A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection

Summary The Highway Safety Manual (HSM) recommends using the empirical Bayes method with locally derived calibration factors to predict an agency's safety performance. The data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics in...

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Veröffentlicht in:	Journal of advanced transportation 2016-06, Vol.50 (4), p.522-540
Hauptverfasser:	Saha, Dibakar, Alluri, Priyanka, Gan, Albert
Format:	Artikel
Sprache:	eng
Schlagworte:	Calibration Crashes Data acquisition Data collection data mining Density High-speed machining Highway safety Highway Safety Manual Lanes Manuals random forests variable importance variable prioritization Variables
Online-Zugang:	Volltext
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