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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
30.04.2024
DOI
https://doi.org/10.7307/ptt.v36i2.366
ISSUE
2024 (Vol 36), Issue 2
LICENSE
Copyright (c) 2024 Jie Li, Yuntao Shi, Shuqin Li
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Analysis of Beijing Traffic Violations Based on the BERT-CRF Model

Authors:Jie Li, Yuntao Shi, Shuqin Li

Abstract

Traffic violations are a major cause of traffic accidents, yet current research falls short in comprehensively analysing these violations and the  named entity method fails to extract the name of traffic violation events from records, thereby lacking in providing guidance for managing urban traffic violations. By expanding the People’s Daily dataset from 71,456 words to 95,291 words, the BERT-CRF (Bidirectional Encoder Representations from Transformers-Conditional Random Field) model achieves an accuracy rate of 88.53%, a recall rate of 92.90% and an F1 score of 90.66%, successfully identifying event, time and location named entities within traffic violations. The data of traffic violations is then enhanced through forward geocoding and the Bayesian formula, and traffic violations are analysed from time, space, administrative region, gender and weather, to provide support for the dynamic allocation of law enforcement forces on traffic scenes and the precise management of
traffic violations.

Keywords:traffic violation, traffic accident, name entity, People's Daily, BERT-CRF, Bayesian formula

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