Path driving rhythm pattern analysis method and system based on DNA sequence characterization

By constructing an OD cost matrix and analyzing taxi trajectory data using the Smith-Waterman algorithm, this study addresses the problem that existing path planning algorithms fail to accurately reflect actual road conditions. It also enables the mining of path selection patterns and sentiment tendencies, thereby improving the accuracy and personalization of path planning.

CN115687949BActive Publication Date: 2026-05-22CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Application Number
CN202211336225.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-10-28
Publication Date
2026-05-22
Estimated Expiration
2042-10-28

AI Technical Summary

Technical Problem

Existing path planning algorithms fail to accurately reflect the optimal path under actual conditions when considering road travel costs, especially when affected by complex factors such as traffic volume, lane distribution, road surface conditions, road traffic rules, real-time congestion, and weather conditions.

Method used

By acquiring taxi trajectory data, an OD cost matrix is ​​constructed. Combining the distance cost and transit time between trajectory points, a driving rhythm sequence is constructed. The Smith-Waterman algorithm is used for similarity comparison, and cluster analysis is performed to identify taxi drivers' route selection patterns and sentiment tendencies.

Benefits of technology

By identifying excellent path selection patterns and typical path selection sentiments, we can help improve and personalize path planning algorithms, enhance the travel experience, and provide a reference for urban transportation planning.

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Abstract

The application provides a path driving rhythm mode analysis method and system based on DNA sequence characterization, comprising the following steps: S1, preprocessing an original taxi trajectory data set to obtain a preprocessed taxi trip trajectory flow data set; S2, constructing an OD cost matrix to obtain the distance cost of each trajectory point in each trajectory flow in the road network; S3, constructing a driving rhythm sequence of the trajectory according to the distance cost of the trajectory in the road network in combination with the passing time and average speed between the trajectory points; S4, calculating the similarity between the driving rhythm sequences in each scene; and S5, taking the similarity as a score to perform clustering analysis on the driving rhythm sequences in each scene to obtain the mode and emotional trend of the taxi drivers for path selection in each scene. The application can mine excellent path selection modes and typical path selection emotional trends, and is helpful for the improvement and personalized customization of path planning algorithms.
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