Urban Transportation Operation and Management in a Connected and Autonomous vehicle Environment

曾经被视为虚构的联网自动驾驶汽车（cav）现在可以“看见”、“说话”和自动驾驶，并正在改变我们城市的交通系统。根据它们的部署方式，自动驾驶汽车将创造巨大的机遇、挑战和研究需求。例如，个人和交通自动驾驶汽车将配备先进的传感技术，在没有任何人类互动的情况下进行导航，从而影响附近的驾驶行为。联网车辆具有车对车、车对基础设施和车对设备的通信能力，将交换重要信息，从而实现协作决策，实现全系统目标。我提出的研究计划建立在先前的发现基金（DG）和发现加速器补充（DAS）期间开展的活动的基础上，以开发连贯的理论和方法平台，以利用新兴技术的潜力，同时预测和减轻其负面外部性。我的长期愿景是弥合理论、方法和实际应用之间的差距，推动加拿大新兴交通技术的部署，同时为经济多样化、环境管理和资源分配公平做出贡献。在接下来的五年里，我的研究计划将开发一个整体框架，使自动驾驶汽车技术、政策和实时交通和运输控制方案协同工作，以实现提高效率、公平、可靠性和减少负面外部性的总体目标。在我的研究中，还没有尝试过公平的概念，这是非常新颖和独特的。要达到的短期目标如下：设计利用自动驾驶汽车潜力的新一代交通管制方案；2. 设计新形式的出行需求政策，塑造自动驾驶汽车的部署，以满足社会目标；3. 设计新颖的自动驾驶控制系统，提高交通可靠性和乘客体验；4. 估计/预测由气候变化引起的旅行模式变化；和5。从吞吐量、可靠性和弹性方面评估最终的网络性能。我们的愿景是通过开发新颖的概念、政策、模型、方法和算法，为城市交通提供突破性的解决方案。我们的科学方法利用了交通流理论、先进的交通管理、交通控制、网络建模和仿真、数学建模和编程、双层博弈论、基于代理的建模、数据驱动的见解和机器学习。本研究对加拿大经济发展具有重要意义，对HQP的多学科培训具有重要意义。我们的学员将成为下一代交通政策制定者、研究人员、学者和工程师，他们将成为推动智慧城市倡议的领导者和拥护者。

Once perceived as fiction, connected and autonomous vehicles (CAVs) can now "see", "talk", and drive themselves, and are transforming our cities' transportation systems. Depending on how they are deployed, CAVs will create tremendous opportunities, challenges and research needs. For instance, personal and transit autonomous vehicles will be equipped with advanced sensing technologies to navigate without any human interaction, influencing the driving behaviour in their vicinity. Connected vehicles, with their vehicle-to-vehicle, vehicle-to-infrastructure and vehicle-to-devices communication capabilities, will exchange vital information to enable cooperative decisions to achieve system-wide goals. My proposed research program builds upon activities undertaken during the previous Discovery Grant (DG) and Discovery Accelerator Supplement (DAS) to develop coherent theoretical and methodological platforms to harness the potentials of emerging technologies while anticipating and mitigating their negative externalities. My long-term vision is to bridge gaps between theory, methodology and real-world applications to advance the deployment of emerging transportation technologies in Canada, while contributing to economic diversification, environmental stewardship and equity in resource allocation. Over the next five years, my research program will develop a holistic framework where CAV-enabled technologies, policies and real-time traffic and transit control schemes all work in concert to achieve the overall objectives of improved efficiency, fairness, reliability and reduction of negative externalities. The inclusion of fairness concepts, which has not yet been attempted, is highly novel and unique to my research. The short-term objectives to be achieved are as follows: 1. Design the next generation of traffic control schemes that leverage the potentials of CAVs; 2. Devise new forms of travel demand policies to shape CAV deployment to meet societal goals; 3. Design novel CAV-enabled transit control to improve transit reliability and rider experience; 4. Estimate/predict the induced changes in travel patterns stemming from CAVs; and 5. Evaluate the resulting network performance in terms of throughput, reliability and resilience. Our vision is to provide ground-breaking solutions for urban transportation by developing novel concepts, policies, models, methodologies and algorithms. Our scientific approach capitalizes on traffic flow theory, advanced traffic management, transit control, network modelling and simulation, mathematical modelling and programming, bi-level game theory, agent-based modelling, data-driven insights, and machine learning. This research has important implications for the Canadian economy and is vital for the multi-disciplinary training of HQP. Our trainees will form the next generation of transportation policymakers, researchers, academics, and engineers who will be the leaders and champions for driving the Smart Cities initiative.