Dynamic STKNN model-based short-time traffic prediction method

A technology of traffic forecasting and modeling, applied in the field of information technology services, can solve the problem of insufficient dynamics in describing traffic conditions in time

Active Publication Date: 2019-04-09
INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
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  • Dynamic STKNN model-based short-time traffic prediction method
  • Dynamic STKNN model-based short-time traffic prediction method
  • Dynamic STKNN model-based short-time traffic prediction method

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[0059] The present invention will be described in further detail below in combination with specific embodiments.

[0060] Such as Figure 1~3 A short-term traffic prediction method based on the dynamic STKNN model is shown, the overall steps of the method are:

[0061] a. Feature representation: construct a three-dimensional tensor that represents the traffic conditions of all road segments at historical moments, and each section of the three-dimensional tensor represents the traffic condition change curve of each road segment for all historical days; through the traffic of each road segment in historical days Conditional averaging, using the average velocity vector to characterize each road segment;

[0062] Assuming that there are N road segments in the road network, the historical traffic conditions of road segment u are X u can be expressed as in, Represents a two-dimensional set of real numbers whose dimension is D×T, Represents the time series of the road segment...

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Abstract

The invention discloses a dynamic STKNN model-based short-time traffic prediction method. The method comprises the steps of: a, feature representation: averaging traffic conditions of each road segment in history days, and carrying out feature representation on each road segment by utilizing an average speed vector; b, automatic traffic mode recognition: automatically recognizing similar traffic modes of road networks through an AP cluster algorithm; c, automatic time interval division: aiming at the automatically recognized traffic modes, automatically dividing time intervals through a K-Means algorithm; and d, self-adaptive STKNN model construction: aiming at each time interval of each traffic mode obtained in the step 2 and 3, respectively constructing an STKNN model. The method is capable of directly helping the traffic management department to generate reasonable and efficient strategies to ease traffic congestion, thereby realizing redistribution of road network traffic flows andhelping the public to realize correct path planning.

Description

technical field [0001] The invention relates to a short-term traffic prediction method, in particular to a short-term traffic prediction method based on a dynamic STKNN model, and belongs to the field of information technology services. Background technique [0002] As an important part of the intelligent transportation system, short-term traffic prediction can help users plan routes reasonably and provide intelligent location services to alleviate road congestion, thereby reducing traffic emissions. At the same time, it can help the transportation department to implement effective traffic control and rationally allocate infrastructure Facilities and optimize the timing of signal lights, thereby improving the operational efficiency of traffic. [0003] In the past few decades, a large number of spatio-temporal modeling methods have been proposed to solve the problem of short-term traffic forecasting. These methods provide alternatives for short-term traffic forecasting, but ...

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Application Information

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IPC IPC(8): G08G1/01
CPCG08G1/0129G08G1/0133
Inventor 陆锋程诗奋彭澎
Owner INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
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