Advancing quantitative assessment and planning of Canadian cycling infrastructure

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This research advances quantitative methods for cycling infrastructure planning and evaluation. The research has two main goals. First, to develop an image-based machine learning algorithm to support rapid expansion of the Level of Traffic Stress (LTS) assessment and cumulative accessibility methods to new municipalities with available aerial and street level photography (e.g., all Canadian cities). Second, a novel mathematical optimization model will be developed to facilitate rapid identification of the streets and intersections where adding cycling infrastructure would provide the most overall benefit. The project will yield an easy to use framework to help planners evaluate and communicate the quantitative impact of cycling infrastructure interventions.