United States Department of Transportation
i
Optimizing Short Duration Bicycle and Pedestrian Counting in Washington State
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2017-12-01
[PDF-4.37 MB]
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Abstract:Across the United States, jurisdictions are investing more in bicycle and pedestrian infrastructure, which requires non-motorized traffic volume data. While some agencies use automated counters to collect continuous and short duration counts, the most common type of bicycle and pedestrian counting is still manual counting. The objective of this research is to identify the optimal times of day to conduct manual counts for the purposes of estimating annual average daily non-motorized traffic (AADNT) accurately. This study used continuous bicycle and pedestrian counts from six U.S. cities, including three in the Pacific Northwest, to analyze AADNT estimation errors for multiple short duration count scenarios. Using two permanent counters per factor group reduces error substantially (>50%) compared to using just one; afternoon counts seem to be best for reducing error (2PM-6PM). While Monday is associated with high error, Friday is comparable to other weekdays. Error on Sunday is often as good, if not better than Saturday, contrary to what others have found. Arlington had the lowest AADNT estimation error (mean absolute percent error) likely due to better data quality and higher non-motorized traffic volumes and Mt. Vernon, Washington had the highest. Average AADNT estimation errors for the studied short duration count scenarios ranged from 30% to 50%. Error is lower for the commute factor group, bicycle-only counts, scenarios in which more peak hours are counted, and when more than one permanent counter was available to estimate adjustment factors. To minimize error, this study recommends increasing the number of permanent bicycle and pedestrian count sites, validating and calibrating the equipment, and increasing the length of time counted at each count site to at least 8 hours (7-9AM, 11AM-1PM, 4-6PM TWorTh and 12-2PM Saturday), but preferably counting a whole week using calibrated automated equipment. This project produced a guidebook for communities (see Appendix J for link), incorporating results from this research as well as those of a companion project by Dr. Michael Lowry at University of Idaho.
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