A short-term traffic prediction method based on multi-task multi-view learning model

Abstract

The invention discloses a short-term traffic forecasting method based on a multi-task multi-view learning model. The method comprises the following steps: 1, constructing a space-time data model individually for each road segment; 2, constructing a multi-core learning model; 3, constructing an objective function by adopting a multi-task multi-view feature learning model; 4, introducing a particleswarm optimization algorithm to optimize the objective function obtained in the step 3; and 5, repeating the steps 1 and 2 for any road segment to obtain input features, bringing the input features into an optimized objective function, and realizing short-term traffic prediction for any road segment. The method realizes the efficient prediction of short-term traffic, solves the problem that the spatial-temporal heterogeneity and the global prediction ability of the model can not reach equilibrium, solves the problem of parameter optimization of the model, and can be widely used in urban planning, human mobility survey, automobile navigation, emergency response, space-time accessibility analysis and traffic pollution modeling.

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 multi-task multi-view learning model, and belongs to the field of information technology services. Background technique [0002] With the continuous development and popularization of sensor networks, mobile positioning, wireless communications, mobile Internet, high-performance computing and storage technologies, a series of time series data with location tags, called spatiotemporal data, has emerged. These spatio-temporal data contain rich and useful information and knowledge that needs to be automatically mined, thus giving birth to the continuous development of spatio-temporal data mining technology. Traffic, as a typical spatiotemporal data, has become a testing ground for spatiotemporal modeling techniques, and many spatiotemporal modeling techniques employ traffic-related applications. Spatiotemporal short-term traf...

AI Technical Summary

Problems solved by technology

[0003] However, under the background of spatio-temporal autocorrelation and spatio-temporal heterogeneity of urban traffic, the existing spatio-temporal short-term traffic prediction models still have many shortcomings: 1) In the modeling process, the existing methods combine each geographic unit (road segment or subregions) as a separate prediction task, while ignoring the global correlation between geographic units, which makes the rationality of the existing methods insufficient.

The training time of the model usually increases exponentially with the number of parameters, and it is difficult to determine the optimal model structure

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