Abstract
The road transportation sector is responsible for 87 % of the human \(CO_2\) emissions. The estimation and prediction of fuel consumption plays a key role in the development of systems that foster the reduction of those emissions through trip planing. In this paper, we present a predictive regression model of instantaneous fuel consumption for diesel and gasoline light-duty vehicles, based on their instantaneous speed and acceleration and on road inclination. The parameters are extracted from GPS data, thus the models do not require data from dedicated vehicle sensors. We use data collected by 17 drivers during their daily commutes using the SenseMyCity crowdsensor.
We perform an empyrical comparison of several regression algorithms for prediction across trips of the same vehicle and for prediction across vehicles. The results show that models trained for a vehicle show similar RMSE when are applied to other vehicles with similar characteristics. Relying on these results, we propose fuel type specific models that provide an accurate prediction for vehicles with similar characteristics to those on which the models were trained.
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References
Rodrigues, J.G.P., Aguiar, A., Barros, J.: SenseMyCity: Crowdsourcing an Urban Sensor. ArXiv e-prints (2014)
EU Transport in Figures - Statistical Pocket Book. European Commission, Directorate-General for Energy and Transport, in co-operation with Eurostat (2000)
CO2 Emissions from Fuel Combustion 2012. International Energy Agency. Paris: Organisation for Economic Co-operation and Development (2012)
Ribeiro, V., Rodrigues, J., Aguiar, A.: Mining geographic data for fuel consumption estimation. In: 16th International IEEE Annual Conference on Intelligent Transportation Systems (2013)
Bowyer, D.P., Akçelik, R., Biggs, D.C.: Guide to fuel consumption analysis for urban traffic management. Special Report SR No. 32. ARRB Transport Research Ltd, Vermont South, Australia (1985)
Joumard, R., Jost, P., Hassel, D.: Hot passenger car emissions modelling as a function of instantaneous speed and acceleration. Sci. Total Environ. 169, 129–139 (1995)
Jimenez-Palacios, J.L.: Understanding and Quantifying Motor Vehicle Emissions with Vehicle Specific Power and TILDAS Remote Sensing. Massachusetts Institute of Technology, Cambridge (1999)
Cappiello, A., Chabini, I., Nam, E.K., Luè, A., Zeid, M.A.: A statistical model of vehicle emissions and fuel consumption. In: IEEE 5th International Conference on Intelligent Transportation Systems (2002)
Rakha, H., Ahn, K., Trani, A.: Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions. Transp. Res. Part D: Transp. Environ. 9(1), 4974 (2004)
Lei, W., Chen, H., Lu, L.: Microscopic emission and fuel consumption modeling for light-duty vehicles using portable emission measurement system data. World Acad. Sci. Eng. Technol. 42, 918–925 (2010)
Pelkmans, L., Debal, P., Hood, T., Hauser, G., Delgado, M.R.: Development of a simulation tool to calculate fuel consumption and emissions of vehicles operating in dynamic conditions. SAE Technical Paper (2004)
Ericsson, E., Larsson, H., Brundell-Freij, K.: Optimizing route choice for lowest fuel consumption Potential effects of a new driver support tool. Transp. Res. Part C 14, 369–383 (2006)
Song, G., Yu, L., Wang, W.: Aggregate fuel consumption model of light-duty vehicles for evaluating effectiveness of traffic management strategies on fuels. J. Transp. Eng. 135, 611–618 (2009)
Tavares, G., Zsigraiova, Z., Semiao, V., Carvalho, M.G.: Optimisation of MSW collection routes for minimum fuel consumption using 3D GIS modelling. Waste Manage. 29, 1176–1185 (2009)
André, M., Keller, M., Sjödin, A., Gadrat, M., Mc Crae, I., Dilara, P.: The ARTEMIS European tools for estimating the transport pollutant emissions. In: 18th International Emission Inventories Conference, pp. 1–10 (2009)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers, San Francisco (2011)
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Vilaça, A., Aguiar, A., Soares, C. (2015). Estimating Fuel Consumption from GPS Data. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_75
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DOI: https://doi.org/10.1007/978-3-319-19390-8_75
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