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

Abstract

Walkability is a term that describes aspects of the built and social environment. Previous studies have shown that different operationalisations of walkability are associated with physical activity and health. Walkability can be subjective and although multiple operational definitions and walkability measurement exist, there is no single agreed upon conceptual definition. Despite lack of consensus of a walkability definition, typical operational definitions include measures of population density, destinations, and the road network. Network science approaches such centralities and network embedding are missing from existing methods, yet they are integral parts of our mobility and should be an important part of how walkability is measured. Furthermore, most walkability measures have a one-size-fits-all approach and do not take into account individual user’s characteristics or walking preferences. To address some limitations of previous works, we developed the Active Living Feature Score (ALF-Score). ALF-Score is a network-based walkability measure that incorporates the road network structures as a core component. It also utilizes user data to build high-confidence ground truth that are used in conjunction with our machine learning pipeline to generate models capable of estimating walkability scores that address existing gaps in the walkability literature. We find, relying on road structure alone, we are able to train our models to estimate walkability scores with an accuracy of over 86% while maintaining a consistency of over 98% over collected user data. Our proposed approach outperforms existing measures by providing a walkability data at a much higher resolution as well as a user-derived result.

ALF-Score++, a novel approach to transfer knowledge and predict network-based walkability scores across cities

“…As an overview to the data used in this research, the general structure of our road network and feature set remains the same to one described in our paper ALF-Score 13 . We collected a small set of user opinion data containing 1,050 user entries covering 895 unique locations for the city of St. John's, NL.…”

“…In our previous paper, we created the Active Living Feature Score -ALF-Score 13 , a completely new approach to measure walkability. This predictive approach allows us to use various important features currently not utilized by most existing walkability measures, such as road network structure as nodes, road embedding, complex centrality measures, and user opinion along with a new approach of using machine learning to estimate walkability scores.…”

ALF-Score++ - Transferability of a Predictive Network-Based Walkability Scoring System

“…The next step needed to take to prepare the pipeline is the application of Generalized Linear Extension of Partial Orders or GLEPO 13 . GLEPO requires a few data sets such as user opinion, node list and a distance matrix connecting all the nodes within the network.…”

“…As an overview to the data used in this research, the general structure of our road network and feature set remains the same to one described in our paper ALF-Score 13 . We collected a small set of user opinion data containing…”