Fractal traffic flow prediction method combining weekly similarity

Abstract

The invention discloses a method for predicting fractal traffic flow combining weekly similarity characteristic. The invention comprises the following steps: 1) traffic flow data of different working days takes one week as a period, the traffic flow data are grouped to form traffic flow sequences with different directions at the same intersection in a scheduled period of time; 2) the scheduled time before the current time is extracted to the traffic flow sequences {Ni} of the current time, initialized n is equal to one, and {Si} is obtained through performing n-order cumulative calculation, {Sni}(i=1, ..., n)=N(A, epsilon) I, the obtained value is N(A, epsilon) i+1; 3) according to the traffic flow sequences in the same period of time a week ago, the traffic flow sequences in the same period of time two weeks ago, the traffic flow sequences in the same period of time three weeks ago, ... the traffic flow sequences in the same period of time m weeks ago, the calculations of the step 2) are respectively performed to obtain each predicted data, and the predicted data undergoes error correction to obtain the predicted result data. The invention provides a method for predicting fractal traffic flow combining weekly similarity characteristic with good real-time and high prediction precision.

Description

technical field [0001] The invention relates to an intelligent traffic system, in particular to a traffic flow prediction method. Background technique [0002] The models widely used in short-term traffic flow forecasting include: econometric model, neural network model, dynamic traffic allocation model and nonlinear system theory model. The traffic system is a time-varying complex system with human participation, and the traditional quantitative equivalent model (mathematical statistical model) is no longer suitable for the prediction accuracy of short-term traffic flow. The artificial neural network has good adaptability to the prediction of the nonlinear system, but the use of the neural network requires a large number of samples to train the model, and the generalization ability is poor. The main purpose of the dynamic traffic allocation model is to distribute the traffic flow on the road network rationally. The prediction in the model is poor in real-time and the accur...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the disadvantages of poor real-time performance and low prediction accuracy of existing traffic flow prediction methods, the present inv

Images