Short-term traffic flow forecasting method based on three-layer K nearest neighbor

A technology of short-term traffic flow and prediction method, which is applied in the field of short-term traffic flow prediction based on three-layer K-nearest neighbors, which can solve the problems of uncertainty, complex traffic flow prediction, low stability, and complex algorithm.

CN105336163AActive Publication Date: 2016-02-17SHANDONG EAGLE SOFTWARE TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2016-02-17

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Abstract

The invention discloses a short-term traffic flow forecasting method based on three-layer K nearest neighbors. The short-term traffic flow forecasting method comprises the steps of: (1) counting traffic flow based on fixed time intervals and establishing a historical sample database; (2) evaluating shape similarity between a current point and points in the historical sample database by adopting similarity deviation degree and correlation coefficient respectively, and performing first-layer screening of points; (3) evaluating the points screened in the first layer according to hit rate and shape similar distance, and performing second-layer screening of points; (4) and evaluating matching distances between the current point and the points screened in the second layer by using an Euclidean distance method, and outputs a forecasting result by adopting a weighted mean value of inverse similar distance of a combination shape of the traffic flow at the corresponding next moment when nearest neighbor points are translated to the current point. The short-term traffic flow forecasting method adopts a two-layer shape similarity matching function, takes the shape matching distances between the nearest neighbor points and the current point into account, and improves accuracy and timeliness of short-term traffic flow forecasting.
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Description

technical field

[0001] The present invention relates to a short-term traffic flow prediction method, and more specifically, to a short-term traffic flow prediction method based on three-layer K-nearest neighbors. Background technique

[0002] With the development of social economy and the continuous expansion of city scale, urban traffic problems are becoming more and more prominent. Intelligent transportation system is regarded as an important means to solve the problem of traffic congestion. With the advancement of relevant technologies in various fields of intelligent transportation, both traffic travelers and traffic managers are eager to obtain real-time and dynamic traffic operation status on the road. Real-time dynamic traffic allocation has become a key technology of intelligent transportation systems. Real-time dynamic traffic allocation needs to predict the traffic flow at the next decision-making time t+1 and several moments later at the time t when the control v...

Examples

Embodiment Construction

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] Such as figure 1 As shown, the flow chart of the short-term traffic flow prediction method based on the three-layer K-nearest neighbors of the present invention is provided, which is realized by the following steps:

[0056] a). Establish a historical sample database, and establish m historical traffic flow state vectors V of the road section to be predicted according to the historical traffic flow of the road section to be predicted h1 , V h2 ,...,V hm , forming a historical sample database, where the historical traffic flow state vector is shown in formula (1):

[0057] V hi =[v hi (t-l+1), v hi (t-l+2),...,v hi (t)](1)

[0058] In the formula, 1≤i≤m, m is the number of historical traffic flow state vectors, v hi (t-l+1), v hi (t-l+2),...,v hi (t) are the historical traffic flow state vector V hi In t-l+1, t-l+2,..., the traffic flo...