Pedestrian safety at signalized intersections: Spatial and machine learning approaches
The major goal of the present research is to determine hotspot areas by the generation of a geospatial model and develop a model associated with pedestrian-vehicle crash injuries (severe, moderate, slight) at signalized intersections in Erzurum, Turkey. This study used the comprehensive algorithm in Artificial Neural Network (ANN). Data from 197 crashes injury (2015–2019) at 57 intersections depending on the mix of variables such as driver, road and vehicle characteristics, and environment data were collected. Within the four candidate models, the first one including pedestrian density, level of education, traffic congestion, type of vehicle, presence of bus stop, age, and gender had the lowest RMSE and MAE values and the greatest R2 value. Lastly, sensitivity analyses were conducted to evaluate the impact of independent parameters. The importance of the study lies in the expected outcomes to assist the experts to address the pedestrian-vehicle crash risk factors by conducting appropriate countermeasures for facilities management/improvement.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/22141405
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Supplemental Notes:
- © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Kuşkapan, Emre
- Sahraei, Mohammad Ali
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0000-0002-9130-3685
- Çodur, Merve Kayaci
- Çodur, Muhammed Yasin
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 101322
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Serial:
- Journal of Transport & Health
- Volume: 24
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2214-1405
- Serial URL: http://www.sciencedirect.com/science/journal/22141405
Subject/Index Terms
- TRT Terms: Crash injuries; High risk locations; Machine learning; Pedestrian safety; Signalized intersections
- Geographic Terms: Erzurum (Turkey)
- Subject Areas: Highways; Pedestrians and Bicyclists; Safety and Human Factors;
Filing Info
- Accession Number: 01834273
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 25 2022 5:29PM