Multi-Agent Modeling, Simulation & Analysis of Mixed Vehicular Traffic of Autonomous & Human Driven Vehicles and of Spread of Epidemics & their Mitigation Strategies

拟议研究的目的是进一步发展和调查基于个体的建模方法并将其应用于两个数据流：(1)研究由常规(人驾驶)车辆(RV)和自动驾驶车辆(AV)混合组成的公路交通动力学；(2)根据加拿大统计局的人口普查数据研究传染病(如新冠肺炎、新冠肺炎和流感)在现实人口分布中的传播，以及研究非药物干预措施和疫苗接种策略的有效性。因此，我们的目标是：(1)开发具有反应性和认知性智能体的大规模基于多智能体的仿真(MABS)模型；(2)广泛分析它们的定量和定性参数/因素、它们的水平及其相互作用如何影响动态和性能；(3)应用统计和机器学习方法来控制和预测它们的动态和性能；(4)根据MABS模型的数据分析需要开发新的统计和机器学习方法。这项研究的新颖性在于研究了由AV和RV组成的混合公路交通，预计这种混合交通至少会持续到2055年。目前，对汽车音响的研究主要集中在单车上。混合交通(RV、AV)的研究还处于起步阶段，但由于这种混合交通的后果是未知的，它们将在未来30-40年内影响公路交通及其管理，因此开展此类研究具有重要意义。这项研究的影响是为交通工程师提供新的知识，了解不同的AV和RV混合如何影响交通流量和拥堵，从而影响道路/高速公路的通行能力，进而提高交通基础设施的效率。这些将为交通工程师提供高速公路交通管理所需的信息。传染病(如新冠肺炎)的流行是对公共卫生和经济的巨大威胁，对此缺乏药物干预。由于流行病的各种不确定性和非线性&复杂性，制定有效的缓解战略是非常具有挑战性的。这项建议的新颖性和意义在于：(1)开发大规模的MABS模型(带有反应剂、认知剂)，以在现实的大规模人口分布(如安大略省南部)中测试针对新冠肺炎(和其他流行病)的各种非药物干预和疫苗接种策略的建模；(2)开发深度强化学习方法，以自动学习控制流行病传播的策略。总而言之，拟议研究产生的新知识将影响两个群体：交通工程师、公共卫生从业者和决策者，使他们能够了解这些复杂的系统、它们的现象并改善它们的运行。

The objective of the proposed research is the further development & investigation of individually based modeling methodologies and their application to two streams: (1) study of the dynamics of highway traffic consisting of mixed regular (human driven) vehicles (RV) and autonomous vehicles (AV); (2) spread of epidemics of infectious diseases (e.g., COVID-19, COVID-19 & influenza) in realistic population distributions as per Statistics Canada census data and the study of the effectiveness of non-pharmaceutical interventions and vaccination strategies. Thus, the objectives are: (1) the development of large scale Multi-Agent Based Simulation (MABS) models with reactive and cognitive agents; (2) extensive analysis of how their quantitative & qualitative parameters/factors, their levels and their interactions affect the dynamics & performance; (3) application of statistical and machine learning methods for control & prediction of their dynamics & performance; (4) development of novel statistical & machine learning methods as needed by the data analysis of the MABS models. The novelty of the proposed research is in studying the mixed highway traffic consisting of AV & RV, which is predicted to persist at least until 2055. Currently, the focus of research on AV is mostly on individual vehicles. The research on the hybrid vehicular traffic (RV, AV) is in its infancy, but it is important to conduct such research as consequences of such hybrid traffic are unknown and they will affect highway traffic and its management for the next 30-40 years. The impact of this research is in providing the traffic engineers with new knowledge of how various mixes of AV & RV affect traffic flow & congestion, thus roads/highways capacity and in turn transportation infrastructure efficiency. These will provide traffic engineers the information needed for the management of highway traffic. Epidemics of infectious diseases (e.g., COVID-19), for which pharmaceutical interventions do not exist, are a great threat to public health & economy. The development of efficient mitigation strategies is very challenging due to various uncertainties and non-linearities & complexities of epidemics. The novelty and significance of this proposal is in: (1) the development of large scale, MABS models (with reactive agents, cognitive agents) to test the modelling of various non-pharmaceutical interventions & vaccination strategies for COVID-19 (and other epidemics) in large scale realistic population distributions (e.g., Southern Ontario); (2) the development of a deep reinforcement learning approach to automatically learn strategies to control spread of epidemics. In conclusion, the new knowledge produced by the proposed research will impact two communities: traffic engineers, public health practitioners & decision-makers by giving them means to understand these complex systems, their phenomena and to improve their operations.