Passenger capacity prediction method based on distributed lag model

Abstract

The invention discloses a passenger capacity prediction method based on a distributed lag model. The method comprises the following steps: introducing measurement economics and an input-output model, screening a variable, namely traffic consumption, with the strongest correlation from three variables having a very strong correlation with passenger capacity by using a Pearson correlation coefficient method, starting from the angle of industrial structure adjustment and considering the influence of independent variables in the current year and before the current year, and integrating a measurement economics model method to construct a corresponding distribution lag model, so that the performance of a highway passenger capacity prediction model is more excellent.

Description

technical field [0001] The invention relates to the technical field of econometric data modeling and processing, in particular to a method for predicting passenger traffic volume based on a distributed lag model. Background technique [0002] my country's economy has shifted from a stage of high-speed growth to a stage of high-quality development. As far as the transportation field is concerned, it is of great significance to promote the high-quality development of transportation by following this development trend and discussing how to accurately predict the highway passenger volume. With the continuous improvement of urban activity, the number of motor vehicles in my country is increasing day by day, which will lead to the imbalance between demand and supply of infrastructure. The highway passenger volume is the basic data of the road traffic system. Scientifically and accurately predicting it and using it effectively and rationally is not only conducive to the planning a...

AI Technical Summary

Problems solved by technology

The current forecasting trend of highway passenger traffic is to use machine learning methods to build prediction models, but we can only collect passenger traffic data for several years, and it is obviously inappropriate to use machine learning methods to predict passenger traffic

[0004] The traditional passenger traffic forecasting method does not consider the impact of policies on the investment and development of the transportation system industry

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