Fast Realization of Collision Risk Calculation in Power Line Patrol Flight Based on CUDA

In this paper, a low-altitude risk collision model based on CUDA is designed to avoid problems that may occur in the process of unmanned aerial power patrol. By collecting and analyzing the data related to the unmanned aerial power patrol task, the collision accident probability is extracted and the probability distribution model and the influence of weather factors on the collision risk are combined. The model validates the collision risk of unmanned aerial vehicles in different locations and verifies the reliability and computational efficiency of the model based on different operating systems. The model algorithm can effectively improve the response time to avoid collision risk during UAV patrol and reduce the risk level of UAV collision accidents.

Preliminary Risk Assessment of Dam Failure at the Location of the Cukaru Peki Deposit, Bor (Serbia)

Industrial waste landfills, as evidenced by frequent accidents occurring in recent years, are regarded as one of the most hazardous facilities in the world. For the adequate management of a landfill, risk assessments of dam failures should be performed before operations begin. This paper deals with the preliminary risk assessment used for the tailings and pyrite concentrate storage facilities, as well as the drainage waters reservoir, which are currently at the development and construction stage in the Cukaru Peki deposit located in eastern Serbia. The research was conducted to establish the facts and level of risk at an early stage to allow for timely prevention of potential accidents and bring operational practice in line with design requirements. The annual failure probability was estimated using a semi-empirical method, based on the dam stability factor. While, the framework proposed by the New Zealand Society on Large Dams was applied to assess the consequences of potential failures. The risk was assessed as a function of accident probability and the severity of possible consequences, and a 7 × 7 risk matrix was applied for analysis and evaluation. The level of dam failure risk at the location of the Cukaru Peki deposit was preliminarily assessed as moderate and conditionally tolerable, based on a low estimated probability of accident and a significant severity of consequences. Once the operation of these facilities starts risk assessments should be regularly updated, in order to maintain this level, and in accordance with the current situation, the modelling of specific accident scenarios should be included.

A model for weather-related traffic variations and accident probabilities on roads

<p>Weather conditions affect both road traffic volume and the probability of road accidents. The aim of this study is improve the understanding of both effects as well as their interactions. In a first step, we develop generalized linear models for hourly road traffic counts at 1400 traffic stations on German federal roads and highways. It is distinguished between different vehicle types, including motorbikes, cars, delivery vans and trucks. Different meteorological variables are derived from reanalysis and radar data. The impacts of these variables on the predictive skill of the models is analyzed. In particular models for motorbike counts show large improvements, if meteorological predictors are added to the model. At weekends in the afternoon the mean squared errors of modeled motorbike counts are reduced by up to 60%. Temperatures around 25°C, no precipitation, low cloud cover and low wind speeds lead to the highest motorbike counts. In a second step, the information derived from the traffic models is used to improve models for hourly probabilities of road accidents. These models are based on police reports, which are available at the level of administrative districts, and can now explicitly take traffic volume into account. It is shown that in particular winter conditions like precipitation and freezing temperatures lead to a significant increase in accident probability. Especially the probabilities of roadway departures show an increase under such conditions. The models presented in this study are suitable for the integration in risk-based warning systems and have the potential to improve risk perception and behavior of warning recipients.</p>

Should Liability Insurance Be Compulsory for Bicycle Accidents?

This study examines whether the introduction of compulsory bicycle liability insurance is socially desirable when evaluation bias—the difference between the objective and subjective evaluations of liability amounts—exists. The main results of this study are summarized as follows. First, when there is no evaluation bias, the introduction of compulsory bicycle liability insurance is socially desirable when the interest rate is high without any condition, and the loading rate is low, the maximum amount of liability is small, and the effort cost is high if the loading rate is higher than the interest rate. Second, if there is an evaluation bias and the accident probability is uniformly distributed, more severe additional conditions are needed for deriving the same results. The study concludes that the evaluation bias prevents the realization of the situation in which the introduction of compulsory bicycle liability insurance is socially desirable.

Translating weather forecasts to road accident probabilities

<p>In Germany about 1000 severe road accidents are recorded by the police per day. On average, 8 % of these accidents are related to weather conditions, for example due to rain, snow or ice. In this study we compare several versions of a logistic regression models to predict hourly probabilities of such accidents in German administrative districts. We use radar, reanalysis and ensemble forecast data from the regional operational model of the German Meteorological Service DWD as well as police reports to train the model with different combinations of input datasets. By including weather information in the models, the percentage of correctly predicted accidents (hit rate) is increased from 30 % to 70 %, while keeping the percentage of wrongly predicted accidents (false-alarm rate) constant at 20 %. Accident probability increases nonlinearly with increasing precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are approximately 5 times larger at negative temperatures compared to positive temperatures. When using ensemble weather forecasts to predict accident probabilities for a leadtime of up to 21 h ahead, the decline in model performance is negligible. We suggest to provide impact-based warnings for road users, road maintenance, traffic management and rescue forces.</p>

Study on comprehensive index system of production safety accident in chemical enterprise

This paper classified, extracted and characterized the causes of accidents based on accident statistics of chemical enterprises in Jiangsu Province from 2015 to 2019. The internal relationship between the accident result and various causes is analyzed from the aspects of human, machine, material, method, ring and pipe; and using the analytic hierarchy process to establish the comprehensive evaluation index system of the accident, which was applied to predict the accident probability of chemical enterprise. The probability of production safety accident in a chemical enterprise is predicted by using this method, and the probability of the accident is at level of "more likely to happen". The example showed that the analytic hierarchy process has strong operability and good effect, and can be used to predict the accident risk of chemical enterprises. causes and can realize the accident probability prediction of chemical enterprises.

A Rural Road Accident Probability Model Based on Single-Vehicle Hazard Properties including Hazard Color and Mobility: A Driving Simulator Study

Road safety has recently been considered an important issue in the country. Single-vehicle accident statistics show the importance of this issue. From a safety viewpoint, drivers need to have a reasonable time window for hazard recognition and reaction; therefore, the hazard has to be in sight from a distance preferably longer than the standard minimum stopping sight distance. Nevertheless, if the roadside configuration makes the sight available for a very long distance, the hazard properties are the ones defining the visibility. The hazard size, color, and mobility are some of the most important hazard properties, which may mainly interact with ambient light (like being day or night) and driving speed. In this research, effect of hazard properties on driving accident likelihood was investigated in a condition that enough recognition and reaction time window was available for the driver to provide a ceteris paribus experiment. To fulfil that in a safe experiment condition, a driving simulator was used to test the behavior of 90 licensed drivers encountering an average of 14 hazards with various sets of properties. Based on the findings of this research, there are some interactions between influential hazard properties. The results imply that it is approximately 23% more likely to observe an accident when encountering a dark small stationary hazard at nighttime like a dark-colored with an observed size of 0.5 m × 0.5 m (e.g., a stone) than a major moving light-colored hazard in the daytime like a camel of 1.5 m ∗ 2 m in size. A green-colored hazard is 27% less likely to involve in an accident at nighttime than hazards with other colors. Each 10 km/h speed increment leads to 1.9% more accident likelihood, and every time the driver encounters a hazard, they will be 0.84% less likely to crash next time.

Predictive modeling of hourly probabilities for weather-related road accidents

Impacts of weather on road accidents have been identified in several studies with a focus mainly on monthly or daily accident counts. This study investigates hourly probabilities of road accidents caused by adverse weather conditions in Germany on the spatial scale of administrative districts using logistic regression models. Including meteorological predictor variables from radar-based precipitation estimates, high-resolution reanalysis and weather forecasts improves the prediction of accident probability compared to models without weather information. For example, the percentage of correctly predicted accidents (hit rate) is increased from 30 % to 70 %, while keeping the percentage of wrongly predicted accidents (false-alarm rate) constant at 20 %. When using ensemble weather forecasts up to 21 h instead of radar and reanalysis data, the decline in model performance is negligible. Accident probability has a nonlinear relationship with precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are approximately 5 times larger at negative temperatures compared to positive temperatures. The findings are relevant in the context of impact-based warnings for road users, road maintenance, traffic management and rescue forces.