ALF–Score—A novel approach to build a predictive network–based walkability scoring system

Abstract

Walkability is a term that describes various aspects of the built and social environment and has been associated with physical activity and public health. Walkability is subjective and although multiple definitions of walkability exist, there is no single agreed upon definition. Road networks are integral parts of mobility and should be an important part of walkability. However, using the road structure as nodes is not widely discussed in existing methods. Most walkability measures only provide area–based scores with low spatial resolution, have a one–size–fits–all approach, and do not consider individuals opinion. Active Living Feature Score (ALF–Score) is a network–based walkability measure that incorporates road network structures as a core component. It also utilizes user opinion to build a high–confidence ground–truth that is used in our machine learning pipeline to generate models capable of estimating walkability. We found combination of network features with road embedding and points of interest features creates a complimentary feature set enabling us to train our models with an accuracy of over 87% while maintaining a conversion consistency of over 98%. Our proposed approach outperforms existing measures by introducing a novel method to estimate walkability scores that are representative of users opinion with a high spatial resolution, for any point on the road.