Quantifying the Uncertainty in Annual Average Daily Traffic (AADT) Count Estimates

摘要: This paper describes how Average Annual Daily Traffic (AADT) values provide a key variable in many models and policy decisions; however, these are simply rough estimates of traffic counts along the vast majority roadway sections. research quantifies level uncertainty AADT compares across sampling strategies. Variations estimation errors investigated area types, for both Minnesota Florida automatic recorder (ATR) sites. Errors as function distance to nearest site also studied, using predictions network travel patterns Austin, Texas. Overall at ATR sites found be highest (averaging 24.6%) when data come from misclassified on weekends. Spatial temporal (inter-sampling year) extrapolations can further add such error, sizable way. The analytical results this investigation suggest variety recommendations agencies seeking reduce appreciate their estimates. These include spring summer months (on weekdays), exercising greater caution with multi-lane low-AADT roadways, pursuing appropriate assignment groups, recognizing effects site. With adequate attention, (average) probably reduced 10 percent level. Nevertheless, still will have an impact investment decisions, crash rate calculations, demand model validation, other analyses.