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Export Reference (APA) 
Ilharco, A., Bastos, A., Elvas, J_P & de Almeida, A. (2013). Generation of road accident risk maps. CITTA 6th Annual Conference on Planning Research.
Export Reference (IEEE) 
A. Ilharco et al.,  "Generation of road accident risk maps", in CITTA 6th Annu. Conf. on Planning Research, Coimbra, 2013
Export BibTeX 
@misc{ilharco2013_1716204078453,
	author = "Ilharco, A. and Bastos, A. and Elvas, J_P and de Almeida, A.",
	title = "Generation of road accident risk maps",
	year = "2013",
	howpublished = "Both (printed and digital)",
	url = ""
}
Export RIS 
TY  - CPAPER
TI  - Generation of road accident risk maps
T2  - CITTA 6th Annual Conference on Planning Research
AU  - Ilharco, A.
AU  - Bastos, A.
AU  - Elvas, J_P
AU  - de Almeida, A.
PY  - 2013
CY  - Coimbra
AB  - Knowing the factors that affect the likelihood of an accident occurring has been increasingly challenging to the researchers given the huge social and financial costs that derive from road accidents.
In Portugal, developments in this area have mainly involved interurban roads studies. However, according to ANSRi, about 70% of Portuguese road accidents occur in urban spaces, a trend common to most European countries. The lack of national or local information systems containing geo-referenced road accidents, and geometric characteristics of roads, among others, hamper the creation of tools that help to assess the risk of exposure at a micro level, i.e. road intersections.
The weaknesses mentioned above led us towards the study of the implementation of models in a GIS-based environment in order to estimate the frequency of accidents for urban areas according to several breakdowns: road element, type of accident and the inclusion of explanatory variables related to road environment. One of the major challenges faced by researchers when applying these models is the absence of data or its poor quality. Therefore, it is necessary to cross and analyse information from different sources, such as traffic variables (from model transportation planning), digital cartographic data, and other geometric variables, that may not be obtained directly (e.g. using OpenStreetMap or Google Maps).
This paper proposes a methodology for automatic generation of road accident maps quantifying the risk of exposure. These maps will serve as a decision support tool not only to insurers (taxing drivers more effectively according to their exposure to risk), but also to drivers themselves (through the generation of alarms allowing them to tailor their driving performance), envisaging road safety improvement.
ER  -