A multivariate analysis of crash and naturalistic driving data in relation to highway factors.
20131416 ST [electronic version only]
Gordon, T.J. Kostyniuk, L.P. Green, P.E. Barnes, M.A. Blower, D.F. Bogard, S.E. Blankespoor, A.D. Leblanc, D.J. Cannon, B.R. & Mclaughlin, S.B.
Washington, D.C., National Research Council NRC, Transportation Research Board TRB, 2013, 64 p., 86 ref.; Second Strategic Highway Research Program SHRP 2 ; Report S2-S01C-RW-1 - ISBN 978-0-309-12890-2
|Samenvatting||This report documents the second phase of a two-phase project under SHRP 2 Safety Project S01C. A primary part of this work involved conducting a multivariate analysis of crash and naturalistic driving data in relation to highway factors. A geographic information system (GIS) framework was used as the basis for fusing multiple information sources to analyse road departure crash risk. A major goal was to use this method to support formulation and validation of crash surrogates. Two analytical models developed in the study focus on the statistical relationship between surrogate measures of crashes and actual crashes and on the formulation of exposure-based risk measures using surrogate measures. The first approach is an extension of the traditional univariate response model for crashes to a model that treats crashes and crash surrogates as a bivariate response variable, incorporates a correlation structure between them, and can be extended to a Bayesian model. The second approach is based on extreme value theory and estimates the probability of events that are more extreme than any that have been observed. The surrogate measures examined ranged from relatively simple measures based on lane position and time to the crossing of a lane boundary to more complex measures, such as a driver’s adjustment of the vehicle yaw angle to match that of the road. The report also describes three exploratory studies that illustrate the value of the geo-spatial approach taken. The first study examined the application of spatial statistics to the problem of determining if concentrations of crashes were really “hot spots” or if they could be considered simply random groupings. The second study compared surrogate event rates in episodes of driving while on and off using a cell phone. The comparison was made for the same driver for the same conditions. The third study compared the yaw rate error of drivers through areas of lane widening and locations with uniform lane widths. The results of each exploratory study suggest that the combination of naturalistic, crash, and highway data provides a rich data resource for many types of research. This report provides valuable background information to highway safety analysts seeking to use the data that will be made available from the SHRP 2 NDS. The beneficiaries of such research will be the nation’s highway users as vehicle design and road design, as well as highway traffic control, are improved through the analytical use of the data. (Author/publisher)|
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