Abstract
The Chapter describes an approach of the dynamic data-driven applications systems (DDDAS) paradigm to reduce fuel consumption and emissions in surface transportation systems. The approach includes algorithms and distributed simulations to predict space-time trajectories of onroad vehicles. Given historical and real-time measurement data from the road network, computation resources residing in the vehicle generate speed/acceleration profiles used to estimate fuel consumption and emissions. These predictions are used to suggest energy-efficient routes to the driver. Because many components of the envisioned DDDAS system operate on mobile computing devices, a distributed computing architecture and energy-efficient middleware and simulations are proposed to maximize battery life. Energy and emissions modeling and mobile client power measurements are also discussed.
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