Optimal Deployment of Vision Zero Road Safety Strategies

该研究将调查新兴的和现有的道路安全策略，针对弱势道路使用者（骑自行车的人和行人）在信号十字路口，与两个合作伙伴组织，多伦多市和微交通。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，瑞尔森团队将开发和应用先进的分析方法和工具，对视频数据进行分析，以评估有针对性的道路安全策略所带来的最大效益，例如：-自行车道设计，以最大限度地减少与右转车辆的冲突；-作为行人和骑单车人士的安全措施，作为行人/单车的主要间隔的替代方案，提供全面保护的车辆左转弯；-使左转平静，使转弯速度变慢，行人和骑单车的人更容易看清；-抬高交叉路口和人行横道；右转通道设计。除了训练学生掌握道路安全分析的先进建模技术外，项目成果还将拯救生命，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他们的数据驱动的“零愿景”计划，并重新制定他们的设计指南和交通项目的标准实践。MicroTraffic将使用他们的软件来处理城市的视频馈电，并向Ryerson研究团队提供交通冲突和车辆速度的数据，并使用开发的模型来调整他们的平台测量替代安全数据的方式。

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.