Optimal Deployment of Vision Zero Road Safety Strategies

零愿景道路安全策略的优化部署

• 批准号: 566916-2021
• 负责人: Persaud, Bhagwant
• 金额: $ 2.19万
• 依托单位: Ryerson University
• 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=723516
• 关键词: Optimal; Deployment; Vision; Zero; Road

The research will investigate emerging and existing road safety strategies targeted at vulnerable road users (cyclists and pedestrians) at signalised intersections, with two partner organisations, the City of Toronto and MicroTraffic. Using data derived from videos provided by the City of Toronto and other cities and the technological strength of MicroTraffic in computer vision, the Ryerson team will develop and apply advanced analytical methods and tools to the video-derived data to assess the design and operational circumstances associated with the greatest benefits of the targeted road safety strategies such as the following:- bike lane designs for minimising conflicts with right turn vehicular movements;- fully protected vehicular left turns as a pedestrian and cyclist safety measure, as an alternative to leading pedestrian/bicycle intervals;- left turn calming that results in slower turning speeds and better visibility of pedestrians and cyclists;- raised intersections and crosswalks; and- right turn channel designs.Apart from training students in advanced modelling techniques in road safety analysis, the project outcomes will save lives and reduce injuries in contributing to safer roads in Canada's increasingly congested cities. It is expected that the City of Toronto will use the results to optimise their data-driven Vision Zero program as well as re-formulate their design guidance and standard practices for transportation projects. MicroTraffic will use their software to process the City's video feeds and provide data to the Ryerson research team on traffic conflicts and vehicle speeds, and use the developed models to adapt the way their platform measures surrogate safety data.

该研究将调查新兴和现有的道路安全战略，针对弱势道路使用者（骑自行车和行人）在信号交叉口，与两个合作伙伴组织，多伦多市和微型交通。利用多伦多市和其他城市提供的视频数据以及MicroTraffic在计算机视觉方面的技术优势，瑞尔森团队将开发并应用先进的分析方法和工具来分析视频数据，以评估与有针对性的道路安全策略的最大利益相关的设计和运营环境，例如：- 全面保护车辆左转弯，作为行人和骑自行车者的安全措施，作为领先的行人/自行车间隔的替代方案;-左转弯平静，导致转弯速度较慢，行人和骑自行车者的能见度更好;-升高的十字路口和人行横道;及右转弯通道设计。除了训练学生在道路安全分析方面的先进模型技术外，该项目的成果将拯救生命，减少伤害，为加拿大日益拥挤的城市提供更安全的道路。预计多伦多市将利用这些结果来优化其数据驱动的Vision Zero计划，并重新制定其交通项目的设计指南和标准实践。MicroTraffic将使用他们的软件来处理城市的视频馈送，并向Ryerson研究团队提供有关交通冲突和车辆速度的数据，并使用开发的模型来调整其平台测量替代安全数据的方式。