Optimization of Two-Phase Sampling Designs with Application to Naturalistic Driving Studies
Artikel i vetenskaplig tidskrift, 2022

Naturalistic driving studies (NDS) generate tremendous amounts of traffic data and constitute an important component of modern traffic safety research. However, analysis of the entire NDS database is rarely feasible, as it often requires expensive and time-consuming annotations of video sequences. We describe how automatic measurements, readily available in an NDS database, may be utilised for selection of time segments for annotation that are most informative with regards to detection of potential associations between driving behaviour and a consecutive safety critical event. The methodology is illustrated and evaluated on data from a large naturalistic driving study, showing that the use of optimised instance selection may reduce the number of segments that need to be annotated by as much as 50%, compared to simple random sampling.

case-control studies

unequal probability sampling

optimal design

naturalistic driving studies

safety critical event



Henrik Imberg

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Vera Lisovskaja

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Selpi Selpi

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet

Olle Nerman

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

Vol. 23 4 3575-3588

Statistiska metoder för tolkning av förarbeteenden och olycksorsaker i mätintensiva realistiska trafikförsök

Vetenskapsrådet (VR) (2012-5995), 2012-01-01 -- 2015-12-31.




Sannolikhetsteori och statistik



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