Integrative Transportation Systems through Automation and Connectivity

能够“看”、“说话”、“听”和自动驾驶的联网和自动驾驶汽车（cav）正在将汽车从纯粹的“自私模式”转变为更“具有社会意识”但更高效的模式。自动驾驶汽车的出现将带来巨大的机遇、复杂性、挑战和研究需求，这取决于它们的部署方式。例如，自动驾驶汽车可以通过车辆对基础设施（V2I）、车辆对车辆（V2V）以及车辆对设备和基础设施对设备（X2X）通信收集并无处不在地传播交通网络中的信息。这种高度互联的互惠网络将使综合决策成为可能，既能满足旅客个人的需求，又能满足整个系统的社会目标。在综合交通系统中提出的CRC计划旨在推进加拿大新兴交通技术的理论和部署，以实现提高效率、公平、可靠性和减少负面外部性的明确目标。该提案的核心是四个协同的基于自动驾驶汽车的主题，它们同步工作，将自动驾驶汽车的部署塑造成一种“有意义的颠覆”，以满足我们未来城市的社会和可持续发展愿望。这些主题为设计下一代交通和过境控制工具、制定新的配套政策以及利用道路网络更好地预测出行需求波动提供了综合解决方案。我们的愿景是通过开发新颖的概念、政策、模型、方法和算法，为城市交通网络提供突破性的解决方案。我们的科学方法充分利用了交通流理论、交通管理、交通控制、网络建模和仿真、数学建模和编程、博弈论、基于代理的建模、数据驱动的见解、预测分析和机器学习等方面的先进技术。这项研究对加拿大经济具有重要意义，对下一代交通政策制定者、研究人员、学者和工程师的多学科培训至关重要。因此，这项研究将通过加强公平、安全、可达性和流动性、减少排放和实现经济多样化，直接和显著地为加拿大人的福利做出贡献。

Connected and autonomous vehicles (CAVs) that can "see", "talk", "hear" and drive themselves are transforming the automobile from a purely "selfish mode" to a more "socially-aware" yet efficient one. Depending on how they are deployed, the emergence of CAVs will bring tremendous opportunities, complexities, challenges, and research needs. For instance, CAVs can collect and ubiquitously disseminate information in the traffic network through vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V) and vehicle-to-device and infrastructure-to-device (X2X) communication. This hyperconnected web of reciprocity will enable integrative decisions which are inclusive of individual travellers' needs while also meeting desired system-wide societal objectives.This proposed CRC program in Integrative Transportation Systems aims to advance the theory and deployment of emerging transportation technologies in Canada to achieve the explicit objectives of improved efficiency, equity, reliability, and reduction of negative externalities. At the crux of this proposal are four synergistic CAV-based themes working in synchrony to shape CAV deployment into a "meaningful disruption" that meets the societal and sustainability aspirations of our future cities. These themes provide integrative solutions to design the next generation of traffic and transit control tools, devise novel accompanying policies, and instrument the road network to better predict travel demand fluctuation.Our vision is to provide ground-breaking solutions for urban transportation networks by developing novel concepts, policies, models, methodologies and algorithms. Our scientific approach capitalizes on advancements in traffic flow theory, traffic management, transit control, network modelling and simulation, mathematical modelling and programming, game theory, agent-based modelling, data-driven insights, predictive analytics and machine learning. This research has important implications for the Canadian economy and is vital for the multi-disciplinary training of the next generation of transportation policymakers, researchers, academics, and engineers. This research will thus directly and significantly contribute to the welfare of Canadians by enhancing equity, safety, accessibility and mobility, reducing emissions and diversifying the economy.