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

An autoregressive model and traffic demand technology, which is applied in the traffic control system of road vehicles, traffic control system, traffic flow detection, etc., can solve problems such as correlation and time-varying space characteristics that cannot be considered, and achieve the effect of quantifying the spatial spillover effect

Active Publication Date: 2018-05-01
BEIHANG UNIV
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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 w

Method used

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

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Embodiment

[0088] A multi-mode traffic demand impact analysis method based on a spatial autoregressive model, specifically as follows:

[0089] 1), such as Figure 4 As shown, there are two traffic districts near Xidan and Fuxingmen in Beijing. Statistics are made on the number of subway entrances, bus boarding volumes, and road network congestion indexes in the selected traffic districts. The results are shown as follows:

[0090] Table taz1 community subway, bus, road network data

[0091]

[0092] Table taz2 community subway, bus, road network data

[0093]

[0094]

[0095] 2) Stationary test of variables. The demand of the three modes of transportation in the two districts - the number of subway entrances, the number of bus boarding, and the congestion index of private cars, a total of six variables are used as the input variables of the model The unit root test was carried out on the six variables of the original data to quantitatively analyze the stability of the vari...

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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...

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Application Information

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IPC IPC(8): G08G1/01G06Q10/06G06Q50/30
CPCG06Q10/06315G06Q50/30G08G1/0129
Inventor 马晓磊张宪杜博文于海洋丁川
Owner BEIHANG UNIV
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