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Walking and cycling, commonly referred to as active travel, have becomeintegral components of modern transport planning. Recently, there has beengrowing recognition of the substantial role that active travel can play inmaking cities more liveable, sustainable and healthy, as opposed to traditionalvehicle-centred approaches. This shift in perspective has spurred interest indeveloping new data sets of varying resolution levels to represent, forinstance, walking and cycling street networks. This has also led to thedevelopment of tailored computational tools and quantitative methods to modeland analyse active travel flows.  In response to this surge in active travel-related data and methods, ourstudy develops a methodological framework primarily focused on walking andcycling as modes of commuting. We explore commonly used data sources and toolsfor constructing and analysing walking and cycling networks, with a particularemphasis on distance as a key factor that influences, describes, and predictscommuting behaviour. Our ultimate aim is to investigate the role of differentnetwork distances in predicting active commuting flows.  To achieve this, we analyse the flows in the constructed networks by lookingat the detour index of shortest paths. We then use the Greater London Area as acase study, and construct a spatial interaction model to investigate theobserved commuting patterns through the different networks. Our resultshighlight the differences between chosen data sets, the uneven spatialdistribution of their performance throughout the city and its consequent effecton the spatial interaction model and prediction of walking and cyclingcommuting flows.

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