Method and system for forecasting traffic flow data based on exponential smoothing

Abstract

The invention discloses a method and system for forecasting traffic flow data based on exponential smoothing. The method comprises the steps of conducting pre-processing, wherein collected traffic data are pre-processed; correcting errors, wherein filtering is conducted on the pre-processed traffic data according to the exponential smoothing method, reverse recursion is conducted on a filtering result, and therefore the errors in the filtering result are eliminated; conducting forecasting, wherein forecasting is conducted on the traffic data without errors through a cubic exponential smoothing forecasting model, and therefore traffic flow forecasting information is obtained. According to the method and system for forecasting the traffic flow data based on exponential smoothing, the traffic data which are collected in real time are pre-processed, so that abnormity in measured data is eliminated; accumulative errors are eliminated through forward and reverse filtering on the pre-processed data, and the accuracy of the filtering result is guaranteed; the traffic data flow is accurately forecasted through the single-step forecasting method, and a forecasting result has certain objectivity.

Description

technical field [0001] The invention relates to the field of traffic flow prediction, in particular to a method and system for predicting traffic flow data based on exponential smoothing. Background technique [0002] Traffic control and guidance system is a hot core issue in the research of intelligent transportation system (ITS), and the key issue in realizing traffic flow guidance system is real-time and accurate traffic flow data prediction, that is, how to effectively use real-time traffic data information (instant sequence flow) Go scroll to predict traffic conditions for the next few minutes. Real-time and accurate traffic flow data can provide travelers with real-time and effective information to help them make better route selection and realize route guidance to shorten travel time and reduce traffic congestion. [0003] Currently, traffic flow forecasting models and methods mainly include Kalman filter, neural network model, and exponential smoothing. However, Ka...

AI Technical Summary

Problems solved by technology

However, Kalman filtering needs to adjust the weights every time it is calculated, which requires a lot of matrix and vector operations, and the algorithm is complex and difficult to use for online prediction

The neural network model has only simple external weight processing capabilities but not complex internal transfer function processing capabilities, so there are deficiencies such as local minima and slow convergence.

There will always be lag errors in the exponential balance method, and the accumulation of errors in multiple calculations will result in inaccurate predictions

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