Expressway traffic flow forecasting method based on time series

Abstract

The invention discloses an expressway traffic flow forecasting method based on time series. The expressway traffic flow forecasting method includes the steps of selecting one time scale, and carrying out statistics to build the traffic flow time series Q=(x); setting the value range of the number p of autoregression items and the number q of moving average items according to the selected time scale; solving the number p of the autoregression items and the number q of the moving average items; fitting the optimal number p of the autoregression items and the optimal number q of the moving average items through the maximum likelihood estimation in cooperation with the traffic flow time series Q to obtain an optimal ARMA model, and obtaining weight parameters of historical measured values and weight parameters of historical error values; solving the traffic flow forecasting series (please see the specifications) under the different time scales. By means of the expressway traffic flow forecasting method, an obtained time series model can better meet the requirement for forecasting various kinds of flow of an expressway, and the forecasting universality is improved; operation is simple, the forecasting efficiency is improved, the forecasting speed is increased, and the engineering requirement of traffic forecasting of the expressway is met.

Description

technical field [0001] The invention relates to the technical field of traffic flow forecasting, in particular to a time-series-based expressway traffic flow forecasting method. Background technique [0002] In recent years, with the development of social economy, the number of motor vehicles has increased rapidly. With the increase of expressway coverage and the improvement of service level in our country, more and more people choose to drive or take a car to travel through the expressway. Under such realistic conditions, the traffic control system is also under greater pressure. As the management basis of the traffic management system, the accuracy of expressway traffic flow forecast becomes particularly important. [0003] At present, the commonly used short-term prediction methods include: Kalman filter, artificial neural network and other methods. These methods are mainly aimed at short-term (generally considered, no more than 15 minutes, or even less than 5 minutes)...

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Problems solved by technology

Moreover, these common short-term prediction methods often have problems such as long computing time and insufficient universality in practical applications, making it difficult to apply them in engineering realization

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