Improving Accuracy and Precision of Bicycle Volume Estimates Using Advanced Machine Learning Approaches

Sirisha Kothuri, Portland State University

Co-investigator:

Summary:

This study seeks to improve modeling methods for estimating bicycle volumes. Better estimates of bicycle activity on local road and trail networks can help agencies prioritize projects, plan and design new bicycle infrastructure, and conduct safety and other analyses thus addressing the NITC theme of improving mobility of people and goods to build strong communities. Other research has shown that the availability of safe, separated bicycle facilities can encourage more people to ride, but first agencies need to know where infrastructure is most needed. Better volume estimates can also help improve safety, resilience, and reliability for bicycle travel by identifying locations needing improvements or serving as critical links in the system. Finally, the proposed idea also applies emerging techniques to advance the state of the practice in applied modeling methods for volume estimation.

Project Details

Project Type:
Technology Transfer
Project Status:
Completed
End Date:
September 30,2024
UTC Grant Cycle:
NITC 16 Tech Transfer
UTC Funding:
$40,000