Measuring Pedestrian Accessibility Within the University Campus Using Data Science

Abstract: In developing countries like India, Walking has become a crucial and sustainable way of travel for college students in their daily lives and improving the walkability of the college campus will increase the convenience and easy of student life. This paper develops a new pedestrian accessibility, whether the students are following the Covid-19 norms like following the lane discipline etc, which optimizes the Walk Score Method (WSM) based on the frequency, variety, and distance of students’ walking to and from public facilities. Here we measure pedestrian density, pedestrian speed using data science tools. For pedestrian density we use Voronoi diagrams. Voronoi diagram assigns each point to the area that is closer to that point than any other. This allows for the definition of a density distribution for each pedestrian and for measuring the pedestrian speed we use computer vision technology, once the data is collected then we use Python because it has a number of libraries that can be used for data analysis and visualization, such as NumPy, Pandas, and Matplotlib. We examine the campus of Reva University to test the tool’s application and evaluate the accessibility of facility layout and walkability, and to give suggestions for improvement. This assessment tool can help urban planners and campus designers make decisions about how to adjust the facility layout of existing campuses in different regions or to evaluate the campus schemes based on the results of their walkability assessment to achievement of Sustainable Development Goals (SDG-2030) as per United Nations.