Citation

Pusuluri VL, Dangeti MR, Kotamrazu M. Innov. Infrastruct. Solut. 2023; 8(5): e146.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s41062-023-01111-y

PMID

unavailable

Abstract

According to the World Bank Report 2020, there is one fatal road crash every four minutes globally. Loss of lives and limbs seriously hampers the livelihood of the victims and their families. Despite cities' efforts to improve roads, crashes continue to occur worldwide. Road crashes are not accidents but preventable by systematic efforts in crash spot identification, prediction, and remedial actions. Road crashes occur due to various factors ranging between vehicle, weather, driver, or environmental categories. There is a need to establish methods of analysis that cities can quickly adopt to save lives. Traditionally statistical methods are used for crash zone identification, but recently, Geographic Information Systems (GIS) helped identify crash zones graphically while validating the results statistically. This review concerns road crash zone (Blackspot) identification GIS methodologies developed and adopted by cities between 2011 and 2022. It is found that GIS as a mapping tool has expanded into a data management system. It has high adaptability to integrate newer combinations with machine learning and fuzzy logic. The review finds that Kernel Density Estimation and Weighted Severity Index could be easy methods to adopt using local crash data, geocoding, and open-source software. It aims to assist cities with multi-modal traffic and two-wheeler dominance in future forms of research. The study is limited to transportation planning factors and GIS methodologies.

Language: en

Keywords

Blackspot identification; Geocoding; GIS; Kernel density estimation; SDG 3.6; Severity index