Science China Technological Sciences

, Volume 62, Issue 1, pp 106–120 | Cite as

Analyzing distributions for travel time data collected using radio frequency identification technique in urban road networks

  • JianHua Guo
  • ChangGuang Li
  • Xiao Qin
  • Wei Huang
  • Yun Wei
  • JinDe CaoEmail author


Travel time distribution studies are fundamental for supporting transportation system reliability studies, particularly for urban road networks. However, such studies are generally based on travel time data sets with limited sample sizes, which provide inconsistent findings. In this paper, a large amount of travel time data collected from the emerging radio frequency identification (RFID) technique are used to conduct empirical investigations and estimations of travel time distributions, and three major findings are determined. First, travel time data are shown to have a complex statistical structure: the travel time distribution is in general peaky, multi-modal, and skewed to the right, which cross validates findings shown in previous publications. Second, unimodal distribution models are shown to be unable to capture the complex statistical dynamics embedded in the travel time data; therefore, a multistate distribution model is more appropriate for modeling travel time distributions. In this respect, a three-component gaussian mixture model (GMM) is tested and results consistently outperform those of unimodal distribution models. Finally, the aggregation time interval is shown to have a trivial effect on the shape of travel time distributions: the travel time distribution is stable under different aggregation time intervals. Future work is recommended to investigate further travel time variabilities and travel time distribution estimations.


reliability travel time travel time distribution time interval RFID 


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  1. 1.
    Bates J, Polak J, Jones P, et al. The valuation of reliability for personal travel. Transp Res Part E-Logistics Transp Rev, 2001, 37: 191–229CrossRefGoogle Scholar
  2. 2.
    Hollander Y, Liu R. Estimation of the distribution of travel times by repeated simulation. Transp Res Part C-Emerging Technol, 2008, 16: 212–231CrossRefGoogle Scholar
  3. 3.
    Taylor MAP. Travel time variability—The case of two public modes. Transp Sci, 1982, 16: 507–521CrossRefGoogle Scholar
  4. 4.
    Wirasinghe S C, Liu G. Determination of the number and locations of time points in transit schedule design—Case of a single run. Ann Oper Res, 1995, 60: 161–191CrossRefzbMATHGoogle Scholar
  5. 5.
    Li R, Rose G, Sarvi M. Using automatic vehicle identification data to gain insight into travel time variability and its causes. Transp Res Record, 2006, 1945: 24–32CrossRefGoogle Scholar
  6. 6.
    Dandy G C, McBean E A. Variability of individual travel time components. JTranspEng, 1984, 110: 340–356Google Scholar
  7. 7.
    Kimpel T, Strathman J, Callas S. Improving scheduling through monitoring using AVL/APC data. In: Proceedings of the 9th International Conference on Computer-Aided Scheduling of Public Transport, CASPT. San Diego, 2004zbMATHGoogle Scholar
  8. 8.
    Susilawati S, Taylor MAP, Somenahalli SVC. Distributions of travel time variability on urban roads. J Adv Transp, 2013, 47: 720–736CrossRefGoogle Scholar
  9. 9.
    Van-Lint J, Van-Zuylen H. Monitoring and predicting freeway travel time reliability: Using width and skew of day-today travel time distribution. Transp Res Rec, 2005, 1917: 54–62Google Scholar
  10. 10.
    Pattanamekar P, Park D, Rilett L R, et al. Dynamic and stochastic shortest path in transportation networks with two components of travel time uncertainty. Transp Res Part C-Emerging Technol, 2003, 11: 331–354CrossRefGoogle Scholar
  11. 11.
    Chang T S, Nozick L K, Turnquist M A. Multiobjective path finding in stochastic dynamic networks, with application to routing hazardous materials shipments. Transp Sci, 2005, 39: 383–399CrossRefGoogle Scholar
  12. 12.
    Huang H, Gao S. Optimal paths in dynamic networks with dependent random link travel times. Transp Res Part B-Methodol, 2012, 46: 579–598CrossRefGoogle Scholar
  13. 13.
    Sun L, Yang J, Mahmassani H. Travel time estimation based on piecewise truncated quadratic speed trajectory. Transp Res Part A-Policy Practice, 2008, 42: 173–186CrossRefGoogle Scholar
  14. 14.
    May A, Bonsall P, Marler N. Travel time variability of a group of car commuters in north London. Working Paper, Institute of Transport Studies University ofLeeds. Leeds, 1989Google Scholar
  15. 15.
    Clark S, Watling D. Modelling network travel time reliability under stochastic demand. Transp Res Part B-Methodol, 2005, 39: 119–140CrossRefGoogle Scholar
  16. 16.
    Ma Z, Ferreira L, Mesbah M. Measuring Service Reliability Using Automatic Vehicle Location Data. Math Problems Eng, 2014, 2014: 1–12Google Scholar
  17. 17.
    Hellinga B, Izadpanah P, Takada H, et al. Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments. Transp Res Part C-Emerging Technol, 2008, 16: 768–782CrossRefGoogle Scholar
  18. 18.
    Kazagli E, Koutsopoulos H N. Estimation of arterial travel time from automatic number plate recognition data. Transp Res Record, 2013, 2391: 22–31CrossRefGoogle Scholar
  19. 19.
    Rahmani M, Koutsopoulos H N. Path inference from sparse floating car data for urban networks. Transp Res Part C-Emerging Technol, 2013, 30: 41–54CrossRefGoogle Scholar
  20. 20.
    Zheng F, Van Zuylen H. Urban link travel time estimation based on sparse probe vehicle data. Transp Res Part C-Emerging Technol, 2013, 31: 145–157CrossRefGoogle Scholar
  21. 21.
    Jenelius E, Koutsopoulos H N. Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation. Transp Res Part B-Methodol, 2015, 71: 120–137CrossRefGoogle Scholar
  22. 22.
    Richardson A, Taylor M. Travel time variability on commuterjourneys. High Speed Ground Transp J, 1978, 12: 77–79Google Scholar
  23. 23.
    Fosgerau M, Karlstrom A. The value of reliability. Transp Res B, 2010, 43: 813–820CrossRefGoogle Scholar
  24. 24.
    Sumalee A, Watling D, Nakayama S. Reliable network design problem: Case with uncertain demand and total travel time reliability. Transp Res Rec, 2006, 1964: 81–90Google Scholar
  25. 25.
    Kieu L M, Bhaskar A, Chung E. Public transport travel-time variability definitions and monitoring. J Transp Eng, 2015, 141: 04014068CrossRefGoogle Scholar
  26. 26.
    Uno N, Kurauchi F, Tamura H, et al. Using bus probe data for analysis of travel time variability. J Intelligent Transp Syst, 2009, 13: 2–15CrossRefzbMATHGoogle Scholar
  27. 27.
    Polus A. A study of travel time and reliability on arterial routes. Transportation, 1979, 8: 141–151CrossRefGoogle Scholar
  28. 28.
    Jordan W C, Turnquist M A. Zone scheduling of bus routes to improve service reliability. Transp Sci, 1979, 13: 242–268CrossRefGoogle Scholar
  29. 29.
    Al-Deek H, Emam E B. New methodology for estimating reliability in transportation networks with degraded link capacities. J Intelligent Transp Syst, 2006, 10: 117–129CrossRefzbMATHGoogle Scholar
  30. 30.
    Burr I W. Cumulative frequency functions. Ann Math Statist, 1942, 13: 215–232MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zimmer W J, Keats J B, Wang F K. The burr XII distribution in reliability analysis. J Qual Tech, 1998, 30: 386–394CrossRefGoogle Scholar
  32. 32.
    Fosgerau M, Fukuda D. Valuing travel time variability: Characteristics of the travel time distribution on an urban road. Transp Res Part C-Emerging Technol, 2012, 24: 83–101CrossRefGoogle Scholar
  33. 33.
    Guo F, Rakha H, Park S. Multistate model for travel time reliability. Transp Res Record, 2010, 2188: 46–54CrossRefGoogle Scholar
  34. 34.
    Vlahogianni E, Karlaftis M. Temporal aggregation in traffic data: implications for statistical characteristics and model choice. Transp Lett, 2011,3: 37–49CrossRefGoogle Scholar
  35. 35.
    Mazloumi E, Currie G, Rose G. Using GPS Data to Gain Insight into Public Transport Travel Time Variability. J Transp Eng, 2010, 136: 623–631CrossRefGoogle Scholar

Copyright information

© Science in China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • JianHua Guo
    • 1
    • 2
  • ChangGuang Li
    • 1
  • Xiao Qin
    • 3
  • Wei Huang
    • 1
  • Yun Wei
    • 4
  • JinDe Cao
    • 2
    • 5
    Email author
  1. 1.Intelligent Transportation System Research CenterSoutheast UniversityNanjingChina
  2. 2.Transportation Sensing and Cognition Research CenterSoutheast UniversityNanjingChina
  3. 3.Department of Civil & Environmental EngineeringUniversity of Wisconsin at MilwaukeeWisconsinUSA
  4. 4.National Engineering Laboratory for Green and Safe Construction Technology in Urban Rail TransitBeijingChina
  5. 5.School of MathematicsSoutheast UniversityNanjingChina

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