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Farm tractor accidents are a frequent cause of death for people in agricultural field of work. The aim of this study was to evaluate the characteristics of the fatal farm tractor accidents, as well as the most significant types of injuries and causes of death. Research is based on epidemiological criteria of subjects, traffic participation, cause of death, distribution of injury, outliving period and toxicological details. The study included 22 (4.2%) farm tractor related fatalities out of all 525 subjects who died from sustained injuries or complications of the injuries sustained in road traffic accidents. The highest number of deaths was reported in men in 95.5%. The highest percentage died of bleeding out (41%), while in 18% of cases, complications that occurred during the outliving period were the cause of death. Analyzing injuries through organ systems, it is observed that the highest number of farm tractor drivers had a chest injury (90%), and the most common chest injury was a rib fracture (82%). When determining the outliving period, the highest number of fatal farm tractor drivers died at the scene of accidents (68.2%). Of all of the subjects, 32% were under the influence of alcohol, while 23% had blood alcohol concentration level greater than 0.5 ‰. The high mortality of farm tractor drivers can be reduced by introducing a mandatory rollover protective structures system and the use of a seat belt in farm tractors with cabs, which would reduce the possibility of the driver falling out of the farm tractor seat and additional injuries due to rollover. [ABSTRACT FROM AUTHOR]

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Copyright of Notas Económicas: Revista da Faculdade de Economia da Universidade de Coimbra is the property of Imprensa da Universidade de Coimbra and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• This work focuses on macroscopic traffic flow model calibration and validation of large freeway networks. • An intensive literature review is presented on the subject to determine the strengths and weaknesses of various technical paths, and figure out a viable roadmap for future studies. • The paper proposes a benchmarking framework concerning some of the key factors about macroscopic traffic flow model calibration and validation, which including congestion tracking, traffic flow inhomogeneity, adverse weather conditions and accidents, capacity drop, scattering, hysteresis, stop-and-go waves, and traffic heterogeneity. • The paper presents comprehensive results of model calibration and validation concerning key factors included in the benchmarking framework as stated above. • Works of the same focus were not reported before. Macroscopic traffic flow models are of paramount importance to traffic surveillance and control. Before their employments in applications, the models need to be calibrated and validated against real traffic data. The model calibration determines an optimal set of model parameters that minimizes the discrepancy between the modeling results and real traffic data. The model validation is furthermore performed to corroborate the accuracy of a calibrated model using data other than used for calibration. The model calibration aims to reflect traffic reality, while model validation focuses on the prediction of future traffic using calibrated models. This paper delivers a comprehensive review of state-of-the-art works on macroscopic model calibration and validation, proposes a benchmarking framework on traffic flow modeling, and has conducted a large number of case studies based on the framework using macroscopic traffic flow model METANET with respect to the urban expressway network in Shanghai. In comparison to previous works, quite more comprehensive results on model calibration have been presented in this paper, in consideration of congestion tracking, traffic flow inhomogeneity, capacity drop, stop-and-go waves, scattering, adverse weather conditions, and accidents. The paper has also reported many results of model validation with respect to the same field examples. The results demonstrate that METANET is able to model complex traffic flow dynamics in large-scale freeway networks with sufficient accuracy. The paper is closed with discussion on limitations and future works. [ABSTRACT FROM AUTHOR]

• We introduce an "Hourly Risk Index" to assess fatigue-related work schedule risk. • We consider the relative risk of cumulative, time-of-day, and time-on-duty effects. • We validate with a large-scale dataset from Belgian railway traffic control centers. • The Hourly Risk Index outperforms its predecessor, the Risk Index. • Error severity is significantly related to a further increase in risk. Around-the-clock staffing in safety-critical environments calls for an accurate risk assessment of work schedules. In this paper we introduce and evaluate an hourly version of the "Risk Index", an established risk model in the UK rail industry. This "Hourly Risk Index" is the first risk-based model that allows the prediction of risk at the hourly level, and is founded on the most recent scientific insights in fatigue-related accident risk. It considers the build-up of risk over consecutive shifts, recovery during days off, quick returns, and time-of-day and time-on-duty effects. Improved understanding of hourly risk is highly relevant for both ex-ante (staff rostering) and ex-post (accident and incident analysis) evaluations. We evaluated the performance of the Hourly Risk Index by analyzing a purpose-built real-world dataset from Belgium's railway traffic control centers (containing close to 8 million round-the-clock working hours in the period 2013–2018). Results not only show that the occurrence of safety-critical errors is significantly associated with higher Hourly Risk Index values, but also that error severity is significantly related to a further increase in risk. In addition, the Hourly Risk Index outperformed the original Risk Index. Our empirical results also emphasize the importance of job demands, by indicating a higher performance of the Hourly Risk Index under conditions of low or high workload. Given these outcomes, it is currently being deployed and tested at Belgian national railways. This should allow the further refinement of the Hourly Risk Index in an iterative manner. [ABSTRACT FROM AUTHOR]