Traffic alarm condition level predication method based on distance metric learning

A technology of distance measurement and prediction method, which is applied in the field of level prediction and can solve the problems of no prediction function and poor accuracy.

Active Publication Date: 2015-08-12
ZHEJIANG YINJIANG RES INST
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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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  • Traffic alarm condition level predication method based on distance metric learning
  • Traffic alarm condition level predication method based on distance metric learning
  • Traffic alarm condition level predication method based on distance metric learning

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] refer to figure 1 . A traffic police level prediction method based on distance metric learning, comprising the following steps:

[0032]Step 1: Multidimensional data collation and traffic police classification

[0033] The collection of historical weather data, historical major event data, construction and road closure and other environmental data together with working days, holidays and historical traffic police data constitute a multi-dimensional historical database of traffic police. In the real urban traffic network, the current The traffic flow is closely related to the flow at the previous moment. The continuous historical traffic police data is divided into n segments according to equal time periods as training samples. Each training sample segment includes weather attributes, major event attributes, environmental factor attributes, Working days and hol...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06K9/62
Inventor 王浩李建元陈涛顾超
Owner ZHEJIANG YINJIANG RES INST
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