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2022 ◽  
Vol 12 (2) ◽  
pp. 828
Author(s):  
Tebogo Bokaba ◽  
Wesley Doorsamy ◽  
Babu Sena Paul

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.


Author(s):  
Ethel Avrahamov-Kraft ◽  
Alon Yulevich ◽  
Yechiel Sweed

Abstract Introduction The use of electric bicycles (EBs) among children younger than 18 years of age is rapidly increasing worldwide and becoming a substantial contributor to road accidents. We analyzed patterns and severity of pediatric bicycle-related injuries, comparing children riding EBs and classic bicycles (CBs). Materials and Methods This was a retrospective study (January 2016–December 2018) of patients arriving at our medical center due to a bicycle accident. Data were collected from medical records and included demographics, injury characteristics, treatment, and outcomes. Results Of 561 children, 197 (35%) were EB riders and 364 (65%) were CB riders. Injury severity score (ISS) of EB cyclists was significantly higher than CB cyclists (mean 4.08 ± 4.67 and 3.16 ± 2.84, respectively, p = 0.012). The rate of accidents involving motorized vehicles was higher in the EB versus CB group (25.9 vs. 11.3%, p < 0.001). Head injuries were the most common type of injury in both groups; incidence was higher in CB than in EB cyclists. However, loss of consciousness was more common in the EB group (18.3 and 12.1%, respectively, p = 0.057). Lower extremity injuries were more common in EBs versus CBs (55.8 and 37.6%, respectively, p < 0.001). Orthopaedic surgical interventions were significantly higher in the EB group (49.2 vs. 33.2%, p < 0.001), and length of stay in hospital and admission to pediatric intensive care unit were more common in EB compared with CB, although not significantly. Conclusion Injury severity of EB patients was significantly higher than that of CB patients. Accidents involving motorized vehicles were more common in the EB group. Head injury associated with loss of consciousness was significantly higher in EB patients.


2022 ◽  
Vol 14 (2) ◽  
pp. 662
Author(s):  
Lorenzo Domenichini ◽  
Andrea Paliotto ◽  
Monica Meocci ◽  
Valentina Branzi

Too often the identification of critical road sites is made by “accident-based” methods that consider the occurred accidents’ number. Nevertheless, such a procedure may encounter some difficulties when an agency does not have reliable and complete crash data at the site level (e.g., accidents contributing factors not clear or approximate accident location) or when crashes are underreported. Furthermore, relying on accident data means waiting for them to occur with the related consequences (possible deaths and injuries). A non-accident-based approach has been proposed by PIARC. This approach involves the application of the Human Factors Evaluation Tool (HFET), which is based on the principles of Human Factors (HF). The HFET can be applied to road segments by on-site inspections and provides a numerical performance measure named Human Factors Scores (HFS). This paper analyses which relationship exists between the results of the standard accident-based methods and those obtainable with HFET, based on the analysis of self-explaining and ergonomic features of the infrastructure. The study carried out for this purpose considered 23 km of two-way two-lane roads in Italy. A good correspondence was obtained, meaning that high risky road segments identified by the HFS correspond to road segments already burdened by a high number of accidents. The results demonstrated that the HFET allows for identifying of road segments requiring safety improvements even if accident data are unavailable. It allows for improving a proactive NSS, avoiding waiting for accidents to occur.


2022 ◽  
Vol 5 (1) ◽  
pp. 63-88
Author(s):  
Gizem Kodak ◽  
Gökhan Kara ◽  
Murat Yıldız ◽  
Aydın Şalcı

In this study, maritime accidents that occurred in the Strait of Istanbul over a 10-year period were evaluated in terms of ship-based risk factors. The frequency analysis was performed using the R - Studio program language. In this context, the accident data obtained from the Ministry of Transport and Infrastructure Main Search and Rescue Coordination Center were matched with the ship information accessed from Türk Loydu database. Thus, ship origin risk factors to be used within the scope of the study were determined and 10 different criteria were included in the analysis. These are ship length, ship breadth, ship draught, ship age, ship DWT, turning point, turning radius, L/B ratio, B/T ratio and number of propellers. The process of creating a data set was completed by spatially filtering the data and classifying of the ship-based causes accidents. The variables were examined with frequency analysis in the perspective of the Law of Large Numbers. With the results obtained, optimum characteristics based on ship origin risk factors have been revealed for each ship type that will pass through the Strait.


Aviation ◽  
2021 ◽  
Vol 25 (4) ◽  
pp. 283-294
Author(s):  
Neelakshi Majumdar ◽  
Karen Marais ◽  
Arjun Rao

Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source of accident data, but conventional analyses of the database yield limited insights to LOC-I. We investigate the causes of 5,726 LOC-I fixed‑wing GA aircraft accidents in the United States in 1999–2008 and 2009–2017 using a state-based modeling approach. The multi-year analysis helps discern changes in causation trends over the last two decades. Our analysis highlights LOC-I causes such as pilot actions and mechanical issues that were not discernible in previous research efforts. The logic rules in the state-based approach help infer missing information from the National Transportation Safety Board (NTSB) accident reports. We inferred that 4.84% (1999–2008) and 7.46% (2009–2017) of LOC-I accidents involved a preflight hazardous aircraft condition. We also inferred that 20.11% (1999–2008) and 19.59% (2009–2017) of LOC-I accidents happened because the aircraft hit an object or terrain. By removing redundant coding and identifying when codes are missing, the state-based approach potentially provides a more consistent way of coding accidents compared to the current coding system.


2021 ◽  
Vol 67 (4) ◽  
pp. 17-20
Author(s):  
Ana Globočnik Žunac ◽  
Predrag Brlek ◽  
Ivan Cvitković ◽  
Goran Kaniški

Safety analysis focuses on how traffic safety can change while mobility analysis is used to determine how people change travel behavior. The integration of mobility, safety and behavioral data related to COVID-19 can provide valuable insights to decision makers. Wide availability of mobile sensors has given us the opportunity to be able to assess changes in the performance and mobility of transport systems in, almost real time. The researchers also measured the impact of COVID-19 on human mobility using public mobile location data available from many companies such as Google and Apple, which is very useful for changing human mobility. The platforms produce aggregated metrics of daily mobility, including the purpose of travel, the mode of travel, and imputations of social demographics. Based on a comprehensive data set of people who participated in the collected accident data and mobile device data, we record the impact of COVID-19 on traffic safety. The paper systematically and statistically approaches the assessment of road safety in Croatia during the COVID-19 pandemic.


2021 ◽  
Vol 67 (4) ◽  
pp. 17-20
Author(s):  
Ana Globočnik Žunac ◽  
Predrag Brlek ◽  
Ivan Cvitković ◽  
Goran Kaniški

Safety analysis focuses on how traffic safety can change while mobility analysis is used to determine how people change travel behavior. The integration of mobility, safety and behavioral data related to COVID-19 can provide valuable insights to decision makers. Wide availability of mobile sensors has given us the opportunity to be able to assess changes in the performance and mobility of transport systems in, almost real time. The researchers also measured the impact of COVID-19 on human mobility using public mobile location data available from many companies such as Google and Apple, which is very useful for changing human mobility. The platforms produce aggregated metrics of daily mobility, including the purpose of travel, the mode of travel, and imputations of social demographics. Based on a comprehensive data set of people who participated in the collected accident data and mobile device data, we record the impact of COVID-19 on traffic safety. The paper systematically and statistically approaches the assessment of road safety in Croatia during the COVID-19 pandemic.


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