| Autor: |
Cristian Arteaga, JeeWoong Park |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Engineering Proceedings, Vol 36, Iss 1, p 31 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2673-4591 |
| DOI: |
10.3390/engproc2023036031 |
| Popis: |
Crash narratives provide valuable information to understand traffic crashes and develop roadway safety countermeasures. However, manually reading long text narratives is time-consuming and error-prone. This study presents a deep-learning and clustering-based approach to identifying contributors to traffic crash severity in text narratives. We evaluate the approach using a dataset of narratives from Massachusetts and compare different deep-learning models for semantic similarity. The approach clusters semantically similar phrases in the narratives and provides an overview of frequent topics related to severe crashes, offering a valuable tool for roadway safety analysis and countermeasure development. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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