Skip to main content
SpringerLink
Log in

Statistical methods to estimate vehicle count using traffic cameras

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Traffic camera has played an important role in enabling intelligent and real-time traffic monitoring and control. In this paper, we focus on establishing a correlation model for the traffic cameras’ vehicle counts and increase the spatial-resolution of a city’s vehicle counting traffic camera system by means of correlation-based estimation. We have developed two methods for constructing traffic models, one using statistical machine learning based on Gaussian models and the other using analytical derivation from the origin-destination (OD) matrix. The Gaussian-based method outperforms existing correlation coefficient based methods. When training data are not available, our analytical method based on OD matrix can still perform well. When there is only a limited number of cameras, we develop heuristic algorithms to determine the most desirable locations to place the cameras so that the errors of traffic estimations at the locations without traffic cameras are minimized. We show some improvements in the performance of our proposed methods over an existing method in a variety of simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bas, E., Tekalp, A. M., & Salman, F. S. (2007). Automatic vehicle counting from video for traffic flow analysis. In Proc. IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, Jun. 2007.

  • Beymer, D., McLauchlan, P., Coifman, B., & Malik, J. (1997). A real-time computer vision system for measure traffic parameters. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, pp. 496–501, Jun. 1997.

  • Cucchiara ., Piccardi ., Mello P. (2000) Image analysis and rule-based reasoning for a traffic monitoring system. IEEE Transactions Intelligent Transportation Systems 1(2): 119–130

    Article  Google Scholar 

  • Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J., & Hong, W. (2004). Model-driven data acquisition in sensor networks. In Proceedings of the 30th VLDB Conference, Toronto, Canada, Aug. 2004.

  • Guestrin, C., Krause, A., & Singh, A. P. (2005). Near-optimal sensor placements in gaussian processes. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, Aug. 2005.

  • Guitton, A., Skordylis, A., & Trigoni, N. (2007). Correlation-based data dissemination in traffic monitoring sensor networks. In Proceedings of IEEE Wireless Communications and Networking Conference, Hong Kong, China, Mar. 2007.

  • Gupte S., Masoud O., Martin R.F.K., Papanikolopoulos N.P. (2002) Detection and classification of vehicles. IEEE Transactions Intelligent Transportation Systems 3(1): 37–47

    Article  Google Scholar 

  • Hsieh J.W., Yu S.H., Chen Y.S., Hu W.F. (2006) Automatic traffic surveillance system for vehicle tracking and classification. IEEE Transactions Intelligent Transportation Systems 7(2): 175–187

    Article  Google Scholar 

  • Hsu, W. L., Liao, H. Y., Jeng, B. S., & Fan, K. C. (2004). Real-time traffic parameter extraction using entropy. In IEEE Proc. Vision, Image and Signal Processing, Vol. 151, pp. 194–202.

  • Krause, A., Guestrin, C., Gupta, A., & Kleinberg, J. (2006). Near-optimal sensor placements: Maximizing information while minimizing communication cost. In Proceedings of the fifth International Conference on Information Processing in Sensor Networks, Nashville, TN, Apr. 2006.

  • Krause, A., Horvitz, E., Kansal, A., & Zhao, F. (2008). Community sensing: Foundations and applications. In International Conference on Information Processing in Sensor Networks, Apr. 2008.

  • Rajagopal, R., Nguyen, X., Ergen, S., & Varaiya, P. (2008). Distributed online simultaneous fault detection for multiple sensors. In International Conference on Information Processing in Sensor Networks, Apr. 2008.

  • Schiff, J., Antonelli, D., Dimakis, A. G., Chu, D., & Wainwright, M. J. (2007). Robust message-passing for statistical inference in sensor networks. In Proceedings of the 6th International Conference on Information processing in Sensor Networks, Cambridge, MA, pp. 109–118, Apr. 2007.

  • Schrank, D., & Lomax, T. (2007). Urban mobility report.

  • Spiess H. (1987) A maximal likelihood model for estimating origin-destination matrices. Transportation Research 21B: 395–412

    Google Scholar 

  • Sun S., Zhang C., Yu G. (2006) A bayesian network approach to traffic flow forecasting. IEEE Transcations of Intelligent Transportation Systems 7(1): 124–132

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Missouri, Columbia, MO, USA

    Peng Zhuang & Yi Shang

  2. Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Bei Hua

Authors
  1. Peng Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bei Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Zhuang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhuang, P., Shang, Y. & Hua, B. Statistical methods to estimate vehicle count using traffic cameras. Multidim Syst Sign Process 20, 121–133 (2009). https://doi.org/10.1007/s11045-008-0068-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-008-0068-x

Keywords

Search

Navigation