Automatic Detection Method of Traffic Incidents Based on Trend Index and Volatility Index

Abstract

The invention discloses an automatic detection method for a traffic incident based on a tendency indicator and a fluctuation indicator. The automatic detection method comprises the following steps: S1, acquiring real-time traffic data by a sensor; S2, preprocessing the real-time traffic data; S3, calculating the following real-time feature vectors based on the preprocessed real-time traffic data: the tendency indicator, the fluctuation indicator as well as upstream and downstream change indicators; S4, using the real-time feature vectors obtained by calculation as an input sequence of a training model, and adopting the training model for calculating to obtain a corresponding output result serving as a detection result of the traffic incident. According to the automatic detection method disclosed by the invention, the real-time traffic data are acquired by the sensor, and can be used for detecting and judging the traffic incident, so that the change of a traffic state is acquired in time, and the time and the place of the traffic incident are accurately found in time; the automatic detection method can be widely applied to the field of detection of the traffic incidents.

Description

technical field [0001] The invention relates to the field of highway traffic event detection, in particular to an automatic detection method for traffic events based on trend indexes and volatility indexes. Background technique [0002] Glossary: [0003] Occupancy: [0004] Kurtosis: also known as kurtosis coefficient, characterizes the characteristic number of the peak value of the probability density distribution curve at the average value; [0005] Skewness: It is a measure of the skewed direction and degree of statistical data distribution, and it is a digital feature of the asymmetrical degree of statistical data distribution; [0006] Upstream and downstream: In this application, it refers to the upstream section and the downstream section of a certain location of the traffic route; [0007] Dirty data: It means that the data in the source system is not within the given range or meaningless to the actual business, or the data format is illegal, and there are irregu...

AI Technical Summary

Problems solved by technology

When there is a detector failure on the road, it will cause its upstream and downstream to be unable to use this method for detection

At the same time, the start time and end time of traffic events are the key time nodes that characterize the event. At these time nodes, data such as occupancy rate and terminal alarms will also have certain characteristic changes. The traditional California algorithm does not consider before and after the key time nodes. A certain range of data volatility characteristic indicators

In general, the current detection methods cannot effectively detect traffic events, so that the changes in traffic conditions cannot be learned in time.

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