Data-driven optimization of hub locations for smart mobility

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Smart mobility refers to using cleaner, safer, fuel- and time-efficient modes of transportation that can lead to improvements in ridership habits, transit network efficiency, fuel economy, and reduce congestion and emissions. Smart mobility plays a big role in Canada's long-term ambition to drastically reduce greenhouse gas emissions and achieve a safe, secure, green, innovative, and integrated transportation system. Current and emerging smart mobility practices include ridesharing, electric scooter (e-scooter) sharing, and truck platooning. For all these applications, hub locations, and therefore the overall network design, is crucial for operational and infrastructure efficiency and accessibility. The overarching goal of the proposed research program is to provide efficient and sustainable designs for smart mobility networks by optimizing hub locations and to develop the analytical tools and methods for data-driven decision support in hub network design. Short-term objectives focus on the optimization of hub locations for three different smart mobility applications: ridesharing, e-scooter sharing, and truck platooning. For each application, the corresponding optimization problems will be identified through analyzing real-life data and mathematical models will be developed based on information derived from those data. Decomposition methodologies will be implemented to develop efficient solution algorithms for these challenging combinatorial optimization problems. In addition to the deterministic variants, the hub location problems explored within this research program will also be modelled and solved under uncertainty, using stochastic and robust optimization techniques, to develop resilient hub networks. The developed models and algorithms will be tested and verified through key performance indicators on real-life datasets, including New York City taxi, Toronto, and Montreal bike-sharing data. The results from this research program will provide insights into the efficient design of smart mobility networks and will have an impact on the future of transportation. The developed models and algorithms will be shared through conferences and publications in respectable journals. Students trained in this program will graduate with sought-after skills in data science and optimization to work in both academia and the supply chain and logistics industry.