A reinforcement learning framework for strategic, long-term urban transportation planning.
2022 BTR Conference. Aug. 4, 2022
Modeling and managing metropolitan power-mobility joint systems
2022 BTR Conference. Aug. 4, 2022
Modeling & simulation platform for mobility-energy joint system planning.
First panelist for the session "Preparing for the zero-emission paradigm shift."
2022 ITS-CA annual meeting. May, 2022
This video demonstrates a modeling and simulation platform I developed for studying the operation strategies of the transportation-energy joint system using a hybrid of agent-based, system dynamics, and event-based modeling paradigms.
This video demonstrates a modeling and simulation platform I developed for operating energy-transportation joint systems. The model is built on the system dynamics modeling paradigm.
This video shows a simulation of a high-resolution multi-modal traffic (passenger trains, motor vehicles, and pedestrians) dynamics. The model is currently used as a testbed for developing inverse multi-agent reinforcement learning algorithms for model calibration.
This video is a record of a presentation I gave at the 2021 System Dynamics Conference on the utilization and generalization of the sequential sampling and accumulation-to-threshold principles for simultaneously capturing decision and decision time distributions.
The associated paper can be found at https://doi.org/10.1002/sdr.1712
This video is a record of a presentation I gave as a invited panelist on a generalized diffusion model my collaborator, Dr. Michael F. Hyland and I developed for capturing preference and response time in ordering mobility-on-demand services. The associated paper can be found at https://doi.org/10.1016/j.trc.2020.102854
In 2021 April, I presented as a guest lecture on the potential of applying quantum cognition models and quantum computing techniques in civil and environmental engineering.
