Estimation of Average Annual Daily Bicycle Count Using Bike-Share GPS Data and Bike Counter Data for an Urban Active Transportation Network
Marzi Rafieenia, Liza Wood, Mohsen Zardadi, Scott Fazackerley, Ramon, Lawrence

TL;DR
This study combines GPS data from dockless bike-share and physical counters to estimate and visualize the annual average daily bicycle volume in Kelowna's urban network, informing infrastructure planning.
Contribution
It introduces a novel method integrating GPS bike-share data with physical counters using graph modeling and statistical analysis to estimate bicycle volumes.
Findings
Estimated annual daily bicycle counts in downtown Kelowna.
Identified non-traditional cycling routes like laneways and highway crossings.
Provided insights for urban bike infrastructure improvements.
Abstract
In 2018, the City of Kelowna entered into a license agreement with Dropbike to operate a dockless bike-share pilot in and around the downtown core. The bikes were tracked by the user's cell phone GPS through the Dropbike app. The City's Active Transportation team recognized that this GPS data could help understand the routes used by cyclists which would then inform decision-making for infrastructure improvements. Using OSMnx and NetworkX, the map of Kelowna was converted into a graph network to map inaccurate, infrequent GPS points to the nearest street intersection, calculate the potential paths taken by cyclists and count the number of trips by street segment though the comparison of different path-finding models. Combined with the data from four counters around downtown, a mixed effects statistical model and a least squares optimization were used to estimate a relationship between…
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Taxonomy
TopicsUrban Transport and Accessibility · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques
