Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction

Michael Hunter; Aradhya Biswas; Bhargava Chilukuri; Angshuman Guin; Richard Fujimoto; Randall Guensler; Jorge Laval; Haobing Liu; Sabra Neal; Philip Pecher; Michael Rodgers

doi:10.1007/978-3-030-74568-4_20

Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction

Michael Hunter, Aradhya Biswas, Bhargava Chilukuri, Angshuman Guin, Richard Fujimoto, Randall Guensler, Jorge Laval, Haobing Liu, Sabra Neal, Philip Pecher, Michael Rodgers

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Handbook of Dynamic Data Driven Applications Systems
Subtitle of host publication	Volume 1: Second Edition
Publisher	Springer International Publishing
Pages	475-495
Number of pages	21
Volume	1
ISBN (Electronic)	9783030745684
ISBN (Print)	9783030745677
DOIs	https://doi.org/10.1007/978-3-030-74568-4_20
State	Published - Jan 1 2022
Externally published	Yes

Keywords

Distributed simulation
Energy and emissions modeling
Transportation systems
Vehicle activity monitoring

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-74568-4_20

Cite this

Hunter, M., Biswas, A., Chilukuri, B., Guin, A., Fujimoto, R., Guensler, R., Laval, J., Liu, H., Neal, S., Pecher, P., & Rodgers, M. (2022). Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction. In Handbook of Dynamic Data Driven Applications Systems: Volume 1: Second Edition (Vol. 1, pp. 475-495). Springer International Publishing. https://doi.org/10.1007/978-3-030-74568-4_20

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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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author = "Michael Hunter and Aradhya Biswas and Bhargava Chilukuri and Angshuman Guin and Richard Fujimoto and Randall Guensler and Jorge Laval and Haobing Liu and Sabra Neal and Philip Pecher and Michael Rodgers",

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Hunter, M, Biswas, A, Chilukuri, B, Guin, A, Fujimoto, R, Guensler, R, Laval, J, Liu, H, Neal, S, Pecher, P & Rodgers, M 2022, Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction. in Handbook of Dynamic Data Driven Applications Systems: Volume 1: Second Edition. vol. 1, Springer International Publishing, pp. 475-495. https://doi.org/10.1007/978-3-030-74568-4_20

Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction. / Hunter, Michael; Biswas, Aradhya; Chilukuri, Bhargava et al.
Handbook of Dynamic Data Driven Applications Systems: Volume 1: Second Edition. Vol. 1 Springer International Publishing, 2022. p. 475-495.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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T1 - Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction

AU - Hunter, Michael

AU - Biswas, Aradhya

AU - Chilukuri, Bhargava

AU - Guin, Angshuman

AU - Fujimoto, Richard

AU - Guensler, Randall

AU - Laval, Jorge

AU - Liu, Haobing

AU - Neal, Sabra

AU - Pecher, Philip

AU - Rodgers, Michael

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N2 - 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.

AB - 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.

KW - Distributed simulation

KW - Energy and emissions modeling

KW - Transportation systems

KW - Vehicle activity monitoring

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BT - Handbook of Dynamic Data Driven Applications Systems

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Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction

Abstract

Keywords

UN SDGs

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