Using deep learning to examine the correlation between transportation planning and perceived safety of the built environment

Justin B Hollander, Giorgi Nikolaishvili (), Alphonsus A Adu-Bredu, Minyu Situ and Shabnam Bista

Environment and Planning B, 2021, vol. 48, issue 7, 2023-2038

Abstract: In this study, we attempt to estimate the effects of various transportation policies on the perceived safety of the built environment. We train a convolutional neural network on a dataset of safety perception scores for Google Street View images taken in Boston, MA . We then apply the trained neural network to a large set of Google Street View images of coordinates in Montreal and Toronto to generate their respective safety perception scores. We estimate probit, logit, and ordinary least squares regression models using our cross-sectional dataset consisting of safety perception scores, as well as transportation policy variables and a set of control variables, by regressing the safety perception scores on the remaining set of variables. We answer our research question by observing the direction, magnitude, and statistical significance of the coefficient estimates associated with the policy variables across all regression models. We studied and cataloged transportation policies planned for over the past 10 years in both cities. We found that those census tracts with the poorest safety scores were the same places where planners focused their transportation investments. The study makes an important contribution to transportation planning methodologies by drawing on the novel data source of Google Street View images, to understand the safety of an area.

Keywords: Transportation policy; machine learning; Google Street View; Canada (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:48:y:2021:i:7:p:2023-2038

DOI: 10.1177/2399808320959079

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