Travel intention prediction method based on double-attention graph embedded network

Abstract

The invention particularly relates to a travel intention prediction method based on a double-attention graph embedded network. The travel intention prediction method comprises the following steps: establishing a travel intention prediction model; obtaining travel track data of the user and corresponding interest point sign-in data; the method comprises the following steps: firstly, aggregating travel trajectory data and interest point sign-in data through a travel intention prediction model to enhance activity semantics corresponding to a time-space situation, a starting point interest point situation and an end point interest point situation; embedding comprehensive activity semantics of the travel intention extracted in the advanced feature space based on the space-time situation, the starting point interest point situation and the terminal point interest point situation through the double attention graph; finally, the prediction probability of each candidate activity is calculated based on the comprehensive activity semantics of the travel intention; and taking the candidate activity with the highest prediction probability as the predicted travel intention of the user. According to the travel intention prediction method, personal sensitive information of the user does not need to be obtained, so that the privacy and practicability of travel intention prediction can be improved.

Description

technical field [0001] The invention relates to the technical field of Internet big data, in particular to a travel intention prediction method based on a double-attention graph embedded in a network. Background technique [0002] The analysis of users' travel behavior is the basis of smart travel and urban applications, and it is also a long-standing topic in this field, including transportation, urban planning, epidemic control, etc. In the past decade, with the widespread use of GPS trajectory data, many achievements have been made in revealing the spatiotemporal patterns of travel behavior. However, there are relatively few studies on the purpose (intention) of user travel behavior, that is, travel intention. Unlike trajectories that explicitly tell users when and where to move, travel intent is the semantic information that answers why users travel in a city. [0003] Capturing users' travel intention information will greatly facilitate human-centered urban smart serv...

AI Technical Summary

Problems solved by technology

However, the above existing schemes need to obtain the user's flight travel behavior records, which, like most schemes, need to rely on the user's personal sensitive information to complete the prediction

However, on the one hand, obtaining users’ personal sensitive information (such as family structure, home address, employment status, etc.) will lead to serious privacy issues, which should be avoided in smart city applications, so the travel intention prediction privacy of existing schemes Not good; on the other hand, in the actual application process, most applications cannot obtain these sensitive information of the user, resulting in poor practicability of the travel intention prediction of the existing scheme

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