Method, system and device for travel generation prediction based on gradient boosting decision tree
A forecasting method and decision tree technology, applied in forecasting, data processing applications, instruments, etc., can solve problems such as unintuitive results, large amount of calculation for checking input values and forecasting nonlinear relationship models, and inability to truly reflect travel generation methods. Achieve high precision and robustness, and the effect of simple model parameter verification process
Active Publication Date: 2021-12-28
北京市城市规划设计研究院 +1
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[0006] In order to solve the above-mentioned problems in the prior art, that is, the existing travel generation method cannot truly reflect the nonlinear relationship between the input value and the prediction, and the model checking has a large amount of calculation and the result is not intuitive, the present invention provides a gradient-based A trip generation prediction method for boosting a decision tree that includes:
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[0054] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.
[0055] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.
[0056] The present invention provides a travel generation prediction method based on a gradient lifting decision tree. This method rethinks the use of big data from resident surveys, studies the problem of traffic generation prediction based on a tree structure model, and proposes a ...
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The invention belongs to the field of population trip generation prediction, and specifically relates to a trip generation prediction method, system and device based on a gradient lifting decision tree, aiming to solve the problem that existing trip generation methods cannot truly reflect the nonlinear relationship between input values and predictions Moreover, the model test has a large amount of calculation and the result is not intuitive. The present invention includes: extracting the independent variable of the current trip generation data of each traffic area in the area to be predicted, and performing normalization processing; obtaining the predicted value of each current traffic area in the area to be predicted through the travel generation prediction model; Denormalization is performed to obtain the predicted travel generation data of each traffic area in the area to be predicted. The invention can accurately reflect the nonlinear relationship between the original input and output, and uses the square error principle to find the minimum division feature and division point, automatically ignores redundant variables, saves the manual screening process of variables, and has a high accuracy and robustness.
Description
technical field [0001] The invention belongs to the field of population trip generation prediction, and in particular relates to a trip generation prediction method, system and device based on a gradient lifting decision tree. Background technique [0002] The interactive relationship between urban traffic and urban land use determines that different types and intensities of social activities will result from different land use layout forms and intensities, thus determining the traffic distribution and distribution in different regions. Correspondingly, the functional efficiency of the transportation system also directly affects the surrounding land price, land rent and popularity, and affects whether the surrounding land functions are fully realized. Therefore, in the transportation planning, it is necessary to study the relationship between urban land use and traffic in depth, and the traffic trip rate is one of the important indicators that intuitively reflect this relati...
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IPC IPC(8): G06Q10/04G06Q50/26G06K9/62
CPCG06Q10/04G06Q50/26G06F18/24323G06F18/214
Inventor 杜立群刘斌郑猛张宇吴丹婷吕宜生李志帅
Owner 北京市城市规划设计研究院