Skip to main content
Log in

Selecting the Representative Travel Time Reliability Measure Based on Metric (Dis)Agreement Patterns

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Available TTR metrics can exhibit discrepancies even for the same travel time data, i.e., one metric indicates a reliable travel time distribution, while another one indicates otherwise. This conflict raises concerns for practitioners regarding which metric to use for policy making. To identify the (dis)agreement patterns between TTR metrics, Lognormal, Weibull, Gamma, and Inverse Gaussian distributions were selected to generate travel time distributions. Similarly, six commonly used TTR metrics (Percent variation, Width of travel time distribution, Skew index, Buffer index, 95th percentile, and Misery index) were used to calculate the TTR for hypothetical distributions. Descriptive analysis, majority voting and k-means clustering approaches were utilized to identify the representative TTR metric. The findings revealed that distribution skewness, regardless of the distribution type, can indicate when the metrics are more likely to agree, i.e., the practitioners can choose the TTR metric arbitrarily for travel time distributions that have a skewness smaller than 1.2 or larger than 1.8, because all metrics unanimously agree. It was also shown that for travel time distributions in the disagreement skewness range, buffer index can be used as the representative TTR metric. The results were also validated with USDOT Next Generation Simulation (NGSIM) vehicle trajectory dataset.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author, MB, upon reasonable request.

References

  1. Lomax, T., Schrank, D., Turner, S., Margiotta, R.: Selecting travel reliability measures. (2003) [Online]. Available: http://tti.tamu.edu/documents/474360-1.pdf

  2. Chen, M., Yu, G., Chen, P., Wang, Y.: A copula-based approach for estimating the travel time reliability of urban arterial. Transp. Res. Part C Emerg. Technol. 82, 1–23 (2017)

    Article  Google Scholar 

  3. TRB.: Weigh-in-Motion Subcommittee (AB]35(2)) of the Transportation Research Board Committee on Highway Traffic Monitoring (ABI35), Advancing Highway Traffic Monitoring Through Strategic Research, e-Circular (E-C227) (2017)

  4. Carrion, C., Levinson, D.: Value of travel time reliability: A review of current evidence. Transp. Res. Part A Policy Pract. 46(4), 720–741 (2012)

    Article  Google Scholar 

  5. de Jong, G. C., Bliemer, M., C., J.: On including travel time reliability of road traffic in appraisal. Transp.  Res. Part A: Policy and Pract. (2015). https://doi.org/10.1016/j.tra.2015.01.006

  6. Pu, W.: Analytic relationships between travel time reliability measures. Transp. Res. Rec. 2254, 122–130 (2011)

    Article  Google Scholar 

  7. Polus, A.: A study of travel time and reliability on arterial routes. Transportation (Amst) 8(2), 141–151 (1979)

    Article  Google Scholar 

  8. Al-Deek, H., Emam, E.B.: New methodology for estimating reliability in transportation networks with degraded link capacities. J. Intell. Transp. Syst. Technol. Planning, Oper. 10(3), 117–129 (2006)

  9. Zhang, Z., He, Q., Gou, J., Li X.: Performance measure of travel time reliability of emergency vehicles in an urban region. Presented at 94th Annual Meeting of the Transportation Research Board, Washington, D.C. (2015)

  10. Next Generation Simulation (NGSIM) Vehicle Trajectories and Supporting Data | Department of Transportation - Data Portal. [Online]. Available: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj. [Accessed: 29-Jun-2021]

  11. Lyman, K., Bertini, R.L.: Using travel time reliability measures to improve regional transportation planning and operations. Transp. Res. Rec. 2046, 1–10 (2008)

    Article  Google Scholar 

  12. Iida, Y.: Basic concepts and future directions of road network reliability analysis. J. Adv. Transp. 33(2), 125–134 (1999)

    Article  MathSciNet  Google Scholar 

  13. Chen, A., Yang, H., Lo, H.K., Tang, W.H.: Capacity reliability of a road network: An assessment methodology and numerical results. Transp. Res. Part B Methodol. 36(3), 225–252 (2002)

    Article  Google Scholar 

  14. Elefteriadou, L., Cui, X.: Travel time reliability and truck level of service on the strategic intermodal system part a: Travel time reliability. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C. (2007)

  15. FHWA: Travel Time Reliability: Making It There on Time, All the Tim. [Online]. Available: https://ops.fhwa.dot.gov/publications/tt_reliability/TTR_Report.htm. (2006). Accessed 1 Aug 2021

  16. Florida DOT.: Florida reliabilitv method in florida’s mobility performance measures program. Office of the State Transportation Planner, Transportation Statistics Office, Tallahassee (2000)

  17. Vandervalk, A., Louch, H., Guerre, J., Margiotta, R.: Incorporating reliability performance measures into the transportation planning and programming processes: Technical reference transportation research board SHRP 2 Report S2-L05-RR-3. (2014)

  18. Rakha, H. A., El-Shawarby, I., Arafeh, M., Dion, F.: Estimating path travel-time reliability. In Proceedings of the 2006 IEEE Intelligent Transportation Systems Conference, IEEE, Toronto, Ontario, Canada, pp. 236–241 (2006). https://doi.org/10.1109/ITSC.2006.1706748

  19. Weifeng, L., Zhengyu, D., Gaohua, G.: Research on Travel Time Distribution Characteristics of Expressways in Shanghai. Procedia - Soc. Behav. Sci. 96(Cictp), 339–350 (2013)

  20. Xue, Y., Jin, J., Lai, J., Ran, B., Yang, D.: Empirical characteristics of transit travel time distribution for commuting routes (No. 11-2827). (2011)

  21. Woodard, D., Nogin, G., Koch, P., Racz, D., Goldszmidt, M., Horvitz, E.: Predicting travel time reliability using mobile phone GPS data. Transp. Res. Part C Emerg. Technol. 75, 30–44 (2017)

    Article  Google Scholar 

  22. Rahmani, M., Jenelius, E., Koutsopoulos, H.: Non-Parametric Estimation of Route Travel Time Distributions from Low-Frequency Floating Car Data. Transp. Res. Part C Emerg. Technol. 58B, 343–362 (2015)

    Article  Google Scholar 

  23. Zang, Z., Xu, X., Yang, C., Chen, A.: A distribution-fitting-free approach to calculating travel time reliability ratio. Transp. Res. Part C Emerg. Technol. 89, 83–95 (2018)

    Article  Google Scholar 

  24. Arezoumandi, M.: Estimation of travel time reliability for freeways using mean and standard deviation of travel time. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal Transp. Syst. Eng. Inf. Technol. 11(6), 74–84 (2011)

    Google Scholar 

  25. Al-deek, Emam, E.B.: New methodology for estimating reliability in transportation networks with degraded link capacities. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations. (2006). 10(3), 17–129. 2006. https://doi.org/10.1080/15472450600793586

  26. van Lint, J.W.C., van Zuylen, H.J., Tu, H.: Travel time unreliability on freeways: Why measures based on variance tell only half the story. Transp. Res. Part A Policy Pract. 42(1), 258–277 (2008)

    Article  Google Scholar 

  27. Chen, Z., Fan, W.: Data analytics approach for travel time reliability pattern analysis and prediction. J. Mod. Transp. 27(4), 250–265 (2019)

    Article  Google Scholar 

  28. Van Lint, J.W.C., Van Zuylen, H.J.: Monitoring and predicting freeway travel time reliability: Using width and skew of day-to-day travel time distribution. Transp. Res. Rec. 1917, 54–62 (2005)

    Article  Google Scholar 

  29. Projects | Strategic Highway Research Program 2 (SHRP 2). [Online]. Available: http://www.trb.org/StrategicHighwayResearchProgram2SHRP2/Projects.aspx. [Accessed: 06-Jun-2021]

  30. Skinner, R. E.: Cost-effective performance measures for travel time delay, variation, and reliability. (2003)

  31. Margiotta, R. A., Turner, S., Taylor, R., Chang, C.: National performance measures for congestion, reliability, and freight, and CMAQ traffic congestion: General guidance and step-by-step metric calculation procedures. Report Number FHWA-HIF-18-040. U.S. Department of Transportation, Federal Highway Administration, Washington, DC (2018)

  32. Chepuri, A., Kumar, C., Bhanegaonkar, P., Arkatkar, S.S., Joshi, G.: Travel Time Reliability Analysis on Selected Bus Route of Mysore Using GPS Data. Transp. Dev. Econ. 5(2), 13 (2019)

    Article  Google Scholar 

  33. Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recognit. 36(2), 451–461 (2003)

    Article  Google Scholar 

  34. Kriegel, H.-P., Schubert, E., Zimek, A.: The (black) art of runtime evaluation: Are we comparing algorithms or implementations? Knowl. Inf. Syst. 52(2), 341–378 (2017)

    Article  Google Scholar 

  35. Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of data clusters via the gap statistic. J. R. Stat. Soc. Ser B 63(Part 2), 411–423 (2001)

  36. Ghavidel, M., Khademi, N., Samani, E. B., Milani, S.: Travel time variability analysis for Bluetooth sensor data in highways (No. 4377). EasyChair. (2020) 

  37. Terrace, N., et al.: Travel Time Reliability Measurement for Selected Corridors in the Adelaide Metropolitan Area. J. East. Asia Soc. Transp. Stud. 8, 86–102 (2010)

    Google Scholar 

  38. US 101 fact sheet. [Online]. Available: https://www.fhwa.dot.gov/publications/research/operations/07030/index.cfm. Accessed 19 July  2021

Download references

Author information

Authors and Affiliations

Authors

Contributions

The authors confirm contribution to the paper as follows: analysis and interpretation of results: MB and AY; manuscript preparation: MB and AY. All authors reviewed the results and approved the final version of the manuscript.

Corresponding author

Correspondence to Mahsa Bargahi.

Ethics declarations

Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bargahi, M., Yazici, A. Selecting the Representative Travel Time Reliability Measure Based on Metric (Dis)Agreement Patterns. Int. J. ITS Res. 21, 36–47 (2023). https://doi.org/10.1007/s13177-022-00336-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-022-00336-y

Keywords

Navigation