Traffic speed prediction method based on traffic flow data fusion and system

Abstract

The invention discloses a traffic speed prediction method based on traffic flow data fusion. The method comprises the following steps of: (1) data preprocessing, namely performing data preprocessing on original traffic data, including historical traffic speed data and traffic flow data; (2) designing a traffic speed prediction model based on traffic flow data fusion, wherein the traffic speed prediction model comprises a flow prediction layer, a data fusion layer, a space-time analysis layer and a prediction output layer; (3) generating a sample data set and training the traffic speed prediction model, specifically splitting the traffic data obtained based on the step (2), generating a training data set, a verification data set and a test data set, and training the traffic speed prediction model; and (4) predicting a traffic speed in a future time period, namely based on the prediction model obtained by training in the step (3), predicting the traffic speed in the future time period according to the collected traffic data. The invention further comprises a system for implementing the traffic speed prediction method based on traffic flow data fusion.

Description

technical field [0001] The invention relates to a traffic speed prediction method and system for intelligent traffic, which can predict the traffic speed in a certain period of time in the future, and the traffic speed prediction method can be used for traffic control, path planning and traffic guidance. Background technique [0002] Traffic speed is one of the important parameters reflecting the state of road traffic. Accurate traffic speed prediction helps to optimize traffic control and improve traffic efficiency. Traffic speed prediction is an integral part of intelligent transportation systems. Usually, traffic managers can input the results of traffic speed prediction into the traffic control system to divide the road network, so as to formulate different signal timing schemes, guide traffic travel, and improve the traffic capacity of the entire road network. [0003] There are three main categories of approaches to the traffic speed prediction problem. The first ty...

AI Technical Summary

Problems solved by technology

Not only that, all neural network methods only use historical traffic speed data to predict future traffic speed, and the original traffic speed data has acquisition errors, they are only the average speed of some vehicles, which affects the traffic speed to a certain extent Accuracy of Velocity Prediction

[0004] At present, the existing traffic speed prediction methods have the following main problems: 1) Most methods use recursive iteration, which takes time to calculate, and does not consider the spatial correlation between each road section, and is not suitable for complex road networks; 2) ) Some methods not only consider the time relationship, but also consider the static spatial relationship, but do not consider the dynamic changes of the spatial relationship; 3) Most of the methods only use the historical traffic speed data to predict the future traffic speed. Considering the acquisition error of the original traffic speed data, which affects the accuracy of traffic speed prediction

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