Public transportation network design: A geospatial data-driven method

Abstract

The paper explores an issue of efficient public transportation network design as a part of the urban developing process. Having data about everyday residents travelling inside of an urban area, we can consider this data as people's requirements for the public transport system. We propose a novel method for initial public transportation network design based on clustered geospatial data containing origin/destination point of travel patterns of residents. The core of a method is a set of four algorithms for selecting terminal clusters based on a number of centres (or focuses). The appropriate quality criteria are proposed: degree of transport demand satisfaction, the coefficient of non-straightness, and transport network density. Use cases allow evaluating a performance of proposed method and give sufficent conclusions about its application.