Estimating Annual Average Daily Bicyclists

Abstract

Cities around the United States are investing in bicycle infrastructure, and to secure additional transportation funding, cities are reporting bicycle use and safety improvements. Data on bicyclist traffic volume is necessary for performing safety studies and reporting facility use. Meeting the need for data, available manual bicycle counting programs count cyclists for a few hours per year at designated locations. A key issue in the design of counting programs is determining the timing and frequency of counts needed to obtain a reliable estimate of annual average daily bicyclists (AADB). In particular, in which days of the week, hours of the day, and months of the year should counts be collected? And, most important to program cost, how many hours should be counted? This study used continuous bicycle counts from Boulder, Colorado, to estimate AADB and analyze the estimation errors that would be expected from various bicycle-counting scenarios. AADB average estimation errors were found to range from 15% with 4 weeks of continuous count data to 54% when only 1 h of data was collected per year. The study found that the most cost-effective length for short-term bicycle counts is one full week when automated counting devices specifically calibrated for bicycle counting are used. Seasons with higher bicycle volumes have less variation in bicycle counts and thus more accurate estimates.

Mapping ridership using crowdsourced cycling data

“…Ridership data are difficult to obtain and often limited by traditional methods of data collection (Gosse and Clarens, 2014;Nordback et al, 2013). Traditional data collection methods typically include manual counts of cyclists during peak commuting periods, which are adjusted to provide an estimate of overall ridership.…”

Photos, tweets, and trails: Are social media proxies for urban trail use?

“…These efforts typically involve establishment of a set of permanent automated counters at a fixed number of locations believed to represent specific traffic patterns and then collection of a large number of short-duration (e.g., two to seven day) samples that can be used to characterize flows on a network. Statistics and ratios from the permanent sites are used to extrapolate the sample counts and obtain estimates of daily traffic (Federal Highway Administration, 2013;El Esawey, Lim, Sayed, & Mosa, 2013;Miranda-Moreno, Nosal, Schneider, & Proulx, 2013;Nordback, Marshall, Janson, & Stolz, 2013;Hankey, Lindsey, & Marshall, 2014). Several studies of trail use using these technologies and monitoring approaches have been reported (e.g., Lindsey, Wilson, Rubchinskaya, Yang, & Han, 2007;Wang, Lindsey, Hankey, & Hoff, 2014).…”

Population-Level Exposure to Particulate Air Pollution during Active Travel: Planning for Low-Exposure, Health-Promoting Cities

“…Emerging research focuses on describing patterns of bicycle and pedestrian traffic on transportation networks—for example, designing traffic count programs (Hankey et al 2014; Nordback et al 2013) and building facility–demand models (Hankey and Lindsey 2016; Miranda-Moreno and Fernandes 2011; Schneider et al 2012). Those findings highlight research and policy questions about how best to provide safe, health-promoting infrastructure for active travel (Loukaitou-Sideris et al 2014; McDonald et al 2014; Wilson et al 2010).…”