Grant: Canada Research Chairs

In the post-pandemic world, Disruptive Transportation Technologies and Services (DTTS), e.g., connected and automated vehicles, urban air mobility, electric vehicles on the smart-grid, blockchain based mobility services, micromobility, and on-demand mobility services, have the potential to become an integral and ubiquitous part of the smart city ecosystem. For sustainable adoption, operations, design, and planning of DTTS in our future transportation systems, one must proactively understand the emerging challenges and opportunities associated with them. The proposed CRC program will develop novel research that will explore emerging issues related to DTTS in smart cities. Using virtual reality (VR), digital twin, and advanced machine learning and econometric modelling, the program will further improve our understanding of the key push- and pull-factors affecting mobility behaviour for the sustainable adoption of DTTS at urban scale. The novel travel behavioural models developed in the program will particularly focus on capturing the decision-making process using causal inference theories. The program will also develop novel technologies, algorithms, and modelling/simulation tools that can help DTTS address issues related to climate change, equity and inclusion, privacy, algorithm/data bias, and cybersecurity. For this purpose, the program will explore the use of blockchain, distributed learning, and multi-objective deep reinforcement learning. The research conducted in the next five years will result in a deeper understanding of the effects of DTTS on human mobility and activities, and the development of new models, algorithms, and solutions that can solve emerging issues. The program will bring fundamental methodological and technological contributions to key areas in transportation engineering and smart cities research. The one-of-a-kind VR based digital twin will greatly enhance existing knowledge and the ability to understand DTTS-related mobility behaviour. The innovative behavioural models that will be developed in this program and shared with the research community will contribute to the improvement of travel demand prediction literature. The innovative optimization algorithms and technologies arising from this program will contribute to the research community working on harmful emissions from transportation systems, and provide a systematic analysis of the capacity of DTTS to reduce them.