Advanced models for improving road operations and safety

用于改善道路运营和安全的先进模型

• 批准号: RGPIN-2016-04013
• 负责人: Hellinga, Bruce
• 金额: $ 1.97万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708746
• 关键词: Advanced; models; improving; road; operations

Traffic congestion is reported to cause over 4 billion hours of delay, waste 10.6 billion litres of fuel, and create an economic cost of $87 billion annually in the USA. Comparable national data are not readily available for Canada, but congestion in the Toronto region is estimated to cost more than $6 billion annually and is expected to increase to $15 billion by 2031 in the absence of significant intervention. Twenty five percent of congestion is caused by incidents (such as collisions and spilled loads) and a further 15% is caused by adverse weather. Given the significant costs associated with expanding existing networks to increase capacity, and the staggering costs associated with road congestion, there are substantial economic benefits to better optimizing the use of existing network infrastructure. Existing and emerging technologies, such as crowd sourcing, Connected Vehicles, and Autonomous Vehicles, along with the new data streams these technologies are expected to supply, provide an opportunity to significantly improve traffic operations and safety. The emergence of these new technologies and data streams also presents significant challenges to road authorities in terms of determining the regulations, design guidelines, and practices that are required to maximize benefits in the most cost effective manner. This proposal describes a 5-year research program that builds on the applicant's previous research accomplishments and existing research strengths, and focuses on developing and evaluating algorithms and methods required for proactive and integrated transportation management. The research program consists of four interconnected themes: 1. Improving traffic conditions monitoring and prediction. 2. Identifying how existing roadway design standards and traffic control guidelines could be changed to optimize the performance of autonomous vehicles. 3. Creating a method for evaluating transportation system improvements in terms of their impacts on travel time reliability. 4. Measuring and modelling the interactions between cyclists and motorized vehicles. The objectives of the proposed research program are to (1) develop and evaluate the above methods, measures, and strategies to improve network capacity, reduce congestion, increase system reliability, and increase safety; (2) transfer the developed knowledge and results to practitioners, road authorities, and the scientific community; and (3) train HQP in the latest methods and techniques so they can continue to foster innovation and advances within the public sector, the private sector, and the academic and scientific community in Canada and abroad.

据报道，在美国，交通拥堵每年造成超过40亿小时的延误，浪费106亿升燃料，并造成870亿美元的经济成本。 加拿大没有现成的可比国家数据，但多伦多地区的拥堵估计每年造成60多亿美元的损失，如果不进行重大干预，预计到2031年将增加到150亿美元。25%的拥堵是由事故（如碰撞和溢出负载）引起的，另外15%是由恶劣天气引起的。 考虑到与扩大现有网络以增加容量相关的巨大成本，以及与道路拥堵相关的惊人成本，更好地优化现有网络基础设施的使用具有巨大的经济效益。 现有的和新兴的技术，如众包、联网车辆和自动驾驶车辆，沿着这些技术有望提供的新数据流，为显著改善交通运营和安全提供了机会。 这些新技术和数据流的出现也对道路管理部门提出了重大挑战，即确定以最具成本效益的方式实现最大效益所需的法规、设计指南和实践。 该提案描述了一个为期5年的研究计划，该计划建立在申请人以前的研究成果和现有研究优势的基础上，重点是开发和评估主动和综合交通管理所需的算法和方法。该研究计划包括四个相互关联的主题： 1.改善交通状况监测和预测。 2.确定如何改变现有的道路设计标准和交通控制指南，以优化自动驾驶汽车的性能。 3.创建一种方法来评估交通系统的改进对旅行时间可靠性的影响。 4.测量和模拟骑自行车者和机动车辆之间的相互作用。 研究计划的目标是：（1）开发和评估上述方法、措施和策略，以提高网络容量、减少拥堵、提高系统可靠性和增加安全性;（2）将开发的知识和结果传递给从业人员、道路管理部门和科学界;以及（3）在最新的方法和技术方面对HQP进行培训，以便他们能够继续在加拿大和国外的公共部门、私营部门以及学术和科学界促进创新和进步。