Active-travel modelling: a methodological approach to networks for walking and cycling commuting analysis

Abstract

Walking and cycling, commonly referred to as active travel, have become integral components of modern transport planning. Recently, there has been growing recognition of the substantial role that active travel can play in making cities more liveable, sustainable and healthy, as opposed to traditional vehicle-centred approaches. This shift in perspective has spurred interest in developing new data sets of varying resolution levels to represent, for instance, walking and cycling street networks. This has also led to the development of tailored computational tools and quantitative methods to model and analyse active travel flows. In response to this surge in active travel-related data and methods, our study develops a methodological framework primarily focused on walking and cycling as modes of commuting. We explore commonly used data sources and tools for constructing and analysing walking and cycling networks, with a particular emphasis on distance as a key factor that influences, describes, and predicts commuting behaviour. Our ultimate aim is to investigate the role of different network distances in predicting active commuting flows. To achieve this, we analyse the flows in the constructed networks by looking at the detour index of shortest paths. We then use the Greater London Area as a case study, and construct a spatial interaction model to investigate the observed commuting patterns through the different networks. Our results highlight the differences between chosen data sets, the uneven spatial distribution of their performance throughout the city and its consequent effect on the spatial interaction model and prediction of walking and cycling commuting flows.

References in corpus (13)

• Spatial Networks
• OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
• Gravity model in the Korean highway
• Urban street networks: a comparative analysis of ten European cities
• The Propensity to Cycle Tool: An open source online system for sustainable transport planning
• Exploring the Evolution of London's Street Network in the Information Space: a Dual Approach
• Growing Urban Bicycle Networks
• Data-driven strategies for optimal bicycle network growth
• Urban retail dynamics: insights from percolation theory and spatial interaction modelling
• Automated Detection of Missing Links in Bicycle Networks
• A review of the structure of street networks
• Quantifying relation between mobility patterns and socioeconomic status of dockless sharing-bike users
• Planning for sustainable Open Streets in pandemic cities