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Dynamic space network construction method for traffic demand prediction

A space network and traffic demand technology, applied in the field of space network, can solve the problems that the static space network cannot accurately reflect the regional dependence relationship and the resource scheduling efficiency is not high, so as to achieve the effect of optimizing the traffic resource scheduling strategy and improving the prediction results

Active Publication Date: 2021-01-29
成都星宇数云科技有限公司
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Problems solved by technology

[0004] In order to solve the above problems, the object of the present invention is to provide a method for constructing a dynamic spatial network for traffic demand forecasting. The dynamic spatial network constructed by this method can effectively improve the traffic demand forecast results, and at the same time, it solves the problem that the static spatial network cannot accurately reflect the area (or site) dependencies, and the problem of low resource scheduling efficiency in traffic forecasting

Method used

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  • Dynamic space network construction method for traffic demand prediction
  • Dynamic space network construction method for traffic demand prediction
  • Dynamic space network construction method for traffic demand prediction

Examples

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Embodiment 1

[0028] Take the demand for shared bicycles as the actual application scenario of this application: Bluebike is a shared bicycle system, and now it is necessary to predict the demand for bicycles at each site based on this method based on the historical operation records of the system.

[0029] First, count the information of each site in the past 6 hours, and predict the demand for riding out in the next hour. In the Bluebike bicycle system, the object of the demand forecasting task is the site. In order to be unified with the nodes in the network, the stations are referred to as nodes here. After preprocessing the data, 272 nodes are obtained. Constant S τ It is set to 0.7, and it is used as a threshold to judge whether the spatial relationship of the node is stable. When the spatial relationship is stable, the node with a higher interaction frequency with the node is selected; if the spatial relationship is unstable, the distance strategy is adopted, and the distance thres...

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Abstract

The invention provides a dynamic space network construction method for traffic demand prediction, and relates to the field of space networks. The method is characterized in that the method comprises the steps: time discretization: dividing continuous time points into different time periods; node space relationship matching: calculating the stability index S of the node, and adopting different matching strategies according to the stability condition of each node space relationship; dynamic space network modeling: in each time division, carrying out spatial relationship matching on each node, and constructing a dynamic space network, wherein the dynamic space network is further based on a space network, and heterogeneous network modeling is adopted. According to the dynamic space network construction method for traffic demand prediction provided by the invention, the dependency relationship between regions (or stations) can be more accurately reflected, the resource scheduling efficiencyduring traffic prediction is high, and the traffic demand prediction result can be effectively improved.

Description

technical field [0001] The invention relates to the field of space network, in particular to a dynamic space network construction method for traffic demand forecasting. Background technique [0002] In intelligent transportation systems, effective resource scheduling can not only improve resource utilization, but also bring good user experience. Especially in application systems such as Mobike and Didi Taxi, the role of resource scheduling is more obvious. Therefore, in recent years, traffic demand forecasting has been widely concerned by people. [0003] Traffic demand forecasting is different from traffic flow forecasting. The main difference between them is that the spatial network between regions (or stations) in traffic demand forecasting is uncertain; while the spatial network in traffic flow forecasting is fixed. However, in all current methods of traffic demand forecasting, the constructed spatial network is fixed, that is, it does not change with time. The constr...

Claims

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Application Information

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IPC IPC(8): G06F30/18G06Q10/04G06Q10/06G06Q50/30
CPCG06F30/18G06Q10/04G06Q10/06393G06Q50/40Y02T10/40
Inventor 黄飞虎刘华山段剑锋
Owner 成都星宇数云科技有限公司
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