Methods for predicting travel destinations, and methods for training classifiers
A destination and classifier technology, applied in the field of training classifiers, can solve problems such as unsatisfactory prediction results, limited trajectory data, and failure to consider the time correlation of travel trajectories
Active Publication Date: 2021-05-04
北京京东智能城市大数据研究院
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Problems solved by technology
[0003] In the process of realizing the concept of the present disclosure, the inventor found that there are at least the following problems in the prior art: on the one hand, when predicting the travel purpose based on the similarity of the trajectory, the temporal correlation of the individual travel trajectory is not considered. The prediction effect is not ideal; on the other hand, due to the limited trajectory data of a single user, there is a general sparsity problem in the data of a single user during model training
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[0037] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. It should be understood, however, that these descriptions are exemplary only, and are not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present disclosure.
[0038] The terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting of the present disclosure. The terms "comprising", "comprising", etc. used herein indicate the presence of stated features, ...
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The present disclosure provides a method, apparatus, system and medium for predicting a travel destination. The method includes: acquiring the passenger characteristics of the current trip of the user, acquiring the site characteristics of the starting station of the current trip, and acquiring the site characteristics of each terminal station in the multiple terminal stations that may appear in the current trip, and for each terminal station, construct an input data based on at least the station characteristics of each terminal station, the passenger characteristics of the current trip, and the station characteristics of the starting station; wherein, for the plurality of The terminal station is correspondingly constructed to obtain a plurality of input data; input the plurality of input data to a classifier, and obtain a predicted probability output by the classifier for each of the plurality of input data; and based on the The predicted probability is used to determine the destination of the current trip. The present disclosure also provides a method, apparatus, system and medium for training a classifier.
Description
technical field [0001] The present disclosure relates to the technical field of the Internet, and more specifically, to a method, device, system and medium for predicting a travel destination, and a method, device, system and medium for training a classifier. Background technique [0002] During the daily commute rush hours or holidays in the city, there are many people traveling, and the traffic operation pressure is relatively high. If it is possible to predict the travel classification of the flow of people in advance, and deploy traffic in advance according to the travel rules (for example, increase the frequency of subway operation, bus operation frequency, and guide taxis to increase the number of vehicles in places where the pressure on the number of travel is predicted to be high, etc.), To a certain extent, it can alleviate the problem of traffic congestion and improve the city's public service capabilities. In the prior art, the travel destination of each user can...
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IPC IPC(8): G06K9/62G06Q10/04G06Q50/30
CPCG06Q10/04G06Q50/30G06F18/24G06F18/214
Inventor 尹泽夏王新左何源张钧波郑宇
Owner 北京京东智能城市大数据研究院