Large-scale road network inter-region hot spot path identification method

By dividing the large-scale road network into regions and using k-gram and locality-sensitive hashing algorithms to process license plate recognition data, hotspot paths between regions are identified, solving the problems of high computational overhead and insufficient timeliness in existing technologies, and realizing efficient hotspot path identification and traffic management support.

CN120954240AActive Publication Date: 2025-11-14ZHEJIANG SUPCON INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511493490.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-20
Publication Date
2025-11-14
Estimated Expiration
2045-10-20

AI Technical Summary

Technical Problem

Existing technologies incur huge computational costs when identifying hotspot paths in large-scale road networks, making it difficult to meet timeliness requirements. Furthermore, the algorithms lack adaptability and robustness, resulting in inaccurate identification results.

Method used

By dividing the target road network into multiple regions, filtering cross-regional single-vehicle origin-destination (OD) trip data, and using k-gram feature extraction and locality-sensitive hashing (LSH) algorithms for data simplification and similarity calculation, combined with cluster analysis, representative paths are obtained and hotspot paths between regions are identified.

Benefits of technology

It significantly reduces the complexity of data processing, enables efficient hotspot path identification under massive license plate recognition data, and supports real-time traffic status analysis and decision-making for city-level road networks.

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Abstract

The invention relates to the technical field of intelligent traffic, in particular to a large-scale road network inter-region hot spot path identification method, which comprises the following steps: dividing a target road network into a plurality of regions, obtaining license plate identification data of a period on the target road network, extracting a single vehicle OD travel according to the license plate identification data, and screening out a cross-region single vehicle OD travel as to-be-analyzed data; grouping the to-be-analyzed data, wherein each group of data comprises a cross-regional single-vehicle OD travel in the same starting and ending region and in the same preset time interval; performing simplification and similarity calculation on each group of data to obtain a representative path of the data to be analyzed; aggregating the representative paths of the to-be-analyzed data to obtain a representative path list; and according to the representative path list, obtaining an occurrence frequency of each representative path, and according to the occurrence frequency, determining an inter-region hotspot path in the target road network. According to the method and the device, the hot spot paths among the regions can be efficiently, correctly and intelligently mined.
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