Machine Learning and Image Recognition Technologies to Identify Built Environment Barriers and Incentives to Walk

Abstract

The local built environment characteristics deeply influence pedestrians’ behavior, favoring or imposing barriers on walking trips. However, identifying micro-scale built environment data is challenging and time consuming, and in developing countries there is a general lack of reliable information at street level. The recent development of machine learning and image recognition algorithms is helping researchers to collect data quickly, automatically, and on a large scale. Therefore, this study aims to test the application of an existing semantic segmentation algorithm to represent urban scenes in the city of São Paulo. A confirmatory multivariate technique (structural equation model [SEM]) is used to test if a combination of the predetermined categories derived from machine learning algorithms helps to understand which type of environment (urban scenes) represents barriers or incentives to walk. The impacts of the urban scenes on the walking behavior mediated by the walkability perceptions were tested using the aforementioned SEM model. Car-oriented and unoccupied areas, with a high presence of heavy vehicles and a large presence of vegetation, are considered detrimental to the walkability perception and consequently to the walking frequency. On the other hand, densified areas, proximity to public transportation routes, and the presence of lighting and other pedestrians are considered friendlier for pedestrians, encouraging residents to walk.

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