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:10.1007/978-3-319-95504-9_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

2 Scopus citations

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.

Original language	English
Title of host publication	Handbook of Dynamic Data Driven Applications Systems
Publisher	Springer International Publishing
Pages	467-487
Number of pages	21
ISBN (Electronic)	9783319955049
ISBN (Print)	9783319955032
DOIs	https://doi.org/10.1007/978-3-319-95504-9_20
State	Published - Nov 13 2018
Externally published	Yes

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

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10.1007/978-3-319-95504-9_20

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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. (2018). Energy-aware dynamic data-driven distributed traffic simulation for energy and emissions reduction. In Handbook of Dynamic Data Driven Applications Systems (pp. 467-487). Springer International Publishing. https://doi.org/10.1007/978-3-319-95504-9_20

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title = "Energy-aware dynamic data-driven distributed traffic simulation for energy and emissions reduction",

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.",

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",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018. All rights are reserved.",

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doi = "10.1007/978-3-319-95504-9_20",

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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 2018, Energy-aware dynamic data-driven distributed traffic simulation for energy and emissions reduction. in Handbook of Dynamic Data Driven Applications Systems. Springer International Publishing, pp. 467-487. https://doi.org/10.1007/978-3-319-95504-9_20

TY - CHAP

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

PY - 2018/11/13

Y1 - 2018/11/13

N2 - 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.

AB - 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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U2 - 10.1007/978-3-319-95504-9_20

DO - 10.1007/978-3-319-95504-9_20

M3 - Chapter

AN - SCOPUS:85077786890

SN - 9783319955032

SP - 467

EP - 487

BT - Handbook of Dynamic Data Driven Applications Systems

PB - Springer International Publishing

ER -

Energy-aware dynamic data-driven distributed traffic simulation for energy and emissions reduction

Abstract

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