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Urban traffic flow prediction method based on tensor filling

A forecasting method and technology of traffic flow, applied in the direction of traffic flow detection, etc., can solve the problems of sudden failure and incompleteness of acquisition equipment, failure to overcome the accuracy of traffic flow, etc., and achieve the effect of overcoming poor forecasting accuracy and improving forecasting accuracy.

Active Publication Date: 2018-01-09
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

Traffic flow data has the characteristics of time correlation and space correlation. The traffic flow prediction is analyzed through the data collected by sensor devices and future traffic predictions are made. Sensor devices include mobile devices and fixed devices. Common mobile devices include mobile phones, Tablet PCs, etc. Common fixed devices include ground sensing coils, etc. The prediction of traffic status depends on a large amount of historical data collected by sensors, and the historical data is often incomplete when considering the sudden failure of some acquisition devices in actual use. The previous prediction The method does not overcome the defect of low accuracy in predicting traffic flow with incomplete historical data

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  • Urban traffic flow prediction method based on tensor filling
  • Urban traffic flow prediction method based on tensor filling
  • Urban traffic flow prediction method based on tensor filling

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Embodiment Construction

[0031] The technical solution of the present invention will be described in detail below in conjunction with the drawings.

[0032] The present invention is a method for predicting urban traffic flow based on tensor filling, including the following steps:

[0033] Step S1: Collect traffic flow state data near the predicted point;

[0034] Step S2: Make initial filling based on the collected traffic data;

[0035] Step S3: Establish a traffic data tensor;

[0036] Step S4: According to the traffic flow data tensor, dynamic filling is decomposed based on the tensor, and dynamic filling prediction is performed.

[0037] Further, in this embodiment, the traffic flow state data includes: the number of vehicles passing by the corresponding day, time, and location. The collected traffic flow state data is processed by the above method through a remote server, and the traffic flow is predicted.

[0038] Further, in this embodiment, traffic flow data of k locations adjacent to the same time m day...

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Abstract

The present invention relates to an urban traffic flow prediction method based on tensor filling. The method comprises the following steps: the step S1: collecting traffic flow state data adjacent toa prediction point; the step S2: performing initial filling according to the collected traffic data; the step S3: design a traffic data tensor; and the step S4: performing dynamic filling prediction based on tensor decomposition dynamic filling according to the designed traffic flow data tensor. The urban traffic flow prediction method based on tensor filling overcomes defects that prediction precision is not good in the condition that a traffic flow data source has deficiencies in the prior art; and moreover, a mode of tensor window division dynamic filling is employed while a traffic flow tensor model is constructed, so that prediction precision is improved in the condition that the traffic flow data source has deficiencies.

Description

Technical field [0001] The invention relates to a method for predicting urban traffic flow based on tensor filling. Background technique [0002] With the rapid economic development, the problem of traffic congestion has become one of the primary factors considered by travelers. The waste of travel time and cost caused by traffic congestion has also indirectly brought about various degrees of economic losses and a decline in urban operating efficiency. How to alleviate the road pressure caused by traffic congestion, planning daily travel is a problem that needs to be solved. The accurate prediction of urban traffic flow can save travelers the travel time cost, reduce the road congestion rate, and improve the efficiency of urban operation. Great commercial value. [0003] The traffic flow forecasting methods that have been proposed at home and abroad mainly include time series method, Kalman filter, chaos theory, neural network and support vector machine (SVM). Traffic flow data h...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01
Inventor 郑海峰苗荣臻林凯彤冯心欣魏宏安
Owner FUZHOU UNIV
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