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A Multi-modal Traffic Demand Impact Analysis Method Based on Space Vector Autoregressive Model

An autoregressive model, a technology of traffic demand, applied in the direction of road vehicle traffic control system, traffic flow detection, traffic control system, etc., can solve the problem of inability to consider correlation, time-varying spatial characteristics, etc., and achieve the effect of quantifying the spatial spillover effect

Active Publication Date: 2019-07-26
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 were unable to consider the correlation, time-varying, and complex impact relationships with spatial characteristics between two or more variables.

Method used

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  • A Multi-modal Traffic Demand Impact Analysis Method Based on Space Vector Autoregressive Model
  • A Multi-modal Traffic Demand Impact Analysis Method Based on Space Vector Autoregressive Model
  • A Multi-modal Traffic Demand Impact Analysis Method Based on Space Vector Autoregressive Model

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Experimental program
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Embodiment

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

[0089] 1), if Figure 4 As shown in the figure, for the two traffic communities near Xidan and Fuxingmen in Beijing, the number of subway stops, the number of buses boarding, and the road network congestion index of the selected traffic communities are calculated respectively. 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), the stationarity test of the variables. The demand for three modes of transportation in two communities—the number of subway stops, the number of bus rides, and the private car congestion index—a total of six variables are used as the input variables of the model. The unit root test is performed on the six variables of the original data to quantitatively analyze the variable stationari...

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Abstract

The invention provides a method for analyzing the impact of public transportation demand and private car multi-mode transportation demand between different areas based on a space vector autoregressive model. The invention mainly includes the following contents: (1) Constructing an inter-regional multi-mode traffic demand collaboration model. Based on the traditional spVAR model, improvements were made, regional POI indicators were introduced to define inter-regional spatial weights for different traffic modes, and a multi-modal traffic demand spatial VAR model including regional spatial structural relationships was constructed. (2) Propose an inter-regional multi-modal traffic demand collaboration strategy. Based on the constructed regional multi-modal traffic space VAR model, the impulse response and variance decomposition results of each traffic mode are obtained, and the spatial spillover effect of traffic demand is further analyzed based on this, and collaborative strategies for different spatial states and traffic states are proposed and constructed. Model. The above model has proven to be usable and scientific in improving 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 autoregression model. Background technique [0002] With the increasing scale of contemporary cities and the improvement of the level of urban motorization, urban transportation has developed rapidly, and the internal structure of the transportation system has gradually increased in complexity. Taking Beijing as an example, as of 2016, the number of private cars reached 5.44 million, the average daily passenger volume of public transport reached 13.56 million, and the average daily passenger flow of subway reached 9.998 million. Modal transportation has increasingly become the mainstream of urban transportation system. [0003] Because the traffic flow spreads along the road network and is closely related to the geographical structure, the traffic has certain spatial cha...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06Q10/06G06Q50/30
CPCG06Q10/06315G08G1/0129G06Q50/40
Inventor 马晓磊张宪杜博文于海洋丁川
Owner BEIHANG UNIV
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