Traffic flow prediction method based on deep learning nerve network structure

Abstract

The invention discloses a traffic flow prediction method based on a deep learning nerve network structure. Various kinds of traffic flow data are collected, a depth automatic encoder model is utilized for training the collected various kinds of traffic flow data, the depth automatic encoder model is adjusted in the training process, and finally, the adjusted depth automatic encoder model is used for predicting a short-period traffic flow. By adopting the method, deeper excavation analysis is carried out on traffic flow data, so that the short-period prediction of the traffic flow is more accurate, and the performance is better.

Description

technical field [0001] The invention relates to a traffic flow forecasting method based on a deep learning neural network structure, belonging to the technical field of traffic forecasting. Background technique [0002] With the development of my country's automobile industry, the problem of road congestion in cities and expressways has become increasingly serious. Through in-depth mining of traffic flow data and the establishment of a short-term traffic flow prediction model on this basis, it can effectively predict traffic congestion and guide vehicles to choose reasonable travel routes. [0003] Short-term traffic flow predicts the future traffic conditions on a certain road section or a certain path, and the time interval generally does not exceed 15 minutes. For managers, this prediction can be used to formulate and implement traffic management plans, regulate traffic flow, and alleviate possible traffic congestion and safety hazards during this period. Compared with ...

AI Technical Summary

Problems solved by technology

However, with the advent of the era of big data, shallow models such as support vector machines are difficult to effectively explore the hidden information under massive data, and cannot achieve the best performance.

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