Research And Application Of Roadaccident Prediction Based On Markov Chain

Traffic safety is an important aspect of national economic development and socialstability, and is also one of the two basic tasks in road traffic management. Road accidentprediction is an important element of road safety research, and its purpose is to grasp thefuture state of traffic accidents, to take corresponding countermeasures timely, to control thevarious influencing factors effectively, to avoid the blindness and passivity, and to reducetraffic accidents. The characteristics such as nonlinear, randomness, dynamic and uncertaintyof road traffic system, determine the complexity of road traffic accidents forecast as one of thebehavioral feature quantities of road traffic system. By means of the data in the transportationstatistical yearbook, and Markov theory and grey theory as research tools, in order to improvethe prediction accuracy and reliability as the goal,this paper discuss and study in detail themacro forecast methods of traffic accidents adapted to the different road characteristics.Themain work and conclusions are as follows:1. Against the problem about prediction accuracy is affected during the generalgrey-Markov chain model for using fixed transition probability matrix; the paper improvesgrey-Markov chain model by using sliding transition probability matrix. By means of theimproved grey-Markov chain model, the100,000population death rate of traffic accidentfrom2002to2004has been predicted. The result shows that the prediction accuracy ofimproved gray-Markov chain model is better than that of the ordinary grey-Markov chainmodel, and the new model has a strong practicability.2. Based on the special characteristics of the annually traffic accidents measurableindicators being a dependent stochastic variables, applying sequential cluster method to set upthe classification standard of traffic accident, regarding the standardized self-coefficients asweights,the paper applies the weighted Markov chain to forecast future state of trafficaccident. This method is used and analyzed during the injuried persons from1970to2010inBeijing.This prediction method makes accident prediction results from the point value to theinterval value, greatly improving the prediction reliability.3. In view of the GM(1,1) model fails to analyze many uncertain factors relatedsystems,the paper uses the SCGM(1,1)c model with solid theoretical basis to replace the GM(1,1). Combining with the advantages of weighted Markov Chain, the grey weightedMarkov SCGM(1,1)c model is built to predict future traffic accident. This method is usedand analyzed during road traffic accidents from1975to2010in Beijing. The results showthat the grey weighted Markov SCGM(1,1)c model is reliable and credible,having a strongpracticability.4. Given the traditional GM(1,1) model has inherent bias and own shortcomings, thepaper substitutes the traditional GM(1,1) model for unbiased grey model. The unbiased greymodel is used to fit the development tendency of the forecast system, while Markovprediction is used to forecast the fluctuation along the tendency. Combined with the idea ofnew information priority, the equal dimension and new information unbiased grey Markovmodel is constructed. This method is applied to predict the deaths from2011to2015with thenumber of road traffic deaths from2000to2010. Experiment results show that the equaldimensional and new information grey Markov forecasting model not only can remainadvantages of short-term forecasting accuracy, but also can improve the medium andlong-term forecast accuracy.5. Considering the single gray prediction model has its own assumptions and limitedscopes, this paper puts forward the combined forecasting model based on optimal weightedmethod. The weight coefficients of combined forecasting model were determined by theprinciple of least squares under the constraint condition that weight coefficients sum is oneand objective function that the fitting error squares sum is minimized. The weightedcombination model was used to predict and analyze the road traffic deaths in the year from2001to2010. The results show that combination forecasting model can effectively reduce theprediction error and improve the prediction accuracy than the single forecasting models.