CAREER: Spatial-Temporal Imitation Learning

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Humans make daily decisions based on their own "strategies" (such as taxi drivers' passenger-seeking processes and commuters' transit mode choices). Understanding and incorporating human decision-making strategies will bring significant benefits to the growing gig-worker population and transportation marketplace. For example, learning the decision-making strategies from taxi drivers, personal vehicle drivers, and urban commuters can facilitate the service providers (e.g., taxi/ride-hailing companies) to better serve the passengers, and enable the urban planners to design better road networks and transit routes to meet the needs of urban travelers. The goal of this project is to develop, implement, and evaluate a unified framework to learn the decision-making strategies of human agents from their generated mobility data, with applications to explain and incentivize their decisions to promote individual and societal well-being. Moreover, in this project, the investigator will integrate research, education, and outreach by developing new courses for both undergraduate and graduate students, reaching out to K-12 students, and engaging women and underrepresented minorities.There are several technical challenges to learn human decision-making strategies from their mobility data: Human decision-making strategies may vary over time and space, i.e., spatial-temporal dynamics challenge. The mobility data collected may cover only a part of the spatial regions and time periods, i.e., spatial-temporal sparsity challenge. Most human agents are not "experts", thus the generated mobility data are noisy and uncertain, i.e., non-expert challenge. A large number of human agents interact with each other when making decisions, i.e., interaction and scalability challenge. Human decisions are governed by many (sometimes hidden) factors, which make it hard to infer explainable information from their decision-making strategies, i.e., explainability challenge. Human agents have diverse reactions to offered incentives, making it hard to design targeted incentive mechanisms to consider both agents' inherent decision-making strategies and their online feedback, i.e., incentive design challenge. This project will address these research challenges, and include the development of a novel spatial-temporal imitation learning framework for learning decision-making strategies from individual and interactive human agents, and an interactive system that provides human agents with explainable learning results and online incentives to promote their decision-making strategies. The spatial-temporal imitation learning framework and associated algorithms have the potential to be transformative in both the data and urban sciences by enabling efficient and accurate discovery of human decision-making strategies from their mobility data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.