Tractor rollover fatalities, analyzing accident scenario.

INTRODUCTION: In many countries, traditional data sources for collecting injuries of workers covered by compulsory accident insurance have recently been integrated by new observatories whose results may differ. A comparative analysis of the Italian data collection systems related to fatal tractor accidents in agriculture was performed focusing on tractor rollover fatalities with the aim of analyzing the accident scenario. METHOD: Data from the Operational Archives of the Italian Workers Compensation Authority (INAIL), which collects injuries of workers covered by compulsory accident insurance and those of the National Surveillance System (INAIL_ASL), which provides narrative text reports of work-related fatal accidents have been analyzed and compared to the information collected by the INAIL Observatory. The INAL Observatory was recently set up to complement the collection of fatal accidents involving agricultural machinery. Italian data were then compared to data available at an international level. Fatal tractor accidents vary considerably with respect to fatal accidents in agriculture, being 10.6 and 43.7% for the Operational Archives and Surveillance System, respectively. National Surveillance System records, implemented with narrative texts allowed the accident scenario to be defined. RESULTS: 71.7% of fatal tractor-related accidents refer to non-ROPS equipped vehicles and of these, 26.5% involved machines originally mounted with a ROPS that had been removed or was inoperative in the folded-down position during the rollover event. Just one fatal event from a collapsed ROPS on the overturned tractor was recorded. It is interesting that 16.6% of fatal accidents involved a clear environmental factor. PRACTICAL APPLICATION: A campaign to train tractor drivers on the correct use of the combination ROPS and seatbelt can contribute to decreasing rollover events with fatal outcomes. Contemporarily a strict requirement to install ROPS and a seatbelt on tractors, combined to an official inspection at the farm level, can increase the chance of survival in a rollover accident.

Evaluation of a Commercial Tractor Safety Monitoring System Using a Reverse Engineering Procedure.

There is a high rate of work-related deaths in agriculture. In Italy, despite the obligato-ry installation of ROPS, fatal accidents involving tractors represent more than 40% of work-related deaths in agriculture. As death is often due to an overturn that the driver is incapable of predicting, driver assistance devices that can signal critical stability conditions have been studied and marketed to prevent accidents. These devices measure the working parameters of the tractor through sensors and elaborate the values using an algorithm that, taking into account the geometric characteristics of the tractor, pro-vides a risk index based on models elaborated on a theoretical basis. This research aimed to verify one of these stability indexes in the field, using a commercial driver as-sistance device to monitor five tractors on the University of Bologna experimental farm. The setup of the device involved determining the coordinates of the center of gravity of the tractor and the implement mounted on the tractor. The analysis of the stability in-dex, limited to events with a significant risk level, revealed a clear separation into two groups: events with high values of roll or pitch and low speeds, typical of a tractor when working, and events with low values of roll and pitch and high steering angle and forward speed, typical of travel on the road. The equation for calculating the critical speed when turning provided a significant contribution only for events that were typi-cal of travel rather than field work, suggesting a diversified calculation approach ac-cording to the work phase.

Development and validation of a numerical prediction model to estimate the annoyance condition at the operation station of compact loaders.

This paper describes the results of a study aimed at developing and validating a prediction model to assess the annoyance conditions at the operator station of compact loaders by using noise signal objective parameters only. For this purpose, binaural measurements were carried out on 41 compact loaders, both in stationary and real working conditions. The 62 binaural noise recordings were objectively analysed in terms of acoustic and psychoacoustic parameters and then divided into 9 groups and used in specific jury tests to obtain the subjective annoyance scores. Finally, multiple regression technique was applied to the first 6 groups of noise stimuli to develop the model while the remaining groups were used to validate it.