Traffic jam prediction method and system based on convolutional long short-term memory neural network

Abstract

The invention relates to a traffic jam prediction method based on a convolutional long short-term memory neural network. The method comprises the following steps: S1, acquiring historical information related to traffic jam; S2, constructing a traffic jam index based on historical information; S3, constructing a prediction model based on a convolutional neural network and a bidirectional long-short time memory neural network; S4, training a prediction model based on the traffic jam indexes; S5, predicting the traffic jam condition in the future time period through the prediction model based on the historical information. In addition, the invention also discloses a traffic jam prediction system based on the convolutional long short-term memory neural network. According to the method, a reasonable and visual traffic jam index is constructed based on the traffic speed, and the time, space and periodic characteristics of the traffic data are fully extracted by using the convolutional neural network and the bidirectional long-short-term memory network, so that the fitting degree of the output prediction result and the actual traffic jam condition is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of transportation, in particular to a prediction method for predicting traffic conditions, in particular to a traffic congestion prediction method based on convolutional bidirectional long-short-term memory neural network. Background technique [0002] Traffic congestion has seriously affected people's quality of life. Even though the traffic management department has introduced various mitigation policies, with the explosive growth of the number of motor vehicles, traffic congestion is still one of the most important traffic problems we should pay attention to at present. More accurate short-term traffic congestion prediction can not only provide reliable management basis for traffic managers, but also show the actual road status to road users, which is convenient for them to make judgments, and plays a huge role in alleviating traffic congestion and reducing traffic accidents. effect. [0003] Sho...

AI Technical Summary

Problems solved by technology

For the existing short-term traffic congestion prediction methods, the accuracy is not high, the real-time performance is not en

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