Purpose: The most sustainable forms of urban mobility are walking and cycling. These modes of transportation are the most environmental friendly, the most economically viable and the most socially inclusive and engaging modes of urban transportation. To measure and compare the effectiveness of alternative pedestrianization or cycling infrastructure plans, the authors need to measure the potential flows of pedestrians and cyclists. The paper aims to discuss this issue. Design/methodology/approach: The authors have developed a computational methodology to predict walking and cycling flows and local centrality of streets, given a road centerline network and occupancy or population density data attributed to building plots. Findings: The authors show the functionality of this model in a hypothetical grid network and a simulated setting in a real town. In addition, the authors show how this model can be validated using crowd-sensed data on human mobility trails. This methodology can be used in assessing sustainable urban mobility plans. Originality/value: The main contribution of this paper is the generalization and adaptation of two network centrality models and a trip-distribution model for studying walking and cycling mobility.

Original languageEnglish
Pages (from-to)101-116
Number of pages16
JournalSmart and Sustainable Built Environment
Issue number1
Publication statusPublished - 2018

    Research areas

  • Local betweenness centrality, Local closeness centrality, Radiation model, Social network analysis, Spatial urban dynamics, Sustainable urban mobility

ID: 51456665