Multi-mode traffic demand influence analysis method based on spatial vector autoregression model

Abstract

The invention provides a multi-mode traffic demand influence analysis method, for different areas of public traffic demands and private cars, based on a spatial vector autoregression model. The methodmainly comprises that (1) a multi-mode traffic demand cooperation model among the areas is established, a traditional spVAR model is improved, a regional POI index is introduced to define the spatialweight for the areas of different traffic modes, and a multi-mode traffic demand spatial VAR model including the regional space structural relation is constructed; and (2) a multi-mode traffic demandcooperation strategy of different areas is provided. Pulse response and variance decomposition results of different traffic modes are solved on the basis of the constructed regional multi-mode traffic spatial VAR model, further, a spatial overflow effect of the traffic demands is obtained by analysis, and the cooperative strategy model for different spatial states and traffic states is provided and constructed. It is proved that the model can improve the availability and scientific performance of traffic efficiency.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic information processing, in particular to a multi-mode traffic demand impact analysis method based on a space vector autoregressive model. Background technique [0002] With the increasing scale of contemporary cities and the improvement of urban motorization level, urban traffic is developing rapidly, and the complexity of the internal structure of the traffic system is gradually increasing. Taking Beijing as an example, as of 2016, the number of private cars reached 5.44 million, the average daily passenger volume of buses reached 13.56 million, and the average daily passenger volume of subways reached 9.998 million. Modular traffic has increasingly become the mainstream of the urban traffic system. [0003] Due to the propagation of traffic flow along the road network and the close connection with the geographical structure, the traffic has certain spatial characteristics, and the n...

AI Technical Summary

Problems solved by technology

However, for regional multi-modal traffic demand, most scholars in the past only conducted isolated research, and traditional models and methods were unable to consider the correlation, time-varying, and complex impact relationships with spatial characteristics between two or more variables.

Images