Advancing quantitative assessment and planning of Canadian cycling infrastructure

推进加拿大自行车基础设施的定量评估和规划

• 批准号: 561212-2020
• 负责人: Saxe, Shoshanna
• 金额: $ 2.62万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720193
• 关键词: Advancing; quantitative; assessment; planning; Canadian

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.

本研究提出了自行车基础设施规划与评价的量化方法。这项研究有两个主要目标。首先，开发一种基于图像的机器学习算法，以支持将交通压力水平(LTS)评估和累积可访问性方法快速扩展到拥有可用航空和街道水平摄影的新市政当局(例如，加拿大所有城市)。其次，将开发一种新的数学优化模型，以便于快速识别添加自行车基础设施将提供最大整体效益的街道和十字路口。该项目将产生一个易于使用的框架，帮助规划者评估和沟通自行车基础设施干预措施的量化影响。