Optimisation of Two-Phase Sampling Designs with Application to Naturalistic Driving Studies
Artikel i vetenskaplig tidskrift, 2021

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.


naturalistic driving studies

safety critical event

case-control studies

optimal design

unequal probability sampling


Henrik Imberg

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Vera Lisovskaja

Matematisk statistik

Selpi Selpi

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet, Olycksanalys och prevention

Olle Nerman

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

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

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




Sannolikhetsteori och statistik

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