Narrow Results

Human activities can play a crucial role in the statistical properties of observables in many complex systems such as social, technological, and economic systems. We demonstrate this by looking into the heavy-tailed distributions of observables in fatal plane and car accidents. Their origin is examined and can be understood as stochastic processes that are related to human activities. Simple mathematical models are proposed to illustrate such processes and compared with empirical results obtained from existing databanks.

Unsuitable driving behaviors often lead to the occurrence of traffic accidents. To reduce accidents and to prolong human life, simulated investigations are highly desirable to evaluate the effect of traffic safety in terms of number of traffic accidents. In this paper, a three-lane traffic flow model is proposed to analyze the probability of the occurrence of traffic accidents on highway. We define appropriate driving rules for the forward moving and lane changing of the vehicles. Three types of vehicle accidents are designed to investigate the relationships between different driving behaviors and traffic accidents. We simulate four road driving strategies, and compute the traffic flow, velocity, lane-changing frequency and the probability of the occurrence of traffic accidents for different road driving strategies. According to the simulation and analysis, it is shown that the probability of the occurrence of traffic accidents can be reduced by using the specified road driving strategies. Additionally, we found that the occurrence of traffic accidents can be avoided when the slow vehicles are suitably constrained to move on a three-lane highway.

The occurrence of traffic accidents is regular in probability distribution. Using big data mining method to predict traffic accidents is conducive to taking measures to prevent or reduce traffic accidents in advance. In recent years, prediction methods of traffic accidents used by researchers have some problems, such as low calculation accuracy. Therefore, a prediction model of traffic accidents based on joint probability density feature extraction of big data is proposed in this paper. First, a function of big data joint probability distribution for traffic accidents is established. Second, establishing big data distributed database model of traffic accidents with the statistical analysis method in order to mine the association rules characteristic quantity reflecting the law of traffic accidents, and then extracting the joint probability density feature of big data for traffic accident probability distribution. According to the result of feature extraction, adaptive functional and directivity are predicted, and then the regularity prediction of traffic accidents is realized based on the result of association directional clustering, so as to optimize the design of the prediction model of traffic accidents based on big data. Simulation results show that in predicting traffic accidents, the model in this paper has advantages of relatively high accuracy, relatively good confidence and stable prediction result.

In the modern world, traffic accidents frequently result in fatalities and serious injuries. The ability of machine learning to foretell the severity of road traffic accidents has shown great promise. The classification of traffic accidents has shown to be a good application for algorithms like random forest. In this paper, performance on a specific dataset has been evaluated using random forest and other models. The dataset used for the analysis came from a publicly accessible source and contained information on several variables like the type of road, the time of day, and the weather. In order to analyze the severity of accidents, a number of algorithms were applied to the dataset, including decision tree, random forest classifier, and logistic regression algorithm. Each model was evaluated on parameters such as model accuracy, precision and recall of the model, and F1 score. The random forest classifier outperformed the other models, achieving an accuracy of 98.48%. The study concludes that machine learning algorithms can accurately predict the severity of road traffic accidents, which could help to reduce the number of accidents and fatalities on the road.

Traffic accidents have become a more and more important factor to restrict the development of economy and affect the safety of human. Gray System quests for the inner relation through the original data, this is an approach to find out the rule of data through other data. Highway traffic accident forecasting model based on Gray System uses some original data, through theory of Gray System, processing the data and modeling GM (1,1). Through the validation of actual data, error of GM (1,1) is minor, it can be used in actual forecasting.