movements. The study reveals the efficacy of the usage of massive RFID data, which uses a eulerian perspective of movement for determining spatiotemporal patterns for traffic analysis. The study revealed morning peak and evening peak vehicular movements, for weekdays with Thursdays and Fridays displaying the most vehicular movements. Spatial patterns revealed a clustering of low and high vehicular counts for weekdays, weekends, off-peak and peak hours. This explorative study using RFID technology to determine spatial and temporal patterns in vehicular counts has important application for traffic analysts. This study approach supports traffic congestion monitoring, traffic flow statistics and traffic planning as well as helps to determine low and high traffic locations to evaluate the performance of a traffic system

Personality, socioeconomic status, attitude, intention and risky driving behavior

Risky driving behavior (RDB) is one of the human factors contributing to the majority traffic injuries and crashes. This paper examines the influence of personality factors on RDB and the mediating role of intention and attitude in the relationship. The influence of different SES, personality, marital status, and gender characteristics on attitude, intention, and risky driving was also examined. A sample of 354 fully licensed Ghanaian drivers, including 278 males and 76 females, participated in the study at two times points separated by three months. The correlation results showed that unhealthy family relationship and negative emotions were positively associated with illegal speed-related behaviors, risky driving, and accident involvement. The results of a structural equation model suggested that personality variables significantly and positively influence intention and attitude toward speeding, with normlessness directly and positively influencing RBD. The mediation analysis shows that intention mediated the effects of personality variables on RBD. No significant mediating effect for attitude was found. The analysis of variance result shows that drivers with high socioeconomic status and personality had the high intention and attitude toward speeding and reported more RDB. Finally, drivers who had been divorced significantly reported more positive attitude toward speeding than the married and single group. Based on the results, the study concludes that, in accessing the effect of personality measures on RDB, it is important to include intention as a possible mediator for more accurate decision. Practical implications for managing different personalities and socioeconomic positions are also discussed

Traffic climate, driver behaviour, and accidents involvement in China

Traffic Climate Scale (TCS) and Positive Driver Behaviours Scale (PDBS) are new measurement tools. The study aims to translate the TCS and PDBS into Chinese and to assess their factor structures in a large sample of licensed motor vehicle drivers in China. A further aim is to investigate the effects of TCS factors on drivers' behaviours and traffic accidents involvement. Data were collected using an online survey. Participants were 887 fully licensed motor vehicle drivers, including 531 males and 356 females who completed a Chinese translated questionnaire including the TCS, PDBS, Driver Behaviour Questionnaire (DBQ), items related to drivers' driving records and demographic characteristics. The result of the exploratory factor analysis revealed clear three-factor solution ('Functionality', 'External affective demand' and 'Internal requirement') of TCS with high item loadings and acceptable internal consistency coefficients. The convergent validity of the Chinese TCS was supported by its relationship with driver behaviour factors (violations, errors, lapses and positive behaviours), the traffic accidents involvement and demographic characteristics. The results further show that the external affective demand consistently and positively relate to aberrant behaviours and negatively relate to positive behaviours with indirect positive significant effects on accidents involvement. Functionality is concurrently and negatively related to aberrant behaviours and positively related to positive behaviours with no effects on accidents involvement. The internal requirement is negatively related to aberrant behaviours but, positively related to positive behaviours with positive significant direct effects on accidents involvement