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Abstract
This paper recommends the rolling optimization strategy based on the initial data of road traffic accidents, and builds the rolling optimization-grey Markov dynamic prediction model, which can effectively resolve the matter that the precision of accident forecast is influenced by the time benefit of the predicted data. In order to predict the development tendency of road traffic accidents and further improve the prediction precision of random time series, this paper uses Markov chain theory to probe into the transition law between different states. The case study shows that this measure has good forecast precision and practicability in a certain period of time, and can offer Reference for road traffic accident forecast and traffic safety warning.
Highlights
The two boundedness of road traffic accident forecast ways are the high requirement of prediction data and the strict requirement of mathematical model precision
On the basis of the above analysis and related research, a grey Markov method based on rolling optimization is put, which unites Markov method with GM (1,1) prediction model
The main contribution s of this paper are as follows: (1) The rolling optimization strategy resolves the matter that the forecast precision is quite influenced by the softening data of time line
Summary
The two boundedness of road traffic accident forecast ways are the high requirement of prediction data and the strict requirement of mathematical model precision. It is still difficult to predict road traffic accidents mainly because of the randomness, nonlinearity, volatility and time variation of the initial data. At present, such ways as time series[1], support vector machine (SVM)[2] and Markov method[3] are mostly traffic accident forecast on account of data-driven and statistical analysis. Time series method is hard to establish training and learning model mainly because it can describe the cyclical rules of data, it is impractical to take notes and predict all data. Grounded on rolling data prediction theory[5], this model constantly increases new forecast data and removes old data, and gains the forecast data in the period of study
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