A Multi-Dimensional Feature Analysis Method for Traffic Delay at Urban Traffic Accident Locations

By constructing a multi-dimensional feature analysis system based on a random parameter Logit model and a machine learning model, the problem of analyzing the degree of driving delay at urban traffic accident sites was solved, influencing factors were identified, traffic management was optimized, and traffic congestion and secondary accidents were reduced.

CN119964359BActive Publication Date: 2026-05-26ZHEJIANG UNIV
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Patent Information

Application Number
CN202411903536.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-23
Publication Date
2026-05-26
Estimated Expiration
2044-12-23

AI Technical Summary

Technical Problem

Existing technologies are insufficient to effectively analyze the degree of travel delay at urban traffic accident sites, especially under multiple influencing factors. The lack of systematic, multi-dimensional feature analysis methods leads to increased risks of traffic congestion and secondary accidents.

Method used

This study employs a random parameter logit model (RPLHM) combined with a machine learning model. Through preprocessing, individual heterogeneity analysis, population transferability analysis, and latent class model, it identifies and predicts the degree of driving delay at urban traffic accident locations. A multi-dimensional feature analysis system is constructed, including a preprocessing module, a random parameter logit model based on mean heterogeneity, and a two-layer model of latent class and machine learning, for data imputation and feature selection.

Benefits of technology

It enables multi-dimensional feature analysis of the degree of driving delay at urban traffic accident sites, identifies significant influencing factors, provides prevention and emergency response measures, optimizes road design and management, and reduces traffic congestion and secondary accidents.

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Abstract

This invention relates to the field of traffic safety analysis technology, providing a multi-dimensional feature analysis system for the degree of driving delay at urban traffic accident sites. Based on driving delay data at urban traffic accident sites, it extracts road-dimensional features at the time of the accident, proposes targeted data imputation schemes based on the characteristics of missing features, and completes redundant feature screening according to statistical distribution, constructing structured traffic modeling data with four dimensions: weather, road, POI, and time. It systematically models and analyzes the degree of driving delay caused by urban traffic accidents from three aspects: individual heterogeneity, group transferability, and group heterogeneity, exploring the influence of significant variables among the multi-dimensional influencing factors. The aim is to help traffic management departments take preventive measures and emergency response strategies, provide a theoretical basis for road design optimization and management decisions, thereby mitigating the impact of traffic accidents, improving the operational risk level of accident-prone road sections, avoiding secondary accidents, and reducing traffic pollution.
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