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.
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- 1.
The sampling time of the GPS trajectory data is 6 s; the sampling time of the OBD-II data is 10 s.
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Both road network and physical features are extracted from OpenStreetMap, with more details can be found at www.openstreetmap.org.
- 3.
References
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)
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)
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)
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)
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)
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)
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)
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)
Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers behavior patterns from their digital traces. Comput. Environ. Urban Syst. 34(6), 541–548 (2010)
Saremi, F., et al.: Experiences with greengps—fuel-efficient navigation using participatory sensing. IEEE Trans. Mob. Comput. 15(3), 672–689 (2016)
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)
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)
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)
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_21
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)
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).
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Ding, Y., Chen, C., Xie, X., Yang, Z. (2019). Fuel Consumption Estimation of Potential Driving Paths by Leveraging Online Route APIs. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_7
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DOI: https://doi.org/10.1007/978-3-030-15093-8_7
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