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ETCPS: An Effective and Scalable Traffic Condition Prediction System

  • Dong Wang
  • Wei Cao
  • Mengwen Xu
  • Jian LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)

Abstract

Real-time prediction of the traffic condition is an important ingredient for a variety of applications. In this paper, we propose an Ensemble based Traffic Condition Prediction System (ETCPS) for predicting the traffic conditions of any roads in a city based on the current and historical GPS data collected from floating vehicles. We have observed two useful correlations in the traffic condition time series, which are the bases of our design. In order to exploit these two correlations for prediction, we propose two different models called Predictive Regression Tree (PR-Tree) and Spatial Temporal Probabilistic Graphical Model (STPGM). Our best quality prediction is achieved by a careful ensemble of the two models. Our system provides high-quality prediction and can easily scale to very large datasets. We conduct extensive experimental evaluations with a large GPS data set collected from more than 12,000 taxis in Beijing during two months. The experimental results demonstrate the effectiveness, efficiency, and scalability of our system.

Keywords

Road Network Leaf Node Road Segment Traffic Condition Discrete Time Markov Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This work was supported in part by the National Basic Research Program of China grants 2015CB358700, 2011CBA00300, 2011CBA00301, and the National NSFC grants 61033001, 61361136003.

References

  1. 1.
    Asghari, M., Emrich, T., Demiryurek, U., Shahabi, C.: Probabilistic estimation of link travel times in dynamic road networks. In: ACM SIGSPATIAL (2015)Google Scholar
  2. 2.
    Chu, V.W., Wong, R.K., Liu, W., Chen, F.: Causal structure discovery for spatio-temporal data. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014, Part I. LNCS, vol. 8421, pp. 236–250. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  3. 3.
    Hofleitner, A., Herring, R., Abbeel, P., Bayen, A.: Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network. IEEE Trans. Intell. Transp. Syst. 13(4), 1679–1693 (2012)CrossRefGoogle Scholar
  4. 4.
    Hunter, T., Herring, R., Abbeel, P., Bayen, A.: Path and travel time inference from GPS probe vehicle data. NIPS Anal. Netw. Learn. Graphs 12(1) (2009)Google Scholar
  5. 5.
    Kwon, J., Murphy, K.: Modeling freeway traffic with coupled HMMs. Technical report, University of California, Berkeley (2000)Google Scholar
  6. 6.
    Leontiadis, I., Marfia, G., Mack, D., Pau, G., Mascolo, C., Gerla, M.: On the effectiveness of an opportunistic traffic management system for vehicular networks. IEEE Trans. Intell. Transp. Syst. 12(4), 1537–1548 (2011)CrossRefGoogle Scholar
  7. 7.
    Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 352–361. ACM (2009)Google Scholar
  8. 8.
    Ma, S., Zheng, Y., Wolfson, O.: T-share: a large-scale dynamic taxi ridesharing service. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 410–421. IEEE (2013)Google Scholar
  9. 9.
    Ramezani, M., Geroliminis, N.: On the estimation of arterial route travel time distribution with Markov chains. Transp. Res. Part B: Methodol. 46(10), 1576–1590 (2012)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Yang, B., Guo, C., Jensen, C.S.: Travel cost inference from sparse, spatio temporally correlated time series using Markov models. Proc. VLDB Endow. 6(9), 769–780 (2013)CrossRefGoogle Scholar
  12. 12.
    Yeon, J., Elefteriadou, L., Lawphongpanich, S.: Travel time estimation on a freeway using discrete time Markov chains. Transp. Res. Part B: Methodol. 42(4), 325–338 (2008)CrossRefGoogle Scholar
  13. 13.
    Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2013)CrossRefGoogle Scholar
  14. 14.
    Yuan, J., Zheng, Y., Zhang, C., Xie, X., Sun, G.Z.: An interactive-voting based map matching algorithm. In: Proceedings of the 2010 Eleventh International Conference on Mobile Data Management, pp. 43–52. IEEE Computer Society (2010)Google Scholar
  15. 15.
    Zheng, W., Lee, D.H., Shi, Q.: Short-term freeway traffic flow prediction: Bayesian combined neural network approach. J. Transp. Eng. 132(2), 114–121 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Interdisciplinary Information Sciences, Tsinghua UniversityBeijingChina

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