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|Title:||Portuguese two-lane highways: modelling crash frequencies for different temporal and spatial aggregation of crash data|
|Author(s):||Costa, Jocilene Otília|
Jacques, M. A. P.
Pereira, Paulo A. A.
Freitas, Elisabete F.
Soares, Francisco Emanuel Cunha
|Keywords:||Crash contributory factors|
Crash prediction models
Generalized estimating equations
|Publisher:||Taylor & Francis|
|Citation:||Costa J. O., Jacques M. A. P., Pereira P. A. A., Freitas E. F., Francisco S. PORTUGUESE TWO-LANE HIGHWAYS: MODELING CRASH FREQUENCIES FOR DIFFERENT TEMPORAL AND SPATIAL AGGREGATION OF CRASH DATA, Transport, doi:10.3846/16484142.2015.1073619, 2018.|
|Abstract(s):||The identification of contributory factors to crash frequencies observed in different highway facilities can aid transportation and traffic management agencies to improve road traffic safety. 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 crashes or crash 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 Portugalâ s northern region. They are two-lane single carriageway rural roads. This study analyzed the crash frequencies, average annual daily traffic (AADT) and geometric characteristics of 88 two-lane road segments. The selected segments were 200-m-long and did not cross through urbanized areas. The fixed length of 200 meters corresponds to the road length used in Portugal to define a critical point. Data regarding the annual crash frequency and the AADT were available from 1999 to 2010. Due to the high number of zero-crash records in the initial database, the data were explored to identify the best statistical modeling approach to be adopted. The GEE procedure was applied to 10 distinctive databases formed by grouping the original data in time and space. The results show that the different observations within each road segment present an exchangeable correlation structure type. This paper also analyses the impact of the sample size on the modelâ s capability of identifying the contributing factors to crash frequencies. The major contributing factors identified for the two-lane highways studied were the traffic volume (expressed in AADT), lane width, vertical sinuosity, and density of access points. Acceptable CPM was identified for the highways considered, which estimated the total number of crashes for 400-m-long segments for a cumulative period of two years.|
|Access:||Restricted access (UMinho)|
|Appears in Collections:||C-TAC - Artigos em Revistas Internacionais|
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