Skip to main content
SpringerLink
Log in

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

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

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.

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

  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: 191–229

    Article  Google Scholar 

  2. Hollander Y, Liu R. Estimation of the distribution of travel times by repeated simulation. Transp Res Part C-Emerging Technol, 2008, 16: 212–231

    Article  Google Scholar 

  3. Taylor MAP. Travel time variability—The case of two public modes. Transp Sci, 1982, 16: 507–521

    Article  Google Scholar 

  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: 161–191

    Article  MATH  Google Scholar 

  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: 24–32

    Article  Google Scholar 

  6. Dandy G C, McBean E A. Variability of individual travel time components. JTranspEng, 1984, 110: 340–356

    Google Scholar 

  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, 2004

    MATH  Google Scholar 

  8. Susilawati S, Taylor MAP, Somenahalli SVC. Distributions of travel time variability on urban roads. J Adv Transp, 2013, 47: 720–736

    Article  Google Scholar 

  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: 54–62

    Google Scholar 

  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: 331–354

    Article  Google Scholar 

  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: 383–399

    Article  Google Scholar 

  12. Huang H, Gao S. Optimal paths in dynamic networks with dependent random link travel times. Transp Res Part B-Methodol, 2012, 46: 579–598

    Article  Google Scholar 

  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: 173–186

    Article  Google Scholar 

  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, 1989

    Google Scholar 

  15. Clark S, Watling D. Modelling network travel time reliability under stochastic demand. Transp Res Part B-Methodol, 2005, 39: 119–140

    Article  Google Scholar 

  16. Ma Z, Ferreira L, Mesbah M. Measuring Service Reliability Using Automatic Vehicle Location Data. Math Problems Eng, 2014, 2014: 1–12

    Google Scholar 

  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: 768–782

    Article  Google Scholar 

  18. Kazagli E, Koutsopoulos H N. Estimation of arterial travel time from automatic number plate recognition data. Transp Res Record, 2013, 2391: 22–31

    Article  Google Scholar 

  19. Rahmani M, Koutsopoulos H N. Path inference from sparse floating car data for urban networks. Transp Res Part C-Emerging Technol, 2013, 30: 41–54

    Article  Google Scholar 

  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: 145–157

    Article  Google Scholar 

  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: 120–137

    Article  Google Scholar 

  22. Richardson A, Taylor M. Travel time variability on commuterjourneys. High Speed Ground Transp J, 1978, 12: 77–79

    Google Scholar 

  23. Fosgerau M, Karlstrom A. The value of reliability. Transp Res B, 2010, 43: 813–820

    Article  Google Scholar 

  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: 81–90

    Google Scholar 

  25. Kieu L M, Bhaskar A, Chung E. Public transport travel-time variability definitions and monitoring. J Transp Eng, 2015, 141: 04014068

    Article  Google Scholar 

  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: 2–15

    Article  MATH  Google Scholar 

  27. Polus A. A study of travel time and reliability on arterial routes. Transportation, 1979, 8: 141–151

    Article  Google Scholar 

  28. Jordan W C, Turnquist M A. Zone scheduling of bus routes to improve service reliability. Transp Sci, 1979, 13: 242–268

    Article  Google Scholar 

  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: 117–129

    Article  MATH  Google Scholar 

  30. Burr I W. Cumulative frequency functions. Ann Math Statist, 1942, 13: 215–232

    Article  MathSciNet  MATH  Google Scholar 

  31. Zimmer W J, Keats J B, Wang F K. The burr XII distribution in reliability analysis. J Qual Tech, 1998, 30: 386–394

    Article  Google Scholar 

  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: 83–101

    Article  Google Scholar 

  33. Guo F, Rakha H, Park S. Multistate model for travel time reliability. Transp Res Record, 2010, 2188: 46–54

    Article  Google Scholar 

  34. Vlahogianni E, Karlaftis M. Temporal aggregation in traffic data: implications for statistical characteristics and model choice. Transp Lett, 2011,3: 37–49

    Article  Google Scholar 

  35. Mazloumi E, Currie G, Rose G. Using GPS Data to Gain Insight into Public Transport Travel Time Variability. J Transp Eng, 2010, 136: 623–631

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Transportation System Research Center, Southeast University, Nanjing, 210018, China

    JianHua Guo, ChangGuang Li & Wei Huang

  2. Transportation Sensing and Cognition Research Center, Southeast University, Nanjing, 210018, China

    JianHua Guo & JinDe Cao

  3. Department of Civil & Environmental Engineering, University of Wisconsin at Milwaukee, Wisconsin, 53201, USA

    Xiao Qin

  4. National Engineering Laboratory for Green and Safe Construction Technology in Urban Rail Transit, Beijing, 100037, China

    Yun Wei

  5. School of Mathematics, Southeast University, Nanjing, 210018, China

    JinDe Cao

Authors
  1. JianHua Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ChangGuang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yun Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. JinDe Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to JinDe Cao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, J., Li, C., Qin, X. et al. Analyzing distributions for travel time data collected using radio frequency identification technique in urban road networks. Sci. China Technol. Sci. 62, 106–120 (2019). https://doi.org/10.1007/s11431-018-9267-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-018-9267-4

Keywords

Search

Navigation