Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data

Abstract

Traffic volumes are fundamental for evaluating transportation systems, regardless of travel mode. A lack of counts for non-motorized modes poses a challenge for practitioners developing and managing multimodal transportation facilities, whether they want to evaluate transportation safety or the potential need for infrastructure changes, or to answer other questions about how and where people bicycle and walk. In recent years, researchers and practitioners alike have been using crowdsourced data to supplement the non-motorized counts. As such, several methods and tools have been developed. The objective of this paper is to take advantage of new data sources that provide a limited and biased sample of trips and combine them with traditional counts to develop a practical tool for estimating annual average daily bicycle (AADB) counts. This study developed a direct-demand model for estimating AADB in Texas. Data from 100 stations, installed in 12 cities across the state, was used together with the crowdsourced Strava, roadway inventory, and American Community Survey data to develop the count model for estimating AADB. The results indicate that crowdsourced Strava data is an acceptable predictor of bicycle counts, and when used with the roadway functional class and number of high-income households in a block group, can provide quite an accurate AADB estimate (29% prediction error).