Spatial statistics and random forest approaches for traffic crash hot spot identification and prediction
Crash hot spot identification and prediction using spatial statistics and random forest methods on the interstate of Michigan are evaluated. The Getis-Ord statistics are adopted to identify hot spots using location, frequency, and equivalent property damage only weights computed from the cost and severity of crashes. In the random forest approach, data patterns between 2010 and 2017 are determined to predict hot spots of crashes in 2018. Accordingly, the results indicate that: (i) interstate routes have witnessed 13,089 crashes on significant hot spots, 7,413 on cold spots, and the rest in other locations; (ii) random forest shows 76.7% and 74% accuracy for validation and prediction, respectively. The performance of the model is further affirmed with precision, recall, and F-scores of 75%, 74%, and 70%, respectively; and (iii) clustering of the crashes exhibits spatial dependence of high and low equivalent property damage only crashes. The practical significance of the approach is highlighted in the identification and prediction of hot spots.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/17457300
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Supplemental Notes:
- © 2021 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Atumo, Eskindir Ayele
- Fang, Tuo
- Jiang, Xinguo
- Publication Date: 2022-5
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 207-216
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Serial:
- International Journal of Injury Control and Safety Promotion
- Volume: 29
- Issue Number: 2
- Publisher: Taylor & Francis
- ISSN: 1745-7300
- EISSN: 1745-7319
- Serial URL: http://www.tandfonline.com/loi/nics20
Subject/Index Terms
- TRT Terms: Decision trees; High risk locations; Interstate highways; Predictive models
- Geographic Terms: Michigan
- Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01852623
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 21 2022 5:08PM