Daily traffic flow parameter prediction method fusing holiday and festival influences

A forecasting method and technology for holidays, applied in the field of intelligent transportation, can solve problems such as not considering the relationship between traffic flow parameters and holidays, and achieve excellent forecasting results, obvious forecasting advantages, and significant practical significance

Pending Publication Date: 2022-01-25
浙江省机电设计研究院有限公司
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Problems solved by technology

[0003] The daily traffic flow conditions of holidays (including national holidays and the day before and after national holidays) are often quite different from those of non-holidays, so capturing the correlation between traffic flow parameters and holidays is of great significance. However, In recent years, most studies on traffic

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  • Daily traffic flow parameter prediction method fusing holiday and festival influences
  • Daily traffic flow parameter prediction method fusing holiday and festival influences
  • Daily traffic flow parameter prediction method fusing holiday and festival influences

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Embodiment Construction

[0072] As attached in the manual figure 1 Shown is the overall block diagram of the method of the present invention. A method for predicting daily traffic flow parameters that incorporates the influence of holidays, including the following steps:

[0073] c1. Collect traffic flow parameter data;

[0074] c2. Label the environmental factors;

[0075] c3. Mark holiday labels; the holidays include national holidays, the day before the national holiday, and the day after the national holiday;

[0076] c4. Define the index that characterizes the degree of difference between holiday and non-holiday traffic conditions, namely the daily speed data difference rate S; S is the daily speed data of any holiday within a given time range under the premise of selecting the traffic flow parameter as speed Compared with the average daily speed data of all non-holidays, the percentage of the number of time intervals whose absolute percentage error exceeds a certain threshold to the total nu...

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Abstract

The invention provides a daily traffic flow parameter prediction method fusing holiday and festival influences. Based on a deep learning method formed by a convolutional neural network (CNN) and a long-short term memory (LSTM) network, a traffic flow mode is combined with each environmental factor, the influence of holidays and festivals on the traffic flow mode is considered at the same time, and the characteristic that whether the holidays and festivals are special or not extracted from traffic flow parameter data is fused, so that traffic flow parameter prediction of which the time sequence length is a whole day is realized. The method is excellent in prediction effect, and example analysis shows that the method is high in prediction precision, high in operability and superior to many existing methods.

Description

technical field [0001] The invention relates to a prediction method of daily traffic flow parameters which integrates the influence of holidays, and belongs to the field of intelligent transportation. Background technique [0002] Traffic flow prediction is an important issue in intelligent transportation, and it has been researched for more than 20 years. The prediction of traffic flow parameters (flow rate, speed) can provide theoretical support for the planning and design of traffic facilities, and also provide a reference for traffic management and control. Among them, the role of daily traffic flow parameter prediction is mainly to help traffic management agencies timely Arrange for appropriate management or signal control schemes to ensure operational efficiency. [0003] The daily traffic flow conditions of holidays (including national holidays and the day before and after national holidays) are often quite different from those of non-holidays, so capturing the corre...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q10/10G06Q50/30G06N3/04G06N3/08
CPCG06Q10/04G06Q10/06393G06Q10/109G06Q50/30G06N3/08G06N3/044G06N3/045
Inventor 李保陈诺金盛沈航于涵诚金炎君
Owner 浙江省机电设计研究院有限公司
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