Applications of Spatial and Data Science Approaches in Transportation Studies

Spatial data and methods are widely integrated with transportation studies. In addition, emerging data science approaches are increasingly integrated with transportation research to generate data driven solutions to complex problems. This talk explores the applications of spatial and data science approaches in urban transportation research. It provides case studies on spatial data modelling applications used to identify and understand the sustainability of the vehicular transport system. Then, it explores how data mining approaches can be integrated with transport modelling to study traffic signal optimization. Finally, it discusses the potential of innovative spatial data science methods when studying active travel in data scare contexts.

Dr. S.M. Labib is a Research Associate at Centre for Diet and Activity Research, at the University of Cambridge. His primary research focuses on developing novel quantitative spatial analytical approaches associated with geographic information science, spatial data science in studying urban built environment, and transport health impact modelling. He utilizes spatial analytics in transport-health modelling to explore the influence of the built environment and active transportation (e.g., cycling) on health outcomes.