Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity Contributors
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 t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/36/1/31 |
| _version_ | 1827306370803695616 |
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| author | Cristian Arteaga JeeWoong Park |
| author_facet | Cristian Arteaga JeeWoong Park |
| author_sort | Cristian Arteaga |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:20:26Z |
| format | Article |
| id | doaj.art-92f248b9740b47e9860f67f6be2c74ff |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-04-24T18:20:26Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-92f248b9740b47e9860f67f6be2c74ff2024-03-27T13:36:29ZengMDPI AGEngineering Proceedings2673-45912023-07-013613110.3390/engproc2023036031Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity ContributorsCristian Arteaga0JeeWoong Park1Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USADepartment of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USACrash 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.https://www.mdpi.com/2673-4591/36/1/31crash narrativesclusteringdeep learningsemantic similarityseverity contributors |
| spellingShingle | Cristian Arteaga JeeWoong Park Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity Contributors Engineering Proceedings crash narratives clustering deep learning semantic similarity severity contributors |
| title | Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity Contributors |
| title_full | Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity Contributors |
| title_fullStr | Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity Contributors |
| title_full_unstemmed | Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity Contributors |
| title_short | Deep Learning and Clustering-Based Analysis of Text Narratives for Identification of Traffic Crash Severity Contributors |
| title_sort | deep learning and clustering based analysis of text narratives for identification of traffic crash severity contributors |
| topic | crash narratives clustering deep learning semantic similarity severity contributors |
| url | https://www.mdpi.com/2673-4591/36/1/31 |
| work_keys_str_mv | AT cristianarteaga deeplearningandclusteringbasedanalysisoftextnarrativesforidentificationoftrafficcrashseveritycontributors AT jeewoongpark deeplearningandclusteringbasedanalysisoftextnarrativesforidentificationoftrafficcrashseveritycontributors |