Geostatistical approach to estimate car occupant fatalities in traffic accidents

Despite the scientific and technological advances toward road crash prediction models, modelling road crashes in Brazil is a challenging task due to unreliable data and unavailability of essential information. Geostatistical approaches fit into this context as they not only incorporate the spatial f...

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Main Authors: Monique Martins Gomes, Cira Souza Pitombo, Ali Pirdavani, Tom Brijs
Format: Article
Language:English
Published: Universidade Federal de Uberlândia 2018-12-01
Series:Revista Brasileira de Cartografia
Subjects:
Online Access:http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/46140
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spelling doaj-5749a397b9fd4a7fb40247b5be5225152020-11-25T01:57:05ZengUniversidade Federal de UberlândiaRevista Brasileira de Cartografia0560-46131808-09362018-12-01704Geostatistical approach to estimate car occupant fatalities in traffic accidentsMonique Martins GomesCira Souza PitomboAli PirdavaniTom BrijsDespite the scientific and technological advances toward road crash prediction models, modelling road crashes in Brazil is a challenging task due to unreliable data and unavailability of essential information. Geostatistical approaches fit into this context as they not only incorporate the spatial factor, but also estimate variables in locations where they are not sampled. In order to contribute to this investigation and present geostatistics as a suitable tool in estimating road deaths, this study aimed to explore two different univariate interpolation methods to predict car occupant fatalities. In this study, we considered ordinary kriging and indicator kriging as such approaches. Analyses were held based on data from the state of São Paulo in Brazil. The results revealed a statistical outperformance in favor of indicator kriging, although spatial patterns found on kriging maps for both techniques indicated similarities in terms of hotspots. Furthermore, they were coherent with local aspects observed in the state, for instance those related to highway and vehicle characteristics.http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/46140GeostatisticsCrash Prediction ModelsKrigingRoad crashes
collection DOAJ
language English
format Article
sources DOAJ
author Monique Martins Gomes
Cira Souza Pitombo
Ali Pirdavani
Tom Brijs
spellingShingle Monique Martins Gomes
Cira Souza Pitombo
Ali Pirdavani
Tom Brijs
Geostatistical approach to estimate car occupant fatalities in traffic accidents
Revista Brasileira de Cartografia
Geostatistics
Crash Prediction Models
Kriging
Road crashes
author_facet Monique Martins Gomes
Cira Souza Pitombo
Ali Pirdavani
Tom Brijs
author_sort Monique Martins Gomes
title Geostatistical approach to estimate car occupant fatalities in traffic accidents
title_short Geostatistical approach to estimate car occupant fatalities in traffic accidents
title_full Geostatistical approach to estimate car occupant fatalities in traffic accidents
title_fullStr Geostatistical approach to estimate car occupant fatalities in traffic accidents
title_full_unstemmed Geostatistical approach to estimate car occupant fatalities in traffic accidents
title_sort geostatistical approach to estimate car occupant fatalities in traffic accidents
publisher Universidade Federal de Uberlândia
series Revista Brasileira de Cartografia
issn 0560-4613
1808-0936
publishDate 2018-12-01
description Despite the scientific and technological advances toward road crash prediction models, modelling road crashes in Brazil is a challenging task due to unreliable data and unavailability of essential information. Geostatistical approaches fit into this context as they not only incorporate the spatial factor, but also estimate variables in locations where they are not sampled. In order to contribute to this investigation and present geostatistics as a suitable tool in estimating road deaths, this study aimed to explore two different univariate interpolation methods to predict car occupant fatalities. In this study, we considered ordinary kriging and indicator kriging as such approaches. Analyses were held based on data from the state of São Paulo in Brazil. The results revealed a statistical outperformance in favor of indicator kriging, although spatial patterns found on kriging maps for both techniques indicated similarities in terms of hotspots. Furthermore, they were coherent with local aspects observed in the state, for instance those related to highway and vehicle characteristics.
topic Geostatistics
Crash Prediction Models
Kriging
Road crashes
url http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/46140
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AT alipirdavani geostatisticalapproachtoestimatecaroccupantfatalitiesintrafficaccidents
AT tombrijs geostatisticalapproachtoestimatecaroccupantfatalitiesintrafficaccidents
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