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Collision prediction models with longitudinal data: an analysis of contributing factors in collision frequency in road segments in Portugal
University Federal of Pernambuco, Brazil.
University of Minho, Portugal.
University of Brasilia, Brazil.
University of Minho, Portugal.
2016 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In spite of the strategic importance of the national Portuguese road network, there are no recent studies concerned with either the identification of contributory factors to road collisions or collision prediction models (CPMs) for this type of roadway. This study presents an initial contribution to this problem by focusing on the national roads NR-14, NR-101 and NR-206, which are located in Portugal’s northern region. This study analyzed the collisions frequencies, average annual daily traffic (AADT) and geometric characteristics of 88 two-lane road segments through the analysis of the impact of different database structures in time and space. The selected segments were 200-m-long and did not cross through urbanized areas. Data regarding the annual traffic collision frequency and the AADT were available from 1999 to 2010. The GEE procedure was applied to ten distinctive databases formed by grouping the original data in time and space.

sted, utgiver, år, opplag, sider
Linköping: Statens väg- och transportforskningsinstitut, 2016. s. 1-12
Emneord [en]
Accident, Prediction, Model (not math), Method, Data acquisition
HSV kategori
Forskningsprogram
X RSXC
Identifikatorer
URN: urn:nbn:se:vti:diva-10627OAI: oai:DiVA.org:vti-10627DiVA, id: diva2:927797
Konferanse
17th International Conference Road Safety On Five Continents (RS5C 2016), Rio de Janeiro, Brazil, 17-19 May 2016
Tilgjengelig fra: 2016-05-13 Laget: 2016-05-13 Sist oppdatert: 2019-05-08bibliografisk kontrollert

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