Discriminant analysis-based high road real-time traffic accident risk forecasting method

Abstract

The invention relates to a discriminant analysis-based high road real-time traffic accident risk forecasting method. The method comprises the following steps of: building a high road accident risk discrimination model for a detection area; substituting real-time traffic flow characteristic parameters into the high road accident risk discrimination model; and judging whether the risk of traffic accident exists or not. According to the method, traffic accidents can be forecasted in real time by using the real-time traffic flow characteristic parameters acquired by high road traffic detection equipment, the method has relatively high forecasting precision, and technical defects and shortages in the prior art for analyzing traffic safety by using aggregated statistics are overcome. The method has practical engineering application value in the aspects of discrimination of the risk of the high road traffic accidents and forecast of the traffic accidents.

Description

technical field [0001] The invention belongs to the technical field of traffic intelligent management and control, uses a discriminant analysis model, proposes a detection method for the occurrence risk of express road traffic accidents, and predicts express road traffic accidents, which is a real-time traffic accident risk of express roads based on discriminant analysis method of prediction. Background technique [0002] Since the reform and opening up, with the rapid growth of our country's economy, the expressway has experienced a leap-forward rapid development in our country. In 1986, my country's first expressway was completed and opened to traffic. By the end of 2008, the mileage of my country's expressways had exceeded 60,300 kilometers, ranking second in the world. From 2003 to 2007, a total of 105,687 road traffic accidents occurred on national highways, resulting in 30,588 deaths, 77,505 injuries, and direct property losses of 2.62 billion yuan. [0003] Therefore...

AI Technical Summary

Problems solved by technology

However, the disadvantage of these methods is that aggregate statistics such as AADT need to be measured, so it is impossible to determine "accident black spots" (accident-prone locations) in real time, and it takes a long period of time (1 month to 3 years) Traffic accident and traffic flow historical data

In addition, these aggregated statistics of AADT cannot reflect factors that change over time and have a significant impact on the occurrence of traffic accidents (such as the fluctuating characteristics of traffic flow and weather)

Therefore, these accident prediction models based on static traffic data are difficult to predict the risk of highway accidents in real time.

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