Intelligent combination forecasting method for short-term traffic flow

Abstract

The invention discloses an intelligent combination forecasting method for short-term traffic flow, which at least comprises the following modules: 1) a historic average module, which divides different dates of one year into three different types, namely a holiday type, a weekend type as well as a Monday and Friday type, and calculates different traffic flows for each type respectively based on statistical data of historic traffic flow; 2) a neural network module, which is a parallel distributed information processing network, has the functions of non-linear mapping and associative memory, and forecasts the traffic flows through the internal relation modeling of the data; and 3) a fuzzy combination module, which aims at different traffic conditions to perform fuzzy combination transformation on the output of the historic average module and the neural network module to forecast the short-term traffic flow. The forecasting precision of the method is higher than the precision that single forecasting methods are independently used respectively, and is an effective method for forecasting the short-term traffic flow.

Description

technical field [0001] The invention relates to a traffic flow forecasting method, in particular to a short-term traffic flow intelligent combination forecasting method. Background technique [0002] In recent years, with the vigorous development of ITS (Intelligent traffic system), intelligent traffic management and control, dynamic traffic state identification and prediction, and real-time traffic flow dynamic induction have become hot topics in ITS research. For these three systems, the information they first need is the short-term traffic flow forecast information from a certain moment kT to the next moment (k+1)T and even several moments later, so accurate real-time short-term traffic flow Prediction is the premise and key to the realization of these three systems, and the quality of the prediction results is directly related to the effect of the implementation of these three systems. It is generally believed that the forecast whose span of the forecast cycle time T do...

AI Technical Summary

Problems solved by technology

However, the change of urban road traffic flow is affected by many natural and social factors, and the mechanism of each factor can not be described by precise mathematical language, which belongs to time-varying, complex nonlinear large-scale system

[0004] At present, a single traffic flow forecasting method has its own unique information characteristics and application conditions. It can only reflect the future situation from different angles, and it is somewhat one-sided. Before forecasting, it often requires a lot of analysis and judgment to choose. best way

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