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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
US Energy Information Admin, http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=10
F, Darema, Dynamic data driven applications systems: A new paradigm for application simulations and measurements, in Computational Science-ICCS 2004 (Springer, Berlin Heidelberg, Kraków, Poland, 2004) pp. 662–669.
H. Chen, J. Wang, L. Feng, Research on the dynamic data-driven application system architecture for flight delay prediction. J. Softw. 7(2), 263–268 (2012)
Q. Long, A framework for data-driven computational experiments of inter-organizational collaborations in supply chain networks. Inf. Sci. 399, 43–63 (2017)
J. Mandel, L. Bennethum, M. Chen, J. Coen, C. Douglas, L. Franca, C. Johns, M. Kim, A. Knyazev, R. Kremens, Towards a dynamic data driven application system for wildfire simulation, in Computational Science–ICCS 2005 (Springer-Verlag Berlin Heidelberg, Atlanta, GA, 2005) pp. 197–227
D. Henclewood, W. Suh, A. Guin, R. Guensler, R.M. Fujimoto, M. Hunter, A real-time data-driven traffic simulation for performance measure estimation. IET Intell. Transp. Syst. 10(8), 562–571 (2016)
W. Suh, D. Henclewood, G. Angshuman, R. Guensler, M. Hunter, R.M. Fujimoto, Dynamic data driven transportation systems. Multimed Tools Appl. 76(23), 25253–25269 (2017)
R. Guensler, H. Liu, X. Xu, Y. Xu, M. Rodgers, MOVES-Matrix: setup, implementation, and application, in 95th Annual Meeting of the Transportation Research Board (Washington, DC, 2016)
J. Von Neumann, A.W. Burks, Theory of self-reproducing automata. IEEE Trans. Neural Netw. 5(1), 3–14 (1966)
K. Nagel, M. Schreckenberg, A cellular automaton model for freeway traffic. J. Phys. I 2(12), 2221–2229 (1992)
M.J. Lighthill, G.B. Whitham, On kinematic waves. II. A theory of traffic flow on long crowded roads, in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 229, no. 1178 (The Royal Society, London, 1955) pp. 317–345
P.I. Richards, Shock waves on the highway. Oper. Res. 4(1), 42–51 (1956)
C.F. Daganzo, In traffic flow, cellular automata= kinematic waves. Transp. Res. B Methodol. 40(5), 396–403 (2006)
NGSIM Community Home, http://ngsim-community.org
US EPA, U.S. Environmental protection agency: population and activity of on-road vehicles in MOVES2014. EPA Report EPA-420-R-16-003a (2015). Available at: https://www3.epa.gov/otaq/models/moves/documents/420r16003a.pdf
A. Savitzky, M.J. Golay, Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)
R.M. Fujimoto, A. Biswas, An empirical study of energy consumption in distributed simulations, in IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications (DS/RT) (2015).
S. Neal, G. Kanitkar, R.M. Fujimoto, Power consumption of data distribution management for on-line simulations, Principles of Advanced Discrete Simulation, May 2014.
The Apache Software Foundation, httpd.apache.org
A. Beilis, M. Tonkikh, http://cppcms.com/wikipp/en/page/main
A. Biswas, R. Fujimoto, Profiling energy consumption in distributed simulations, in Proceedings of the 2016 Annual ACM Conference on SIGSIM Principles of Advanced Discrete Simulation. (ACM, 2016)
M. Rickert, K. Nagel, M. Schreckenberg, A. Latour, Two lane traffic simulations using cellular automata. Physica A. 231(4), 534–550 (1996)
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 1462503 and AFOSR award FA9550-17-1-022).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hunter, M. et al. (2018). Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-95504-9_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95503-2
Online ISBN: 978-3-319-95504-9
eBook Packages: Computer ScienceComputer Science (R0)