Grant: Discovery Grants Program - Individual

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.