Method for road traffic flow prediction under suddenly occurred traffic event

Abstract

The invention belongs to the technical field of urban road traffic flow prediction and analysis and particularly relates to a method for road traffic flow prediction under a suddenly occurred trafficevent. The method comprises the steps that road traffic event alarm information data is preprocessed, and abnormal data is removed and repaired; a place name address space positional information database is established, traffic event position information is obtained and classified, and spatial and temporal distribution characteristics of road traffic flow and spatial and temporal characteristics of road traffic flow under the suddenly occurred event are analyzed; a random forest algorithm, an ARIMA method and a Kalman filtering method are utilized to conduct traffic flow prediction on time series data and space state data under the suddenly occurred event; a weighted least square method is used for conducting fusion processing on prediction results obtained through a time series data prediction method and a spatial series prediction method, and new prediction results are obtained. In the method, three indexes including a comprehensive error percentage absolute value mean value, an error absolute value mean value and a square-error mean value reach an ideal prediction effect.

Description

technical field [0001] The invention belongs to the technical field of urban road traffic flow prediction and analysis, and in particular relates to a method for road traffic flow prediction under sudden traffic events. Background technique [0002] With the development of the economy and the complexity of urban roads, the relatively passive management methods in the field of transportation in my country will restrict the pace of social development. Inadequate analysis, mining and management of massive data has become the bottleneck of modern transportation services, and data analysis and specific applications are stagnant. [0003] At present, the acquisition of emergency traffic accident information in my country mainly comes from the 122 alarm service desk. Although the 122 alarm service desk records a large amount of natural language alarm information in daily work, it is only simply applied to accident responsibility analysis and accident filing, and there is no system...

AI Technical Summary

Problems solved by technology

[0005] The traditional linear forecasting method uses the linear change trend of historical data to predict the future traffic flow trend. These forecasting methods are simple and easy to understand, but because they cannot reflect the nonlinearity of traffic flow, and only consider the historical data of their own road sections when predicting. Considering the influence of adjacent road sections on it, there are some problems such as local minimum and difficulty in adjusting parameters online, and the prediction accuracy is poor

The nonlinear prediction method takes into account the nonlinear characteristics of traffic flow, which is more practical than the linear model, but it must have a large amount of historical data as support. The larger the amount of data, the higher the prediction accuracy, but the calculation amount is correspondingly larger

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