Automated Bicycle Counts

Abstract

Quantifying bicycle use is fundamental to understanding bicycle travel. Methods of counting bicycles vary from limited-time, manual counts to permanent overhead imaging sensors. One common permanent counting method uses inductive loops embedded in the pavement to count cyclists on paths. Although inductive loop detectors have been found to be a highly accurate method of counting bicycles under ideal test conditions, their accuracy after years of use has not been systematically studied. This study focuses on bicycle counts collected by the City of Boulder, Colorado, since 1998, on multiuse paths with inductive loop detectors. To estimate the accuracy of the devices in use, two individuals manually counted path users at six locations. On average, the loop detectors counted 4% fewer bicycles than the manual counters at the same locations. Of the 22 detector channels with sufficient counts to judge their accuracy, roughly 68% were considered accurate. The most dramatic inaccuracies were caused primarily by detector settings and software-related problems. This study found that inductive loop detectors can provide accurate measures of bicycle use on a pathway, but only when detectors are properly installed, calibrated, maintained, and free of external interference.

Big Data and Cycling

“…The most common methods are based on pneumatic tubes, inductive loops, passive infrared, automated video counters, infrared cameras and fibre optic pressure sensors (Ryus et al, 2014). Pneumatic and inductive are widespread, but proved to be accurate only when detectors are properly installed, calibrated, maintained, free of external interference, and on a dedicated bicycle lane (Nordback & Janson, 2010). Recently, more innovative counts based on fibre optics register cyclists on mixed traffic lanes, offering insight not only in the cycling volume but also in the speed and direction.…”

Guidebook on Pedestrian and Bicycle Volume Data Collection

“…Tests of embedded loop detectors in Colorado showed an accuracy of -4% at off-road locations, -3% accuracy on separated paths, and +4% accuracy on shared roadways (Nordback et al 2011;Nordback and Janson, 2010). Testing in New Zealand showed ranges of accuracy from Sensor technology toolbox 87 -10% to +4% for on-road count sites and -10% to +25% for off-road sites (ViaStrada, 2009).…”

Crowdsourcing Bicycle Volumes: Exploring the role of volunteered geographic information and established monitoring methods

“…This study addresses a need to evaluate current datasets that may extend the value of bicycle counting programs through volumes contributed by other means. Significant progress in institutionalizing bicycle and pedestrian monitoring has been made in Colorado, Minnesota, and Oregon, and agencies are searching for efficient solutions to fill this need (Lindsey, Nordback, and Figliozzi 2014;Nordback and Janson 2010;. A recent Transportation Research Board circular summarized a number of recent and ongoing studies in the realm of active transportation monitoring methods and identified a need to continue research in crowdsourced volume data sources (Griffin, Nordback, Götschi, Stolz, and Kothuri 2014).…”