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Garrido, R., Bastos, A., de Almeida, A. & Elvas, J. P. (2014). Prediction of road accident severity using the ordered probit model. In Transportation Research Procedia. (pp. 214-223).: Elsevier.

R. F. Garrido et al.,  "Prediction of road accident severity using the ordered probit model", in Transportation Research Procedia, Elsevier, 2014, vol. 3, pp. 214-223

@inproceedings{garrido2014_1770493184296,
	author = "Garrido, R. and Bastos, A. and de Almeida, A. and Elvas, J. P.",
	title = "Prediction of road accident severity using the ordered probit model",
	booktitle = "Transportation Research Procedia",
	year = "2014",
	editor = "",
	volume = "3",
	number = "",
	series = "",
	doi = "10.1016/j.trpro.2014.10.107",
	pages = "214-223",
	publisher = "Elsevier",
	address = "",
	organization = "",
	url = "http://www.sciencedirect.com/science/article/pii/S2352146514002701"
}

TY  - CPAPER
TI  - Prediction of road accident severity using the ordered probit model
T2  - Transportation Research Procedia
VL  - 3
AU  - Garrido, R.
AU  - Bastos, A.
AU  - de Almeida, A.
AU  - Elvas, J. P.
PY  - 2014
SP  - 214-223
SN  - 2352-1465
DO  - 10.1016/j.trpro.2014.10.107
UR  - http://www.sciencedirect.com/science/article/pii/S2352146514002701
AB  - The ordered probit model is used to examine the contribution of several factors to the injury severity faced by motor-vehicle occupants involved in road accidents. The estimated results suggest that motor-vehicle occupants travelling in light-vehicles, at two-way roads, and on dry road surfaces tend to suffer more severe injuries than those who travel in heavy-vehicles, at one-way roads, and on wet road surfaces. Additionally, the driver's seat is clearly the safest seating position, urban areas seem to originate less serious accidents than rural areas, and women tend to be more likely to suffer serious or fatal injuries than men.
ER  -