Rafael Szeliga
As a civil engineer with over 15 years of experience, I focus on transportation engineering and infrastructure development. I contribute to diverse projects in transport engineering, including geometry design, drainage, pavement, and earthworks. My expertise also extends to urban mobility consulting and traffic engineering, utilizing advanced traffic microsimulation tools for impact studies on traffic-generating hubs.
My technical proficiency includes geoprocessing using QGIS, statistical analyses with R programming, and foundational knowledge in Python and machine learning. Currently pursuing a doctorate in Geodetic Sciences (cartography field) at Universidade Federal do Paraná, I am committed to exploring innovative solutions for urban planning and mobility and road safety challenges.
Session
This study evaluates how well OpenStreetMap (OSM) data can explain urban operating speeds using spatial regression models in Curitiba, Brazil, combining naturalistic driving data with socioeconomic and built-environment variables. The results show that OSM-derived variables, especially road network density and traffic signals, are useful for identifying spatial patterns in speed, but their limited detail reduces predictive accuracy, making OSM more suitable for exploratory large-scale safety analyses than precise modeling.