Urban road section traffic volume combination forecasting method

Abstract

On the basis of analysis on the existing research findings of the traffic volume forecasting method, the invention provides a combination forecasting method for urban road section traffic flow characteristics. The method comprises the following steps: 1) actually-measured data of the urban road section traffic volume are gathered, sorted and analyzed, according to a forecasting time interval inter, an actually-measured traffic volume fluctuation index ind(i,j) at each time interval opn each day in history is calculated, and the mean value ind(i) is calculated; 2) according to the actually-measured recent daily traffic volume of the urban road section, a gray forecasting model GM(1,1) is used to forecast the daily traffic volume on a (q+1)th day; and 3) according to actually-measured data of former p time intervals on the (q+1)th day, a time series forecasting method is used to forecast the section traffic volume at a (p+1)th time interval. Based on long-term actually-measured data, theforecasting method makes full use of the urban traffic flow change law and multiple forecasting methods are used; and the forecasting result can reflect the urban road traffic dynamic change trend and can also flexibly respond to short term fluctuation of the traffic flow.

Description

technical field [0001] The invention relates to the field of urban traffic management and control, in particular to a combined prediction method for urban road section traffic volume. Background technique [0002] The management and control of urban traffic needs to analyze the state of road traffic and its changing trend, and formulate targeted management and control measures on this basis to command and guide the orderly operation of traffic flow. At present, the rise of intelligent transportation technology has made the automatic observation technology of urban road traffic volume widely used, which enables the traffic management department to better grasp the operation status of traffic flow and promote the improvement of urban traffic management and control level. However, in view of the complexity of the urban road traffic network and the dynamic and changeable traffic flow, the existing urban traffic forecasting technology has many limitations and cannot well meet the...

AI Technical Summary

Problems solved by technology

However, due to the complexity of the urban traffic system, the change of traffic volume on urban road sections is disturbed by many random factors and has a relatively strong randomness.

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