Traffic flow data prediction method, storage medium and computer equipment

Abstract

The invention discloses a traffic flow data prediction method. The prediction method comprises the steps of obtaining historical traffic flow data related to a to-be-predicted moment; extracting spatial features of the historical traffic flow data by using the trained spatial feature extraction network; inputting the extracted spatial features into a trained time sequence feature extraction network, wherein the time sequence feature extraction network comprises a first long-short-term memory neural network and a second long-short-term memory neural network; wherein the first long-short-term memory neural network outputs long-term time sequence characteristics, and the second long-short-term memory neural network outputs short-term time sequence characteristics and congestion characteristics; and outputting traffic flow data at the moment to be predicted according to the long-term time sequence characteristics, the short-term time sequence characteristics and the congestion characteristics. Compared with a traditional long-term and short-term memory neural network, the long-term and short-term memory neural network based on the ordered neurons is used, influences brought by emergencies can be better captured, and the method is more suitable for variable traffic environments.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a training method and a prediction method of a traffic flow prediction model, a computer-readable storage medium, and a computer device. Background technique [0002] With the improvement of people's living standards and the change of consumption concept, cars have become the standard configuration of every family. At the same time, the transportation network planning of many cities is early, and there is no road planning space reserved for future urban development. Due to the rapid increase in the number of cars and the unreasonable planning of roads in old cities, traffic congestion has become a persistent problem in almost every developing city. Extreme traffic jams in the morning and evening rush hours degrade people's life experience. Therefore, reasonable route planning or accurate off-peak travel is a huge demand for people in congested cities. However,...

AI Technical Summary

Problems solved by technology

For example, if there is a traffic accident in a certain place, and the traffic flow in this place is seriously congested, then the traffic flow in this place will have an impact on the traffic flow for a long time in the future, but ordinary LSTM is not capable of extracting the features of such nodes.

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