Pedestrian Traffic Volume Data Fusion - Using R for Data Integration, Analysis and Presentation
Regular talk, 11:20 AM - 12:20 PM
In this presentation, John Doe will present on work from a recently completed project that developed data and methods for estimating pedestrian traffic volumes across all roadway intersections in Oregon. Using traffic signal push button data and a data integration workflow that combines land use, transportation data, and crowdsourced information, the ODOT Research team built a data fusion model to estimate pedestrian traffic volumes. This presentation will cover the use of R for the entire workflow for this project. From accessing data through APIs, using routing engine packages to create data fusion model inputs, machine learning algorithms and statistical models to build the models, and Shiny to visualize the results, this project displays the immense power of R and its available libraries for improving data-driven transportation decision-making.
R enables reproducible, transparent, and scalable analytics that directly support ODOT's goals of eliminating fatal and serious injuries, advancing equity, and building a safe, climate-responsive, multimodal transportation system. By integrating pedestrian exposure data with safety, land use, and demographic information, these tools help ODOT better understand risk, identify structural disparities in access and safety outcomes, and prioritize investments that support walking, biking, transit, and other multimodal options across communities statewide.
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Pronouns: he/himSalem, OR, USA |