In a series of research projects funded by the National Institute for Transportation and Communities (NITC), researchers have been developing new approaches that combine conventional and emerging data sources to estimate bicycle volumes. Having accurate bicycle volumes can help state departments of transportation (DOTs) and other agencies to prioritize projects, plan and design new bicycle infrastructure, and conduct safety analyses.
Traditional permanent and short-term counting methods have a high degree of accuracy but are limited to certain locations or short periods of time, while crowdsourced data (such as Strava or StreetLight) can cover a wider area but with less accuracy. Fusing the two methods together–potentially with the use of deep learning algorithms–is a promising way to get the best of both.
The latest report to come out of these efforts, by Sirisha Kothuri, Banafsheh Rekabdar and Joe Broach of Portland State University, pushed the needle forward on using advanced techniques to extrapolate data over a large transportation network. Two PSU graduate students also worked on the project: Saba Izadkhah, who is working toward a PhD in computer science, and Andrew Wagner, a computer science masters student.
Read more"These methods are still evolving, and it's still in the research phase. But I think the time is not far off when we will...
