Assessing accident hotspots by using volunteered geographic information

Golnoosh Farajollahi () and Mahmoud Reza Delavar
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Golnoosh Farajollahi: MSc. Student in GIS Division, School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran, Iran.
Mahmoud Reza Delavar: Center of Excellence in Geomatic Eng. In Disaster Management, School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran, Iran.

Journal Clean WAS (JCleanWAS), 2017, vol. 1, issue 2, 14-17

Abstract: Due to the ever-increasing number of vehicles, transportation issues, especially transportation safety have gained great importance. One of the social problems in the world, and particularly in developing countries, which each year imposes great casualties, and economic, social and cultural costs on society, is traffic accidents. Traffic accidents cause waste of time and assets and loss of human resources in society, therefore studies and measures to reduce accidents and damage caused by them, particularly in recent decades, has become important. One of the suggested ways to deal with the problem of car accidents is the modeling of accident-prone points, as by identifying these points, factors affecting accidents can be identified, and elimination of these factors leads to a reduction in accidents. Numerous studies have been conducted in this respect, using official police data to identify these points and performing necessary analysis on them. Official data has gaps and shortcomings. Using Volunteered Geographic Information to determine accident-prone venues can be a suitable answer to the problems of using official data. The aim of this study is the use of volunteered geographic information in relation to the accidents and their causes. By taking into account factors affecting traffic accidents in the study area, and determining the importance of each factor, as well as the severity-of-accidents parameter, and using the Expert Choice software, a decision-making software based on the hierarchical analysis, high-risk venues are determined, and the accident-prone points of the study area are specified.

Keywords: Traffic accidents; volunteered geographic information; hierarchical analysis; accident-prone points; multi-criteria decision making (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:zib:jclnws:v:1:y:2017:i:2:p:14-17

DOI: 10.26480/jcleanwas.02.2017.14.17

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