Variational Inference for Agent-Based Models with Applications to Achieve Fuel Economy
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2017-10-29
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Edition:Final June 2016 – June 2017
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Abstract:In this project, we developed optimization and sampling based inference algorithms to track and predict real-time traffic dynamics in a city-scale transportation network from an agent-based transportation model and isolated observations of the trajectories of several hundred probe vehicles. We demonstrated the value of combining simulation modeling and big data in delivering travel information to drivers and promoting efficient driving through real world road networks and tracking data from mobile phones. This project integrates machine learning, big data, sensor networks, and agent-based transportation modeling to prototype an algorithm that combines the power of a model-driven approach with the power of big data, and promotes responsible driving by showing how different agent trips are associated with different travel time and fuel consumption.
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