Applications of QGIS and machine learning for road crash spot identification-Reference-Cited by-同舟云学术

Applications of QGIS and machine learning for road crash spot identification

Published:2024-03-26 Issue:3 Volume:17 Page:2331-2346
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Pusuluri Vijaya Lakshmikanthi^ORCID,Dangeti Mukund Rao

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12145-024-01271-0.pdf

Reference28 articles.

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2. Arif M, Sengupta S, Mohinuddin SK, Gupta K (2023) Dynamics of land use and land cover change in peri urban area of Burdwan city, India: a remote sensing and GIS based approach. GeoJournal 88(4):4189–4213. https://doi.org/10.1007/s10708-023-10860-3

3. Bisht LS, Tiwari G (2020) Assessing the black spots focused policies for Indian national highways. Transp Res Procedia 48:2537–2549. https://doi.org/10.1016/j.trpro.2020.08.256

4. Chen Y, Wu H, Liu C, Sun W (2011) Identification of black spot on traffic accidents and its spatial association analysis based on geographic information system. In: 2011 Seventh international conference on natural computation, vol 1. IEEE, pp 143–150. https://doi.org/10.1109/ICNC.2011.6021904

5. Deccan Chronicle, DC Correspondent (2022). Road accidents increase in AP by 20%. Retrieved on May 23, 2023. https://www.deccanchronicle.com/nation/in-other-news/010922/roadaccidents-increase-in-ap-by-20.html

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