Traffic Accident Analysis Based On Data Mining

During the period of the12th Five-Year Plan, along with China's continued rapid socioeconomic development, the in-depth urbanization development, the increasingly improved infrastructure of road traffic and constant growth of vehicle population, driver population and traffic flow, road traffic has been playing an increasingly apparent role in guaranteeing and promoting economic and social development. Subsequently, road traffic safety has become the key issue relating to people's lives and property safety, influencing and restricting the quality and efficiency of economic and social development, being highly concerned and focused in national security strategy.Road traffic accident is a process of damage to people or property due to the disordered coupling of people, vehicle, road, environment and other dynamic or static factors. The historical data of road traffic accident can immediately reflect the interaction relationship among people, vehicle, road and environment when the accident occurred. Due to the features of the occurrence of road traffic accidents, including multifactor, contingency and ambiguity, the analysis research on this topic usually chooses the historical data of road traffic accident as the research objective, proposes related theories and research methods, aiming at analyzing the influential factors of road traffic accidents from multiple aspects and levels in order to reveal the potential rule and features of the correlations among the historical data of various accidents and effectively support traffic safety management and accident prevention and treatment.Data mining is a kind of data analysis method, aiming at digging out implicitly unknown concepts, rules, regulations and modes with potential value to decision making. The important and difficult points of putting the historical data of road traffic accident as the object of data mining to conduct an analysis research on road traffic accident are as follows:on the one hand, accident historical data are usually used for the statistical description of four indicators:frequency, injury, death and property loss, but failing to fully dig out and reflect the potential information value; on the other hand, due to the discreteness, multi-dimension and fuzzy factor integration of accident historical data and the integrity, objectivity and standardization problems during information collection process, the application of accident historical data mining is very limited, further directly influencing the application effect of classic data analysis theories and methods. Under the aforementioned background, this paper, based on the features of China's data collection of road traffic accident and the key problems in data analysis application, constructs an analysis system of road traffic accident based on data mining, by applying data classification, regression, clustering analysis, association rules mining and other theories and methods related to data mining in three dimensions, namely accident severity analysis, accident prediction and causation analysis in order to deeply explore the interaction relationship between road traffic accident and people, vehicle, road, environment and other factors. This paper achieves the following results.1. This paper sets road traffic accident information collection data as its research object, applies theories and algorithms related to data mining, proposes the analysis system of road traffic accident, providing with data foundation and theoretical basis for revealing influential factors and laws of function of traffic accidents, predicting traffic accident trends, constructing prevention mechanism of traffic accident and improving the security level of the overall road traffic system.2. Based on the full understanding of the distribution feature of background factors and influential mechanism of road traffic accidents, this paper conducts an analysis research on the similarities and differences of data collection technologies and data features in various countries, and focuses on analyzing China's current information collection field of road traffic accidents, especially the current status and features of accident information structure, to lay a foundation for the implementation of data mining.3. This paper introduces the classification method of data mining theory into the analysis research of accident severity, to construct linear/nonlinear TPMSVM disaggregated models respectively according to binary classification and multiple classification methods. Meanwhile, this paper proposes the background factor analysis method of accident severity based on feature selection, with which operator can sort data according to the contributions of various characteristic variable to the effect of model classification respectively, in order to dig out the core characteristic variables that influences the severity of accidents. In the linkage of empirical research, this paper obtains the linear/nonlinear optimal classification accuracy and ranking of feature variable importance under the condition of cross validation respectively, through feature selection and parameter optimization algorithm.4. Aiming at the prediction of road traffic accident trend, this paper proposes the prediction of time series of traffic accident four indicators by integrating ARIMA and SVR model as an combinative model, achieve the purpose of time point prediction. Meanwhile, in order to obtain the information of the overall change trend and change space of road traffic accidents, this paper further proposes, based on the trend prediction model of information granulation SVR, to realize the prediction of the trend and scope of traffic accident four indicators through constructing triangular fuzzy particles and related SVR modeling.5. Based on the distributional features of traffic accident attributes, this paper respectively constructs the clustering analysis model based on two-step BIRCH and the pattern recognition model of accident causation based on Decision Tree, to realize the micro digging of severe traffic accident causation analysis.