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Detecting Taxi Speeding from Sparse and Low-Sampled Trajectory Data

  • Xibo ZhouEmail author
  • Qiong Luo
  • Dian Zhang
  • Lionel M. Ni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)

Abstract

Taxis are a major means of public transportation in large cities, and speeding is a common problem among motor vehicles, including taxis. Unless caught by sensors or patrol officers, many speeding incidents go unnoticed, which pose potential threat to road safety. In this paper, we propose to detect speeding behaviors of individual taxis from taxi trajectory data. Such detection results are useful for driver risk analysis and road safety management. However, the taxi trajectory data are geographically sparse and the sample rate is low. Furthermore, existing methods mainly deal with the estimation of collective road speeds whereas we focus on the speeds of individual vehicles. As such, we propose to use a two-fold collective matrix factorization (CMF)-based model to estimate the individual vehicle speed. We have evaluated our method on real-world datasets, and the results show the effectiveness of our method in detecting taxi speeding behaviors.

Keywords

Speeding Collective matrix factorization Trajectory 

Notes

Acknowledgments

This work is supported in part by the Guangdong Pre-national project 2014GKXM054 and the Guangdong Province Key Laboratory of Popular High Performance Computers 2017B030314073.

References

  1. 1.
    Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?-arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7(3), 1247–1250 (2014)CrossRefGoogle Scholar
  2. 2.
    Jenelius, E., Koutsopoulos, H.N.: Travel time estimation for urban road networks using low frequency probe vehicle data. Transp. Res. Part B: Methodol. 53, 64–81 (2013)CrossRefGoogle Scholar
  3. 3.
    Liu, Y., Li, Z.: A novel algorithm of low sampling rate GPS trajectories on map-matching. EURASIP J. Wirel. Commun. Netw. 2017(1), 30 (2017)CrossRefGoogle Scholar
  4. 4.
    Tseng, C.-M.: Operating styles, working time and daily driving distance in relation to a taxi driver’s speeding offenses in Taiwan. Accid. Anal. Prev. 52, 1–8 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 25–34. ACM (2014)Google Scholar
  6. 6.
    Wang, Z., Li, M., Wang, L., Liu, X.: Estimation trajectory of the low-frequency floating car considering the traffic control. Math. Prob. Eng. 2013, 11 (2013)Google Scholar
  7. 7.
    Xin, X., Lu, C., Wang, Y., Huang, H.: Forecasting collector road speeds under high percentage of missing data. In: AAAI, pp. 1917–1923 (2015)Google Scholar
  8. 8.
    Xu, J., Deng, D., Demiryurek, U., Shahabi, C., van der Schaar, M.: Mining the situation: spatiotemporal traffic prediction with big data. IEEE J. Sel. Top. Sig. Process. 9(4), 702–715 (2015)CrossRefGoogle Scholar
  9. 9.
    Ypma, T.J.: Historical development of the Newton-Raphson method. SIAM Rev. 37(4), 531–551 (1995)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zhou, X., Ding, Y., Tan, H., Luo, Q., Ni, L.M.: HIMM: an HMM-based interactive map-matching system. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10178, pp. 3–18. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55699-4_1CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xibo Zhou
    • 1
    • 2
    • 3
    • 4
    Email author
  • Qiong Luo
    • 1
  • Dian Zhang
    • 4
  • Lionel M. Ni
    • 5
  1. 1.Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyClear Water BayHong Kong
  2. 2.Guangzhou HKUST Fok Ying Tung Research InstituteThe Hong Kong University of Science and TechnologyClear Water BayHong Kong
  3. 3.Guangdong Key Laboratory of Popular High Performance ComputersShenzhenChina
  4. 4.Shenzhen Key Laboratory of Service Computing and ApplicationsShenzhenChina
  5. 5.University of MacauMacauChina

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