Regional highway main channel traffic demand forecasting method based on multiple-factor regression

Abstract

The invention discloses a regional highway main channel traffic demand forecasting method based on multiple-factor regression. The method comprises the following steps: determining influencing factors of regional highway main channel traffic demand forecasting; determining at least one selectable path which is same with the starting point and end point of a main channel path; constructing a multiple-factor regression model according to the determined influencing factors and the selectable path; and forecasting the traffic demand y1 of the main channel by adopting the established regression model. The method improves an existing regional highway main channel traffic demand forecasting method which carries out regression forecasting only on a single path without considering the influence of other adjacent paths, the trends of which are roughly same with the trend of the path; the traffic demand forecasting of some path in the channel is carried out by utilizing the multiple-factor united regression; and the method improves existing demand forecasting precision and can provide more accurate demand forecasting result for the follow-up jobs in engineering practice.

Description

technical field [0001] The invention belongs to the technical field of demand forecasting, relates to the influence of other adjacent paths in the main channel of a regional highway that are roughly the same as the direction of the predicted path, and is a high-precision traffic demand forecasting method for the main channel of a regional highway. Background technique [0002] When forecasting the traffic demand in the main channel of regional traffic, one-way separate forecasting or four-stage forecasting method based on OD survey data is usually used. The one-way forecasting of traffic volume separately is to obtain the predicted value of the future traffic volume of the line after quantitatively predicting the basic traffic volume, transfer traffic volume and induced traffic volume in the prospective year on the road to be planned and studied respectively. The prediction does not take into account the interaction of multiple paths within the main channel. The four-stage ...

AI Technical Summary

Problems solved by technology

The four-stage forecasting method is a forecasting method developed in the process of studying urban traffic planning. It has been widely used in urban traffic planning, but there is a big difference between urban traffic and regional traffic main channel traffic: urban traffic The traffic flow is complex and divergent, and the traffic flow in the channel has a strong directionality; there are many lines from O to D in the urban traffic system, and travelers have relatively free choice, while in the channel from O to D The lines are generally fixed and the choices are limited

[0003] In the current research, after fully considering the influencing factors of traffic demand, some studies aim at the correlation between traffic demand and social activities, economic development and other influencing factors, predict the total traffic demand of the channel according to the correlation coefficient, and then adopt the feasible path The traffic flow of the feasible path is obtained by the allocation of attraction rights; the other part of the research establishes a game model and a fuzzy comprehensive evaluation model through the analysis of the competitiveness of each path in the channel, but these algorithms are complicated to operate and have relatively large subjective factors

In view of the current situation that when performing linear regression prediction of traffic demand on a route in the main channel of regional highways, usually only a separate analysis is carried out for this route, without directly considering the influence of other routes with roughly the same direction on this route. Invented and proposed a method for forecasting traffic demand on main corridors of regional highways based on multi-factor regression

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