RI: Dynamic Discrete Choice Networks -- An Artificial Intelligence Approach to Modeling Dynamic Travel Behavior

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Project SummaryThe goals of the proposed research are twofold: first, to advance the state of the art in artificial intelligence and cognitive sciences by developing novel probabilistic reasoning techniques; and second, to use these techniques in building better transportation models, which can then be used to help inform public deliberation regarding major infrastructure decisions. Problems of maintaining or replacing aging infrastructure, or adding new infrastructure to meet the needs of population growth and urban expansion of metropolitan areas, are becoming increasingly difficult to solve, in part because the cost is extremely large, and in part because the political discourse over alternative solutions is contentious and reflects divergent assumptions and values. Often, a major source of disagreement is cost; but another is rooted in differing assumptions about how people would adjust their travel in response to changed circumstances in both the short and long term, and how much congestion would result. Current transportation models used in operational analysis and planning are too behaviorally simple to be very useful in addressing these questions. Recent research advances have provided improvements in behavioral representation in these kinds of choice situations, but to date these nnovations are not integrated and are computationally not feasible for large-scale application. During the last decade, the artificial intelligence community has developed a set of techniques that enable fine-grained activity recognition from sensor data; among the most advanced and successful are approaches based on Dynamic Bayesian networks and statistical relational learning. The research team will build on this foundation, integrating these AI techniques with the Discrete Choice Models used in econometric approaches, to yield a new, hybrid reasoning system: Dynamic Discrete Choice Networks. This technique will be applied to the challenging domain of modeling dynamic travel choices of individuals, such as the number of trips, scheduled time of departure, destinations, modes, and routes and to predict how these choices change under dynamically updated travel conditions. Intellectual MeritThe merit of this proposal is grounded in the research challenges in the artificial intelligence and urban modeling areas. This project advances the state of the art in artificial intelligence and cognitive sciences by developing novel probabilistic reasoning techniques that are well suited for modeling the complex combinations of factors involved in human decision making in the commonsense domain of daily travel. By integrating this modeling power into probabilistic temporal models, Dynamic Discrete Choice Networks will provide an extremely general and flexible framework for learning and recognizing human activities from sensor data and for understanding how everyday human decision making adapts to a constantly changing environment.Broader ImpactsUrbanSim has the potential to significantly aid in public deliberation over major decisions regarding transportation replacement or expansion of transportation infrastructure, managing urban development, planning for response to mitigate the effects of events such as hurricane Katrina or a major earthquake, and other issues. UrbanSim is Open Source and freely available, and has already attracted considerable interest and use. Because of their improved ability to recognize and analyze human activities from raw sensor data, Dynamic Discrete Choice Networks will have applications to other significant domains as well, such as eldercare and long term health monitoring.