Operationalizing connected and automated truck platooning on Canadian highway infrastructure for GHG emissions mitigation

在加拿大高速公路基础设施上实施联网和自动化卡车队列，以减少温室气体排放

• 批准号: 577207-2022
• 负责人: Yang, CancanC
• 金额: $ 8.76万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749280
• 关键词: Operationalizing; connected; automated; truck; platooning

Over the next decades, truck freight transport is expected to undergo revolutionary changes with the deployment of 'Connected and Autonomous Vehicles' (CAV) technologies. This transformation is expected to reduce fuel consumption and GHG emissions at both the: 1) vehicle level: considering the cooperative operation of platooned trucks with reduced air drag; and 2) network level: adopting efficient use of highways through traffic congestion mitigation. While this is encouraging, the associated infrastructure planning and resources required to accommodate the expected pressures on our highway systems have fallen behind. To address this gap, the objective of the proposed project is to provide answers to the following two questions: 1) What truck platoon control strategies, traffic management, and infrastructure adaptations are needed to realize the full potential of CAV's emission benefits? 2) How much, realistically, CAV-enabled GHG abatement can be achieved by 2050? Addressing these questions requires a concerted effort from the policymakers, practitioners, and researchers from five distinct disciplines: Structures, Transportation, Aerodynamics, Optimization, and AI/Machine Learning, which compromise the research team and partnership members.As a result, the proposed project will generate experimental data and analytical tools to enable both infrastructure- and transportation-related solutions to the challenges of rolling out truck platooning on existing Canadian highway systems. These solutions include 1) formulation of infrastructure safety sensible regulations on platoon operation; 2) development of adaptation measures that will future-proof Canadian highway infrastructure to proliferate the CAV's GHG benefits to the national scale; 3) optimization of vehicle-level platoon control strategies and network-level traffic management to maximize GHG reductions. The proposed partnership will create a unique knowledge-sharing platform to assist policymakers and stakeholders in navigating the complex pathways for CAV technologies - allowing for transformational reductions in GHG emissions within Canadian road freight transportation.

在未来几十年，随着“互联和自动驾驶汽车”（CAV）技术的部署，卡车货运预计将发生革命性的变化。这一转型预计将在以下两个方面减少燃料消耗和温室气体排放：1）车辆层面：考虑减少空气阻力的车队卡车的合作运营; 2）网络层面：通过缓解交通拥堵有效利用高速公路。虽然这是令人鼓舞的，但为应付本港公路系统所预期的压力而进行的有关基础设施规划和所需的资源，却落后了。为了解决这一差距，拟议项目的目标是提供以下两个问题的答案：1）需要什么样的卡车车队控制策略，交通管理和基础设施的适应，以实现CAV的排放效益的全部潜力？2)到2050年，CAV能够实际实现多少温室气体减排？解决这些问题需要来自五个不同学科的政策制定者、实践者和研究人员的共同努力：结构、运输、空气动力学、优化和人工智能/机器学习，这会危及研究团队和合作伙伴成员。因此，拟议的项目将产生实验数据和分析工具，使基础设施和运输，相关的解决方案，以应对在现有的加拿大公路系统上推出卡车排队的挑战。这些解决方案包括：1）制定关于车队运行的基础设施安全合理法规; 2）制定适应措施，使加拿大公路基础设施面向未来，将CAV的温室气体效益扩散到全国范围; 3）优化车辆级别的车队控制策略和网络级别的交通管理，以最大限度地减少温室气体。拟议的伙伴关系将创建一个独特的知识共享平台，以协助政策制定者和利益攸关方在CAV技术的复杂途径中导航-从而实现加拿大公路货运温室气体排放的转型减少。