The Impacts on Fuel Consumption: A Data Mining-Based Analysis

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-41 Number-1
Year of Publication : 2016
Authors : Akash Iyengar, Karthigainathan.M, Kalpana.G
  10.14445/22312803/IJCTT-V41P108

MLA

Akash Iyengar, Karthigainathan.M, Kalpana.G "The Impacts on Fuel Consumption: A Data Mining-Based Analysis". International Journal of Computer Trends and Technology (IJCTT) V41(1):42-47, November 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
This paper suggests different driving techniques based on the results of an applied research on the eco-driving domain, supplemented by a huge data set produced from Delhi’s transport system. The data set is based on events automatically extracted from the control area network and enriched with information like GPS coordinates, weather and road data. We use online analytical processing (OLAP) and knowledge discovery (KD) techniques which handles the high volume of data and to determine the major factors that determine the average fuel consumption, and assist in classifying the drivers based on their driving efficiency. Our findings leads to the introduction of simple practices, such as optimal clutch, engine rotation, engine running in idle state and traffic precautions, can reduce fuel consumption on average from 3 to 5l/100 km, meaning a saving of thousands of litre of petrol per day. With the availability of traffic through various traffic sensors, a lot of research effort has been involved in developing traffic prediction techniques, which in turn improve route navigation, traffic minimisation etc. One key boon in traffic prediction is the reliability on prediction models that are constructed on the basis of historical data applied in real-time traffic situations, which may differ from that of the historical data and has a tendency to change over a period of time. We aim in obtaining and proving both short-term and long-term performance bounds for our online algorithm. The proposed algorithm also works effectively in scenarios where the realized traffic are missing or are available with a delay. In this paper we used a novel online framework that could learn from the current traffic situation in real-time and predict the future traffic by matching the current situation which will be useful for the effective prediction of fuel consumption and a suitable driving scheme.

References
[1] B. Pan, U. Demiryurek, and C. Shahabi, “Utilizing real-world transportation data for accurate traffic prediction,” ser. ICDM ’12, 2012, pp. 595–604.
[2] B. Pan, U. Demiryurek, C. Shahabi, and C. Gupta, “Forecasting spatiotemporal impact of traffic incidents on road networks,” in ICDM, 2013, pp. 587–596.
[3] W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xing, “Discovering spatio-temporal causal interactions in traffic data streams,” in KDD, 2011, pp. 1010–1018.
[4] S. Lee and D. B. Fambro, “Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting,” in TRR98, 1998.
[5] X. Li, Z. Li, J. Han, and J.-G. Lee, “Temporal outlier detection in vehicle traffic data,” in ICDE, 2009, pp. 13191322.
[6] M. Miller and C. Gupta, “Mining traffic incidents to forecast impact,” ser. UrbComp ’12. New York, NY, USA: ACM, 2012, pp. 33–40.
[7] L. Breiman, “Bagging predictors,” Machine learning, vol. 24, no. 2, pp. 123–140, 1996.
[8] D. H. Wolpert, “Stacked generalization,” Neural networks, vol. 5, no. 2, pp. 241–259, 1992.
[9] M. Sewell, “Ensemble learning,” RN, vol. 11, no. 02, 2008.
[10] P. B¨uhlmann and B. Yu, “Boosting with the l 2 loss: regression and classification,” Journal of the American Statistical Association, vol. 98, no. 462, pp. 324–339, 2003.
[11] Treatise, “Ecodriving—The Smart Driving Style,” Utrecht for the ECTREATISE project, Sep. 2005.
[12] J. MacLennan, B. Crivat, Z. Tang, “Data Mining With Microsoft SQL Server, 2008–2009. Hoboken, NJ, USA: Wiley.
[13] J. Almeida and J. Ferreira, “BUS public transportation system fuel efficiency patterns,” in Proc. 2nd IMLCS, Kuala Lumpur, Malaysia, 2013,pp. 4–8.
[14] E. F. Codd, Providing OLAP (On-Line Analytical Processing) to User-Analysts: An IT Mandate. Miami, FL, USA: E. F. Codd and Associates,1993.
[15] G. Mariscal, O. Marbán, and C. Fernández, “A survey of data mining and knowledge discovery process models and methodologies,” Knowl. Eng.Rev., vol. 25, no. 2, pp. 137–166, 2010.

Keywords
eco-driving, fuel efficiency, OlAP, KD, fuel consumption, traffic prediction big data, spatiotemporal, online learning.