Daily traffic flow parameter prediction method fusing holiday and festival influences

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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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 flow forecasting are based on simple deep learning methods, and do not consider the relationship between traffic flow parameters and holidays. There must be obvious differences, and the daily traffic flow conditions on holidays and non-holidays may also be very similar, and the current research also lacks consideration of this point

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