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

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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.