Traffic mode behavior recognition method and corresponding recognition model construction method

Abstract

The invention provides a traffic mode behavior recognition model construction method. The method comprises the steps of 1) conducting the multi-level classification on specific traffic mode behaviors and establishing a corresponding classification tree; 2) for the classification problem of each parent node on the classification tree, selecting an optimal feature set for a test sample corresponding to the classification problem based on a random forest model according to the identification accuracy; 3) for the classification problem of each parent node on the classification tree, based on the corresponding optimal feature set obtained in the step 2), selecting one optimal classification model as a sub-classification model corresponding to the parent node. The invention also provides a corresponding traffic mode behavior recognition method. Based on the method, specific traffic mode behaviors can be distinguished more accurately. Meanwhile, the computational complexity is reduced.

Description

technical field [0001] The present invention relates to the technical fields of pervasive computing, mobile Internet and urban planning, and more specifically, the present invention relates to a traffic pattern behavior recognition method and a corresponding recognition model construction method. Background technique [0002] Using sensory data to identify users' daily behavior is an important research problem in the field of ubiquitous computing. As a subcategory of many daily behaviors, traffic mode behavior (walking, biking, bus ride, etc.) contains a lot of information related to the user's movement trajectory and life rules. Accurately and effectively capturing users' daily traffic patterns and behaviors, and then analyzing user behavior habits and daily life trajectories, plays an important role in many fields such as smart mobile services, health monitoring, and urban planning. For example, using traffic pattern behavior recognition technology, users' daily trajector...

AI Technical Summary

Problems solved by technology

However, there are many types of traffic mode behaviors, the similarity between different traffic mode behaviors is high, and the internal differences of the same traffic mode behavior are large. These factors lead to the unsatisfactory recognition accuracy of the current traffic recognition schemes. The more difficult it is to meet the requirements of practical applications

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