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Fuel Consumption Estimation of Potential Driving Paths by Leveraging Online Route APIs

  • Yan Ding
  • Chao ChenEmail author
  • Xuefeng Xie
  • Zhikai Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

Greenhouse gas and pollutant emissions generated by an increasing number of vehicles have become a significant problem in modern cities. Estimating fuel usage of potential driving paths can help drivers choose fuel-efficient paths to save more energy and protect the environment. In this paper, we build a fuel consumption model (FCM) for drivers based on their historical GPS trajectory and OBD-II data. FCM on a path only needs three parameters (i.e., the path distance, traveling time on the path and path curvature), which can be easily obtained from online route APIs. Based on experiment results, we can conclude that the proposed model can achieve high accuracy, with a mean fuel consumption error of less than 10% for paths longer than 15 km. In addition, the traveling time on paths provided by online route APIs is accurate and can be input into FCM to estimate the fuel usage of paths.

Keywords

GPS trajectories OBD-II Online route APIs Fuel consumption model 

Notes

Acknowledgments

The work was supported by the National Science Foundation of China (No. 61602067), the Fundamental Research Funds for the Central Universities (No. 106112017cdjxy180001), Chongqing Basic and Frontier Research Program (No. cstc2015jcyjA00016), Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University).

References

  1. 1.
    Castro, P.S., Zhang, D., Chen, C., Li, S., Pan, G.: From taxi GPS traces to social and community dynamics: a survey. ACM Comput. Surv. (CSUR) 46(2), 17 (2013)CrossRefGoogle Scholar
  2. 2.
    Chen, C., Jiao, S., Zhang, S., Liu, W., Feng, L., Wang, Y.: Tripimputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Trans. Intell. Transp. Syst. 99, 1–13 (2018)Google Scholar
  3. 3.
    Chen, C., et al.: Crowddeliver: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans. Intell. Transp. Syst. 18(6), 1478–1496 (2017)Google Scholar
  4. 4.
    Chen, C., Zhang, D., Zhou, Z.-H., Li, N., Atmaca, T., Li, S.: B-planner: night bus route planning using large-scale taxi GPS traces. In: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 225–233. IEEE (2013)Google Scholar
  5. 5.
    Chen, H., Guo, B., Yu, Z., Chin, A., Tian, J., Chen, C.: Which is the greenest way home? A lightweight eco-route recommendation framework based on personal driving habits. In: 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), pp. 187–194. IEEE (2016)Google Scholar
  6. 6.
    Ding, Y., Chen, C., Zhang, S., Guo, B., Yu, Z., Wang, Y.: Greenplanner: planning personalized fuel-efficient driving routes using multi-sourced urban data. In: 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 207–216. IEEE (2017)Google Scholar
  7. 7.
    Ganti, R.K., Pham, N., Ahmadi, H., Nangia, S., Abdelzaher, T.F.: GreenGPS: a participatory sensing fuel-efficient maps application. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 151–164. ACM (2010)Google Scholar
  8. 8.
    Li, Y., Yiu, M.L.: Route-saver: leveraging route apis for accurate and efficient query processing at location-based services. IEEE Trans. Knowl. Data Eng. 27(1), 235–249 (2015)CrossRefGoogle Scholar
  9. 9.
    Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers behavior patterns from their digital traces. Comput. Environ. Urban Syst. 34(6), 541–548 (2010)CrossRefGoogle Scholar
  10. 10.
    Saremi, F., et al.: Experiences with greengps—fuel-efficient navigation using participatory sensing. IEEE Trans. Mob. Comput. 15(3), 672–689 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Shang, J., Zheng, Y., Tong, W., Chang, E., Yu, Y.: Inferring gas consumption and pollution emission of vehicles throughout a city. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1027–1036. ACM (2014)Google Scholar
  12. 12.
    Wang, L., Zhang, D., Wang, Y., Chen, C., Han, X., M’hamed, A.: Sparse mobile crowdsensing: challenges and opportunities. IEEE Commun. Mag. 54(7), 161–167 (2016)CrossRefGoogle Scholar
  13. 13.
    Yu, Z., Xu, H., Yang, Z., Guo, B.: Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Trans. Hum. Mach. Syst. 46(1), 151–158 (2016)CrossRefGoogle Scholar
  14. 14.
    Zhang, D., Chow, C.-Y., Li, Q., Zhang, X., Xu, Y.: Efficient evaluation of k-NN queries using spatial mashups. In: Pfoser, D., et al. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 348–366. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22922-0_21CrossRefGoogle Scholar
  15. 15.
    Zhang, J., Zhao, Y., Xue, W., Li, J.: Vehicle routing problem with fuel consumption and carbon emission. Int. J. Prod. Econ. 170, 234–242 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer ScienceChongqing UniversityChongqingChina
  2. 2.School of Media and CommunicationUniversity of LeedsLeedsEngland

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