Traffic police level prediction method based on distance metric learning

A technology of distance measurement and prediction method, applied in the field of level prediction, which can solve the problems of poor accuracy and no prediction function, and achieve the effect of reducing influence and improving prediction accuracy

Active Publication Date: 2018-02-27
ZHEJIANG YINJIANG RES INST
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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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  • Traffic police level prediction method based on distance metric learning
  • Traffic police level prediction method based on distance metric learning
  • Traffic police level prediction 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

A traffic police level prediction method based on distance metric learning, which predicts the urban traffic police level under known weather data, time data, environmental data, etc., and organizes multi-dimensional historical data according to the requirements of the traffic police command department Classification, using the generalized Mahalanobis distance measurement method to learn the multi-dimensional historical data after classification and marking, the distance measurement learning matrix obtains the weight of each feature attribute to the traffic police level, and the feature attribute with a large weight contributes a lot to the classification , calculate the similarity between the current multi-dimensional data and historical data according to the weighted Euclidean distance, select K historical data most similar to the current data to vote on the police level, and the police level with the highest vote is the current traffic police level prediction results. The invention effectively realizes prediction and has better 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 Patents(China)
IPC IPC(8): G06Q10/04
Inventor 王浩李建元陈涛顾超
Owner ZHEJIANG YINJIANG RES INST
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