Annual Average Nonmotorized Traffic Estimates from Manual Counts: Quantifying Error

Abstract

Across the United States, jurisdictions are investing more in bicycle and pedestrian infrastructure, which can benefit from nonmotorized traffic volume data. The design of nonmotorized counting programs varies. Whereas some agencies use automated counters to collect continuous and short duration counts, the most common type of bicycle and pedestrian counting is manual counting either in the field or from video. The objective of this research is to identify the optimal times of day to conduct manual counts for the purposes of accurately estimating annual average daily nonmotorized traffic (AADNT). This study used continuous bicycle and pedestrian counts from six U.S. cities to estimate AADNT and analyze estimation errors for multiple short duration count scenarios. Using two permanent counters per factor group reduces error substantially (> 50%); afternoon counts seem to be best for reducing error (2:00 to 6:00 p.m.). Error on Sunday is often as good as, if not better than, Saturday, contrary to what others have found. Arlington has the lowest AADNT estimation error (mean absolute percentage error), probably because of better data quality and higher nonmotorized traffic volumes, and Mount Vernon, Washington has the highest. Average AADNT estimation errors for the studied short duration count scenarios range 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 is available to estimate adjustment factors.

Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data

“…Research on monitoring active transportation modes such as bicycling has supported advancements in practice, including reference-quality counts using permanent traffic recorders (6,7), and new approaches to crowdsource bicycling activity in addition to passive sensing using smartphones and other digital devices (8,9). Advancements in different approaches to bicycle counting support performance monitoring, including the critical challenge of comparing collision risk.…”

Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

“…Extrapolation has been used in several studies Journal of Advanced Transportation 3 for estimating Annual Average Daily Pedestrian (AADP) and Annual Average Daily Bicyclist (AADB) [4,6,25,44,45]. When collecting short-term counts, several factors such as counting period length, time of day, month, and year could potentially impact the accuracy of yearly count estimation [3,4,6,[45][46][47]. In addition to collecting short-term data, long-term count data at several locations are also required to perform the extrapolation.…”

“…Due to limited resources and time, it is not possible to collect data for a whole year at many locations. Thus, a common practice is to collect short-term counts at several locations and apply an extrapolation method to convert short-term counts into yearly counts [3][4][5][6][7][8][9], which can be expressed in AADP or AADB forms. Exposure modeling can then be applied to estimate AADP or AADB for all other locations that were not originally selected.…”

Clustering Approach to Generate Pedestrian Traffic Pattern Groups

“…4-1). Furthermore, [7] indicated that the most common source for pedestrian data is manual counts, even though annual estimates from shortterm manual counts could generate errors between 30 and 60 percent. Additionally, [8] indicate that the research related to the study of temporal factors for non-motorized traffic is scarce.…”