Traffic alarm condition level predication method based on distance metric learning

Abstract

The invention provides a traffic alarm condition level predication method based on distance metric learning. The city traffic police situation level is predicated under the condition of known weather data, time data and environment data, the sorted multidimensional historical data is classified according to the requirements of a traffic police command department, a generalized Mahalanobis distance measure method is used to learn the classified and marked multidimensional historical data, and the weight value of each characteristic attribute for a traffic alarm condition level is obtained by a distance metric learning matrix. The classification contribution degree of a characteristic attribute with a large weight value is large, the similarity of the current multi-dimensional data and historical data is calculated according to an Euclidean distance with a weight value, K historical data which is most similar to the current data is selected to carry alarm condition situation level vote, and an alarm condition level with the highest vote is taken as the predication result of a current traffic alarm condition level. The method has the advantages of effective realization of prediction and high accuracy.

Description

technical field [0001] The invention belongs to the field of intelligent traffic, and in particular relates to a method for predicting urban traffic police levels. Background technique [0002] With the rapid development of the economy, the rapid growth of the number of motor vehicles in the urban traffic system has greatly increased the probability of traffic accidents and traffic congestion. For the motor vehicle drivers on the road, the traffic management personnel hope to obtain the traffic police situation level in the macro region. An effective prediction of the regional police situation level in a certain period of time in the future will help the traffic management department to optimize the deployment of police force and formulate corresponding measures. plan to ease the traffic pressure in key areas. [0003] Patent 201410610003.7 collects traffic flow data including working days, non-working days and major holidays, reorganizes the traffic flow data in the same c...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the disadvantages of no prediction function and poor accuracy in the existing traffic police situation discrimination methods, the present invention provides a traffic police situation level prediction method based on distance metric learning that can effectively realize prediction and have good accuracy.

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