Spatial Analysis Methods of Road Traffic Collisions

Loo, Becky P.Y.; Anderson, Tessa Kate

doi:10.1201/b18937

Cited by 26 publications

(19 citation statements)

References 7 publications

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“…Previous research indicates that the choice of kernel bandwidth and cell size have higher impact on the results than the choice of kernel function (Loo and Anderson, 2015;Moridpour and Toran, 2015; Khanh, Pei, and Liang-Tay 2019). In Net-KDE method, the cell size and bandwidth are specified based on the characteristics of the case study area (Loo and Anderson, 2015).…”

Section: Spatial Analysis Along Network (Sanet)mentioning

confidence: 99%

Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman

et al. 2021

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Road traffic crashes (RTCs) are a major global public health problem and cause substantial burden on national economy and healthcare. There is little systematic understanding of the geography of RTCs and the spatial correlations of RTCs in the Middle-East region, particularly in Oman where RTCs are the leading cause of disability-adjusted life years lost. The overarching goal of this paper is to evaluate the spatial and temporal dimensions, identifying the high risk areas or hot-zones where RTCs are more frequent using the geocoded data from the Muscat governorate.

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Section: Spatial Analysis Along Network (Sanet)mentioning

confidence: 99%

Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman

et al. 2021

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“…Similar to the differing approaches above, there are different spatial analysis methods used to assess road traffic collisions (17). Quadrat methods could easily be applied to this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of Open Data to Assess Cyclist Safety in London

Collins¹,

Graham

2019

Transportation Research Record: Journal of the Transportation R

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This study develops a predictive model for cycling collisions in London. Specifically, the effects of bus lanes, parking or loading facilities, and multilane roads on the risk of cycling collisions are considered. To the best of the authors’ knowledge, this is the first such predictive collision model that develops covariates to measure the characteristics of different types of road infrastructure within zones. A kernel density estimator is used to identify 90 collision hotspots. Each hotspot is populated with information regarding the highway infrastructure within it. A multiple linear regression model tests for the statistical significance of the infrastructure variables. Bus lanes, multilane roads, and 30-mph speed limits are found to affect cycle collision counts, whereas junction density has the largest impact on collision density. Speed limits of 20 mph affect collision counts to a lesser degree than 30 mph, indicating potential safety improvement from reducing speed limits. One-way roads are found to reduce the risk of collisions, along with the provision of priority junctions. This infers that other junction types, such as roundabouts and signalized junctions, present a higher collision risk. The models produce conflicting results on parking or loading provision. The models are expanded to include sociodemographic variables, such as population and employment. The combined model offers no performance improvement over the infrastructure-only model, although a potential link between public transport provision and reducing cycle collisions warrants further investigation.

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“…The ICD is a general purpose classification of diagnoses and related matters to all health conditions and includes diagnostic codes for both natures of injury and external causes of injury (WHO, 1992). Loo and Anderson (2016) have attempted work on spatial analysis methods of road traffic collisions. The method could be used in the study of rural-urban differences in road traffic collisions and resultant injuries.…”

Section: Concept Of Injurymentioning

confidence: 99%