KSCE Journal of Civil Engineering

, Volume 23, Issue 2, pp 810–820 | Cite as

Traffic Condition Monitoring with SCAAT Kalman Filter-based Data Fusion in Toronto, Canada

  • Young-Ji Byon
  • Amer Shalaby
  • Baher Abdulhai
  • Chung-Suk Cho
  • Hwasoo YeoEmail author
  • Samah El-Tantawy
Transportation Engineering


For a particular section of a road network, there are multiple sources of quantitative and qualitative traffic information. Quantitative sensors are usually hardware-based, including loop detectors and GPS devices that produce numerical data. Qualitative sensors are usually processed data, including the traffic department’s websites and radio broadcasts that produce subjective categorical data based on hidden processes. Each sensor is characterized by a specific level of error and sampling frequency. It is a challenge to combine and utilize multiple sources of data for estimating real-time traffic conditions. By using Single-Constraint-At-A-Time (SCAAT) Kalman filters, this paper combines multiple data sources from a section of a highway. However, in real-life, true traffic conditions are unknown because all sensors have associated errors with them. A micro-simulation package is used in order to have access to the true traffic conditions of a simulated environment that has been calibrated for a particular road section in Toronto. Then, the performance of predictions made by the developed SCAAT filters are compared with the true traffic conditions under different sampling strategies with varying number of probes and varying sampling frequencies. SCAAT filters are found to be effective for fusing the data and estimating current traffic conditions.


data fusion kalman filter ITS traffic monitoring travel time 


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  1. Bar-Gera, H. (2007). “Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times.” Transportation Research Part C: Emerging Technologies, Vol. 15, No. 6, pp. 380–391, DOI: 10.1016/j.trc.2007.06.003.CrossRefGoogle Scholar
  2. Byon, Y. J., Abdulhai, B., and Shalaby, A. (2009). “Real-time transportation mode detection via tracking global positioning system mobile devices.” Journal of Intelligent Transportation Systems, Vol. 13, No. 4, pp. 161–170, DOI: 10.1080/15472450903287781.CrossRefGoogle Scholar
  3. Byon, Y. J., Cortés, C. E., Jeong, Y. S., Martínez, F. J., Munizaga, M. A., and Zúñiga, M. (2018). “Bunching and headway adherence approach to public transport with GPS.” International Journal of Civil Engineering, Vol. 16, No. 6, pp. 647–658, DOI: 10.1007/s40999-017-0153-3.CrossRefGoogle Scholar
  4. Byon, Y. J., Ha, J. S., Cho, C. S., Kim, T. Y., and Yeun, C. Y. (2017). “Real-time transportation mode identification using artificial neural networks enhanced with mode availability layers: A case study in Dubai.” Applied Sciences, Vol. 7, No. 9, pp.923, DOI: 10.3390/app7090923.CrossRefGoogle Scholar
  5. Byon, Y. J. and Liang, S. (2014). “Real-time transportation mode detection using smartphones and artificial neural networks: Performance comparisons between smartphones and conventional global positioning system sensors.” Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 18, No. 3, pp. 264–272, DOI: 10.1080/15472450.2013.824762.CrossRefGoogle Scholar
  6. Byon, Y. J., Shalaby, A., and Abdulhai, B. (2006). “Travel time collection and traffic monitoring via GPS technologies.” Proc. Intelligent Transportation Systems Conference, ITSC ’06. IEEE, Toronto, Canada, pp. 677–782, DOI: 10.1109/ITSC.2006.1706820.CrossRefGoogle Scholar
  7. Chen, J., Low, K. H., Yao, Y., and Jailet, P. (2015). Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems.” IEEE Transactions on Automation Science and Engineering, Vol. 12, No. 3, pp. 901–921, DOI: 10.1109/TASE.2015.2422852.CrossRefGoogle Scholar
  8. El-Tantawy, S., Abdulhai, B., and Abdelgawad, H. (2013). Multiagent reinforcement learning for integrate network of adaptive traffic signal controllers (MARLIN-ATSC): Methodology and large-scale application on downtown Toronto.” IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 3, pp. 1140–1150, DOI: 10.1109/TITS.2013.2255286.CrossRefGoogle Scholar
  9. Herrera, J. C. and Bayen, A. M. (2008). “Traffic flow reconstruction using mobile sensors and loop detector data.” Proc. 87th Transportation Research Board Annual Meeting, Washington D.C., USA, p. 18, #08-1868.Google Scholar
  10. Ki, Y. K. and Baik, D. K. (2006). “Model for accurate speed measurement using double-loop detectors.” IEEE Transactions on Vehicular Technology, Vol. 55, No. 4, pp. 1094–1101, DOI: 10.1109/TVT.2006.877462.CrossRefGoogle Scholar
  11. Kong, Q. J., Li, Z., Chen, Y., and Liu, Y. (2009). “An approach to urban traffic state estimation by fusing multisource information.” IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 3, pp. 499–511, DOI: 10.1109/TITS.2009.2026308.CrossRefGoogle Scholar
  12. Kwon, J., Petty, K., and Varaiya, P. (2007). “Probe vehicle runs or loop detectors?: Effect of detector spacing and sample size on accuracy of freeway congestion monitoring.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2012, pp. 57–63, DOI: 10.3141/2012-07.CrossRefGoogle Scholar
  13. Li, R.Y., Liang, S. H. L., Lee, D. W., and Byon, Y. J. (2012). “TrafficPulse: A mobile GISystem for transportation.” Proc. 1st ACM SIGSPATIAL Int. Workshop on Mobile Geographic Inf. Systems, MobiGIS 2012 - In Conjunction with the 20th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Inf. Systems, GIS 2012, Redondo Beach, USA, pp. 9–16, DOI: 10.1145/2442810.2442813.Google Scholar
  14. Oh, S., Byon, Y. J., Jang, K., and Yeo, H. (2015). “Short-term traveltime prediction on highway: A review of the data-driven approach. Transport Reviews, Vol. 35, No. 1, pp. 4–32, DOI: 10.1080/01441647.2014.992496.CrossRefGoogle Scholar
  15. Oh, S., Byon, Y. J., Jang, K., and Yeo, H. (2018). “Short-term traveltime prediction on highway: A review on model-based approach. KSCE Journal of Civil Engineering, Vol. 22, No. 1, pp. 298–310, DOI: 10.1007/s12205-017-0535-8.CrossRefGoogle Scholar
  16. Oh, S., Byon, Y. J., and Yeo, H. (2016). “Improvement of search strategy with k-nearest neighbors approach for traffic state prediction.” IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 4, pp. 1146–1156, DOI: 10.1109/TITS.2015.2498408.CrossRefGoogle Scholar
  17. Qiu, Z. and Ran, B. (2008). “Kalman filtering applied to network-based cellular probe traffic monitoring.” Proc. 87th Transportation Research Board Annual Meeting, Washington D.C., USA, pp.11, #08-1984.Google Scholar
  18. Tak, S., Kim, S., Jang, K., and Yeo, H. (2014). “Real-time travel time prediction using multi-level k-nearest neighbor algorithm and data fusion method.” Proc. International Conference on Computing in Civil and Building Engineering, Orlando, Florida, USA, pp. 1861–1868, DOI: 10.1061/9780784413616.231.Google Scholar
  19. Tak, S., Woo, S., and Yeo, H. (2016). “Data-driven imputation method for traffic data in sectional units of road links.” IEEE Transactions on ITS, Vol. 17, No. 6, pp. 1762–1771, DOI: 10.1109/TITS.2016.2530312.Google Scholar
  20. Vanajakshi, L., Subramanian, S. C., and Sivanandan, R. (2008). “Short term prediction of travel time for Indian traffic conditions using buses as probe vehicles.” Proc. 87th Transportation Research Board Annual Meeting, Washington D.C., USA, pp.18, #08-1488.Google Scholar
  21. Welch, G. (1997). “SCAAT: Incremental Tracking with Incomplete Information.” Proc. 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, Los Angeles, CA, USA, pp. 333–344.Google Scholar
  22. Welch, G. and Bishop. G. (2007). “An introduction to the Kalman filter.” TR 95–041, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, Google Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Young-Ji Byon
    • 1
  • Amer Shalaby
    • 2
  • Baher Abdulhai
    • 2
  • Chung-Suk Cho
    • 3
  • Hwasoo Yeo
    • 4
    Email author
  • Samah El-Tantawy
    • 5
  1. 1.Civil Infrastructure and Environmental EngineeringKhalifa University of Science and TechnologyAbu DhabiUAE
  2. 2.Dept. of Civil & Mineral EngineeringUniversity of TorontoTorontoCanada
  3. 3.Civil Infrastructure and Environmental EngineeringKhalifa University of Science and TechnologyAbu DhabiUAE
  4. 4.Dept. of Civil and Environmental EngineeringKorea Advanced Institute of Science and TechnologyDaejeonKorea
  5. 5.Dept. of Engineering Mathematics and PhysicsCairo UniversityGizaEgypt

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