Expressway toll station traffic flow big data prediction method based on multi-target regression

A technology of expressway and prediction method, applied in the fields of big data processing and machine learning, which can solve problems such as not taking into account the correlation between targets, and being unable to build target-specific features

Active Publication Date: 2019-08-02
芽米科技(广州)有限公司
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  • Abstract
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  • Application Information

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Problems solved by technology

The features corresponding to each target are the same, there is no way to build target-

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  • Expressway toll station traffic flow big data prediction method based on multi-target regression
  • Expressway toll station traffic flow big data prediction method based on multi-target regression
  • Expressway toll station traffic flow big data prediction method based on multi-target regression

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Embodiment Construction

[0077] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0078] The technical scheme that the present invention solves the problems of the technologies described above is:

[0079] refer to figure 1 , figure 1 Embodiment 1 of the present invention provides a flow chart of a large data prediction method for traffic flow at expressway toll stations based on multi-objective regression, specifically including:

[0080] 101. Collect historical traffic flow data and weather data and perform preprocessing operations on the data: collect historical traffic flow data and weather data, as follows:

[0081] Collect historical traffic flow data including toll booth ID, toll booth capacity level, whether the toll booth uses an electronic toll collection system, the direct...

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Abstract

The invention discloses an expressway toll station traffic flow big data prediction method based on multi-target regression. The method comprises the following steps: 101, carrying out preprocessing operation on data; 102, marking the data; 103, performing feature engineering construction operation on the data; 104, constructing a multi-target regression model combining the specific characteristics of the target and the target correlation; and 105, predicting the traffic flow of the toll station every 20 minutes from 8 o'clock to 10 o'clock according to the historical traffic flow data of thetoll station, the weather data and other information through the established model. The method mainly preprocesses and analyzes historical traffic flow data, weather data and other information of a toll station to extract characteristics; a multi-target regression model combining target specific characteristics and target correlation is established, and the traffic flow from 8 o'clock to 10 o'clock per 20 minutes is predicted, so that a traffic management department can take measures in time by using big data to reduce the congestion of the toll station.

Description

technical field [0001] The invention belongs to the technical field of machine learning and big data processing, in particular to a large data prediction method for traffic flow at expressway toll stations based on multi-objective regression. Background technique [0002] Highway tollbooths are a well-known bottleneck in transportation networks. During peak hours, long queues at toll booths can overwhelm traffic authorities. Effective pre-emptive countermeasures are necessary to address this problem. These countermeasures include speeding up the toll collection process, temporarily opening more lanes, adaptively adjusting traffic signals, and more. But to take these steps, traffic authorities must receive reliable forecasts of future traffic volumes. These flows are not just one time period, but multiple time periods. This is a typical multi-objective regression problem. Therefore, a large data prediction method for expressway toll station traffic flow based on multi-obj...

Claims

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Application Information

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IPC IPC(8): G06F17/18G06K9/62G06Q50/26
CPCG06F17/18G06Q50/26G06F18/23213
Inventor 王进高选人孙开伟许景益邓欣陈乔松
Owner 芽米科技(广州)有限公司
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