Improving roadway operations and safety considering vulnerable road users and emerging technologies

迫切需要提高货物和人员运输的安全和效率。在加拿大，平均每天有16人因交通碰撞入院，6人死亡，每年因交通碰撞造成的经济损失估计超过400亿美元。此外，由于三个因素，城市交通模式、需求和选择正在发生深刻变化。首先，出行模式正在发生变化，越来越多的人采用远程工作，公共交通乘客减少，以及主要居住地的理想地点发生变化。新冠肺炎疫情极大地加速和放大了这些变化，但很可能会持续下去。其次，技术发展正在迅速扩大交通选择，改变出行模式。自动/半自动车辆(如特斯拉的“全自动驾驶”和Waymo的自动出租车服务)和各种个人移动车辆/设备(如电动自行车、滑板、滑板车等)正在改变个人的出行模式。货运和货物递送也正在经历戏剧性的技术变革，包括最后一英里递送机器人车辆(例如亚马逊的Scout)。第三，交通系统和移动数据(统称为智慧城市数据)的种类和覆盖范围快速增长。交通系统管理人员和当选的决策者负责规划、建设和管理我们的道路和公共交通系统，以提供安全、公平和高效的货物和服务运输，同时将对环境的负面影响降至最低。然而，鉴于移动选项和技术、出行模式以及智慧城市数据的快速变化，缺乏可用于评估计划和政策以及可主动管理系统的知识和工具。该提案描述了一项为期5年的研究计划，该计划建立在申请者之前的研究成果和现有研究优势的基础上，重点是在新兴的移动选择和智能城市数据的背景下，改进道路运营和安全的方法和知识，特别强调弱势道路用户(行人和骑自行车者)。该研究计划包括以下三个主要目标：1.通过应用人工智能改进道路安全网络筛选(NS)2.为规划和安全建模开发更准确的估计脆弱道路使用者暴露的方法3.通过开发和应用高级模拟模型确定更好的城市道路设计和交通控制该计划将在交通工程、道路安全和数据分析等高需求领域培训4名博士和4名硕士学生，并将产生先进技术供研究人员和从业者使用，以提高加拿大城市道路网络的安全和效率。

There is critical need to improve the safety and efficiency of the transport of goods and people. On average, in Canada, 16 people are admitted to hospital and 6 people are killed each day because of a traffic collision and the annual economic cost of collisions is estimated to exceed $40 billion annually. Furthermore, urban mobility patterns, needs, and options are profoundly changing because of three factors. First, travel patterns are changing with increasing adoption of remote work, declining public transit ridership, and changes in desired location of principle residence. These changes have been dramatically accelerated and amplified by the COVID-19 pandemic but are likely to persist. Second, technological developments are rapidly expanding transportation options and altering travel patterns. Personal mobility patterns are being transformed by autonomous/semi-autonomous vehicles (e.g. Tesla's "Full Self Driving" and Waymo's autonomous taxi service) and by a wide range of personal mobility vehicles/devices (e.g. electric bikes, skateboards, scooters, etc). Freight transportation and goods deliveries are also experiencing dramatic technological transformations including last mile delivery robotic vehicles (e.g. Amazon's Scout). Third, there has been a rapid growth in the variety and coverage of transportation system and mobility data (collectively called smart city data). Transportation system managers and elected decision makers are tasked with the responsibility to plan, construct, and manage our road and public transportation systems to provide safe, equitable and efficient transport of goods and services while minimizing negative environmental impacts. However, given the rapid changes in mobility options and technologies, travel patterns, and smart city data, there is a lack of knowledge and tools by which plans and policies can be evaluated and systems can be actively managed. This proposal describes a 5-year research program that builds on the applicant's previous research accomplishments and existing research strengths and focuses on advancing methods and knowledge for improving roadway operations and safety in the context of emerging mobility options and smart city data, with a particular emphasis on vulnerable road users (pedestrians and cyclists). The research program consists of three key objectives as follows: 1.Improve road safety network screening (NS) through the application of AI 2.Develop more accurate methods for estimating exposure of vulnerable road users for planning and safety modeling 3.Identify better urban roadway designs and traffic controls through the development and application of advanced simulation models This program will train 4 PhD and 4 Masters students in high demand areas of transportation engineering, road safety and data analytics, and will result in advanced techniques for use by researchers and practitioners to improve safety and efficiency of Canadian urban road networks.