Monitoring Urban Traffic from Floating Car Data (FCD): Using Speed or a Los-Based State Measure

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 51)


Floating Car Data (FCD) has an important traffic data source due to its lower cost and higher coverage despite its reliability problems. FCD obtained from GPS equipped vehicles can provide speed data for many segments in real-time, as provided by Be-Mobile for urban regions in Turkey. Though only providing speed per consecutive road segments, FCD is a great data source to visualize urban traffic state, in the absence of any other traffic data source, which is the focus of this study. After evaluation of variations of FCD speed values, a more simplified but more robust measure, called traffic state level (TSL) was proposed based on the Level of Service definition for urban arterials in the Highway Capacity Manual. Numerical results from analysis of one month FCD from May 2016, showed the capability of FCD and advantages and limitations of visualization based on TSL measure, which can be very efficient in data archiving, as well.


Traffic pattern detection Traffic state estimation Data visualization Floating Car Data 



The authors would like to express their thanks to Integrated Systems & Systems Design (ISSD) company workers, for providing us to use FCD database.


  1. 1.
    Xu, L., Yue, Y., Li, Q.: Identifying urban traffic congestion pattern from historical floating car data. Procedia – Soc. Behav. Sci. 96(6), 2084–2095 (2013)CrossRefGoogle Scholar
  2. 2.
    Leduc, G.: Road Traffic Data: Collection Methods and Applications. Working Papers on Energy, Transport and Climate Change. Institute for Prospective Technological Studies, Seville (2008)Google Scholar
  3. 3.
    Transportation Research Board: Highway Capacity Manual 2010. Transportation Research Board of the National Academy of Science, Washington (2010)Google Scholar
  4. 4.
    Petrovska, N., Stevanovic, A.: Traffic Congestion Analysis Visualisation Tool.
  5. 5.
    Pongnumkul, S., Kamsiriphiman, N., Poolsawas, J., Amornwat, W.: Congestion Grid: A Temporal Visualization of Road Segment Congestion Level Data.
  6. 6.
    Vasudevan, M., Negron, D., Feltz, M., Mallette, J., Wunderlich, K.: Predicting Congestion States from Basic Safety Messages Using Big Data Analytics. Transportation Research Board of the National Academies, Washington (2015)Google Scholar
  7. 7.
    Pascale, A., Mavroeidis, D., Thanh-Lam, H.: Spatio-Temporal Clustering of Urban Networks: A Real Case Scenario in London. Transportation Research Board of the National Academies, Washington (2015)Google Scholar
  8. 8.
    Liu, D., Kitamura, Y., Zeng, X., Araki, S., Kakizaki, K.: Analysis and Visualization of Traffic Conditions of Road Network by Route Bus Probe Data.
  9. 9.
    Li, Q., Ge, Q., Miao, L., Qi, M.: Measuring variability of arterial road traffic condition using archived probe data. J. Transp. Syst. Eng. Inf. Technol. 12(2), 41–46 (2012)Google Scholar
  10. 10.
    Adu-Gyamfi, Y.O., Sharma, A.: Reliability of Probe Speed Data for Detecting Congestion Trends.
  11. 11.
    Altintasi, O., Tuydes-Yaman, H., Tuncay, K.: Detection of urban traffic patterns from Floating Car Data (FCD). Transp. Res. Procedia 22, 382–391 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Civil EngineeringMiddle East Technical UniversityAnkaraTurkey

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