Abstract
An approach is described to apply the dynamic data-driven application systems (DDDAS) paradigm to reduce fuel consumption and emissions in surface transportation systems. This 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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Acknowledgments and Disclaimer
The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency - Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000613. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The contents do not necessarily reflect the official views or policies of the State of Georgia or any agency thereof. This report does not constitute a standard, specification, or regulation. We also thank the Air Force Office of Scientific Research and National Science Foundation for their support of this research (NSF Award 1,462,503 and AFOSR award FA9550-17-1-022).
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Hunter, M. et al. (2022). Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction. In: Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-74568-4_20
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