Data-Driven Evaluation Of Streets To Plan For Bicycle Friendly Environments: A Case Study Of Brisbane Suburbs

Gabrielle Toohey School of Architecture, The University of Queensland
Tommy Bao Nghi Nguyen School of Architecture, The University of Queensland
Ritva Vilppola School of Architecture, The University of Queensland
Waishan Qiu Cornell University
Wenjing Li Centre for Spatial Information Science, The University of Tokyo
Dan Luo School of Architecture, The University of Queensland

Empirical cycling data from across the world illustrates the many barriers that car-dependent cities face when implementing cycling programs and infrastructure. Most studies focus on physical criteria, while perception criteria are less addressed. The correlations between the two are still largely unknown. This paper introduces a methodology that utilises computer vision analysis techniques to evaluate 15,383 Google Street View Images (SVI) of Brisbane City against both physical and perception cycling criteria. The study seeks to better understand correlations between the quality of a street environment and an urban area’s ‘bicycle-friendliness’. PSPNet Image Segmentation is utilised against SVIs to determine the percentage of an image corresponding with objects and the environment related to specific cycling factors. For physical criteria, these images are then further analysed by Masked RCNN processes. For perception criteria, subjective ranking of the images is undertaken using Machine Learning (ML) techniques to score images based on survey data. The methodology effectively allows for current findings in cycling research to be further utilised in combination via computer visioning (CV) and ML applications to measure different physical elements and urban design qualities that correspond with bicycle-friendliness. Such findings can assist targeted design strategies for cities to encourage the use of safer and more sustainable modes of transport.

Keywords: Sdg 3, Sdg 11, Bicycle-Friendly, Quality Streetscapes, Active Living, Visual Assessment, Computer Visioning, Machine Learning

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