Traffic congestion prediction method, equipment and medium based on multiple signal sources

Abstract

The invention discloses a traffic congestion prediction method, equipment and medium based on multiple signal sources. The method includes: obtaining information on each road section on the road network and various training data; performing corresponding preprocessing on the training data according to the data type; Input the preprocessed training data into the congestion prediction model, determine the connection weight on the unit path of the congestion prediction model according to the prediction error and the state distribution of the congestion prediction model; use the trained congestion prediction model to perform congestion prediction on the prediction data; The correlation test is carried out for the adjacent two congestion prediction results. The present invention acquires various types of signal sources as prediction input values, uses neural network to learn multi-dimensional time series, obtains accurate prediction results through multi-signal source comparison, backpropagation and forward propagation adjustment, and timely predicts traffic information, Provide a reference for urban road network planning and urban congestion management.

Description

technical field [0001] The invention relates to the technical field of traffic forecasting, in particular to a traffic jam forecasting method, equipment and medium based on multiple signal sources. Background technique [0002] Traffic forecasting model refers to the quantitative description of the relationship between various elements of traffic phenomena and the relationship between traffic phenomena and various factors of social and economic activities. It is used in traffic analysis and traffic forecasting, and is one of the important technical methods of traffic planning. The input of traffic prediction is known factors, that is, factors that affect urban traffic conditions, but there are many factors that affect urban traffic conditions, such as weather conditions, time, number of vehicles, traffic accidents, traffic lights, etc. [0003] At present, most of the existing traffic prediction technology solutions belong to single signal source data prediction, and use tra...

AI Technical Summary

Problems solved by technology

[0003] At present, most of the existing traffic prediction technology solutions belong to single signal source data prediction, and use traffic image data to carry out urban traffic prediction intelligent control, and do not combine specific traffic lights, GPS, radar and other data for modeling analysis, which is not enough to fully reflect traffic. The actual state of the flow

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