Deterministic and Stochastic Capacity in Work Zones: Findings from a Long-Term Work Zone
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 1
Abstract
This paper describes the development of a breakdown probability model for a long-term work zone in a suburban area. It was found that the Gompertz distribution best describes the breakdown probability at a work zone. In the absence of work zone breakdown probability models, deterministic capacities may still be used for work zone planning because the work zone data sets needed for the construction of breakdown probability models are scarce. The capacity values and the corresponding breakdown probabilities associated with 10 common definitions of deterministic work zone capacity were determined. This information will assist practitioners in choosing work zone capacity definitions that correspond to an acceptable risk of congestion within their jurisdictions. A data mining method was proposed to automate the process for identifying traffic breakdown, and metrics were proposed to assess goodness of fit of various breakdown probability models.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request. Traffic volume, speed, and occupancy collected at the work zone taper are available from the corresponding author by request. These data were used to develop the breakdown probability models presented in this paper.
Acknowledgments
The authors thank the Missouri DOT for providing work zone traffic sensor data.
References
Akaike, H. 1974. “A new look at the statistical model identification.” IEEE Trans. Autom. Control 19 (6): 716–723. https://doi.org/10.1109/TAC.1974.1100705.
Al-Kaisy, A., and F. Hall. 2003. “Guidelines for estimating capacity at freeway reconstruction zones.” J. Transp. Eng. 129 (5): 572–577. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:5(572).
Benekohal, R. F., A.-Z. Kaja-Mohideen, and M. V. Chitturi. 2004. “Methodology for estimating operating speed and capacity in work zones.” Transp. Res. Rec. 1883 (1): 103–111. https://doi.org/10.3141/1883-12.
Bezdek, J. C., and N. R. Pal. 1998. “Some new indexes of cluster validity.” IEEE Trans. Syst. Man Cybern. Part B Cybern. 28 (3): 301–315. https://doi.org/10.1109/3477.678624.
Bharadwaj, N., P. Edara, C. Sun, H. Brown, and Y. Chang. 2018. “Traffic flow modeling of diverse work zone activities.” Transp. Res. Rec. 2672 (16): 23–34. https://doi.org/10.1177/0361198118758056.
Brilon, W., J. Geistefeldt, and M. Regler. 2005. “Reliability of freeway traffic flow: A stochastic concept of capacity.” In Proc., 16th Int. Symp. Transportation Traffic Theory, Transportation and Traffic Theory: Flow, Dynamics and Human Interaction. New York: Elsevier.
Brilon, W., J. Geistefeldt, and H. Zurlinden. 2007. “Implementing the concept of reliability for highway capacity analysis.” Transp. Res. Rec. 2027 (1): 1–8. https://doi.org/10.3141/2027-01.
Burnham, K. P., D. R. Anderson, and K. P. Huyvaert. 2011. “AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons.” Behav. Ecol. Sociobiol. 65 (1): 23–35. https://doi.org/10.1007/s00265-010-1029-6.
Chitturi, M. V., R. F. Benekohal, and A.-Z. Kaja-Mohideen. 2008. “Methodology for computing delay and user costs in work zones.” Transp. Res. Rec. 2055 (1): 31–38. https://doi.org/10.3141/2055-04.
Collett, D. 2003. Modelling survival data in medical research. Boca Raton, FL: CRC Press.
Croarkin, C., et al. 2012. e-Handbook of statistical methods. Washington, DC: US Dept. of Commerce. https://doi.org/10.18434/M32189.
Davies, D. L., and D. W. Bouldin. 1979. “A cluster separation measure.” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1 (2): 224–227. https://doi.org/10.1109/TPAMI.1979.4766909.
Dissanayake, S., and L. Ortiz. 2015. Highway work zone capacity estimation using field data from Kansas. Washington, DC: Federal Highway Administration, USDOT.
Dixon, K. K., and J. E. Hummer. 1995. Capacity and delay in major freeway construction zones.. Raleigh, NC: Center for Transportation Engineering Studies, North Carolina State Univ.
Edara, P., J. Kianfar, and C. Sun. 2012. “Analytical methods for deriving work zone capacities from field data.” J. Transp. Eng. 137 (6): 809–818. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000374.
Edwards, M. B., and M. D. Fontaine. 2012. “Investigation of travel time reliability in work zones with private-sector data.” Transp. Res. Rec. 2272 (1): 9–18. https://doi.org/10.3141/2272-02.
Elefteriadou, L., A. Kondyli, W. Brilon, F. L. Hall, B. Persaud, and S. Washburn. 2014. “Enhancing ramp metering algorithms with the use of probability of breakdown models.” J. Transp. Eng. 140 (4): 04014003. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000653.
Elefteriadou, L., R. P. Roess, and W. R. McShane. 1995. “Probabilistic nature of breakdown at freeway merge junctions.” Transp. Res. Rec. 1484: 80–89.
Fontaine, M. D., P. Chun, and B. H. Cottrell. 2014. “Using private sector travel time data for project-level work zone mobility performance measurement.” In Proc., 93rd Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board of the National Academies.
Geistefeldt, J. 2011. “Capacity effects of variable speed limits on German freeways.” Procedia Social Behav. Sci. 16: 48–56. https://doi.org/10.1016/j.sbspro.2011.04.428.
Hartigan, J. A., and M. A. Wong. 1979. “Algorithm AS 136: A K-means clustering algorithm.” Appl. Stat. 28 (1): 100. https://doi.org/10.2307/2346830.
Heaslip, K., A. Kondyli, D. Arguea, L. Elefteriadou, and F. Sullivan. 2009. “Estimation of freeway work zone capacity through simulation and field data.” Transp. Res. Rec. 2130 (1): 16–24. https://doi.org/10.3141/2130-03.
Heshami, S., L. Kattan, Z. Gong, and S. Aalami. 2019. “Deterministic and stochastic freeway capacity analysis based on weather conditions.” J. Transp. Eng. Part A: Syst. 145 (5): 04019016. https://doi.org/10.1061/JTEPBS.0000232.
Kaplan, E. L., and P. Meier. 1958. “Nonparametric estimation from incomplete observations.” J. Am. Stat. Assoc. 53 (282): 457–481. https://doi.org/10.1080/01621459.1958.10501452.
Kondyli, A., L. Elefteriadou, W. Brilon, F. L. Hall, B. Persaud, and S. Washburn. 2013. “Development and evaluation of methods for constructing breakdown probability models.” J. Transp. Eng. 139 (9): 931–940. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000574.
Krammes, R. A., and G. O. Lopez. 1994. “Updated capacity values for short-term freeway work zone lane closures.” Transp. Res. Rec. 1442: 49–56.
Kühne, R., R. Mahnke, I. Lubashevsky, and J. Kaupuzs. 2002. “Probabilistic description of traffic breakdowns.” Phys. Rev. E 65 (6 Pt 2): 066125. https://doi.org/10.1103/PhysRevE.65.066125.
Liang, H., and G. Zou. 2008. “Improved AIC selection strategy for survival analysis.” Comput. Stat. Data Anal. 52 (5): 2538–2548. https://doi.org/10.1016/j.csda.2007.09.003.
Luna, R., and M. A. Mohammedi. 2014. Effects of road construction intensity and operations on rural freeway work zone capacity. Washington, DC: Federal Highway Administration, USDOT.
Modi, V., A. Kondyli, S. S. Washburn, and D. S. McLeod. 2014. “Freeway capacity estimation method for planning applications.” J. Transp. Eng. 140 (9): 05014004. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000699.
Ozguven, E. E., and K. Ozbay. 2008. “Nonparametric Bayesian estimation of freeway capacity distribution from censored observations.” Transp. Res. Rec. 2061 (1): 20–29. https://doi.org/10.3141/2061-03.
Pakhira, M. K., S. Bandyopadhyay, and U. Maulik. 2004. “Validity index for crisp and fuzzy clusters.” Pattern Recognit. 37 (3): 487–501. https://doi.org/10.1016/j.patcog.2003.06.005.
Persaud, B., S. Yagar, and R. Brownlee. 1998. “Exploration of the breakdown phenomenon in freeway traffic.” Transp. Res. Rec. 1634 (1): 64–69. https://doi.org/10.3141/1634-08.
Pringle, R., G. Nikolic, K. Bragg, and D. Mendoza. 2010. “Expressway single-lane work zone capacities with commercial vehicle impacts.” In Proc., 89th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board of the National Academies.
Rakha, H., and M. Arafeh. 2010. “Calibrating steady-state traffic stream and car-following models using loop detector data.” Transp. Sci. 44 (2): 151–168. https://doi.org/10.1287/trsc.1090.0297.
Sarasua, W. A., W. J. Davis, M. A. Chowdhury, and J. H. Ogle. 2006. “Estimating interstate highway capacity for short-term work zone lane closures: Development of methodology.” Transp. Res. Rec. 1948 (1): 45–57. https://doi.org/10.1177/0361198106194800106.
Sarasua, W. A., W. J. Davis, D. B. Clarke, J. Kottapally, and P. Mulukutla. 2004. “Evaluation of interstate highway capacity for short-term work zone lane closures.” Transp. Res. Rec. 1877 (1): 85–94. https://doi.org/10.3141/1877-10.
Shojaat, S., J. Geistefeldt, S. A. Parr, C. G. Wilmot, and B. Wolshon. 2016. “Sustained flow index: Stochastic measure of freeway performance.” Transp. Res. Rec. 2554 (1): 158–165. https://doi.org/10.3141/2554-17.
Su, M.-C., and C.-H. Chou. 2001. “A modified version of the K-means algorithm with a distance based on cluster symmetry.” IEEE Trans. Pattern Anal. Mach. Intell. 23 (6): 674–680. https://doi.org/10.1109/34.927466.
Symonds, M. R. E., and A. Moussalli. 2011. “A brief guide to model selection, multimodel inference and model averaging in behavioral ecology using Akaike’s information criterion.” Behav. Ecol. Sociobiol. 65 (1): 13–21. https://doi.org/10.1007/s00265-010-1037-6.
Tan, P.-N. 2007. Introduction to data mining. Delhi, India: Pearson Education India.
Transportation Research Board. 2010. Highway capacity manual. Washington, DC: Transportation Research Board of the National Academies.
Transportation Research Board. 2016. Highway capacity manual: A guide for multimodal mobility analysis. 6th ed. Washington, DC: Transportation Research Board of the National Academies.
Van Aerde, M., and H. Rakha. 1995. “Multivariate calibration of single regime speed-flow-density relationships.” In Proc., IEEE 6th Int. Pacific Rim TransTech Conf. Vehicle Navigation and Information Systems Conf. Proc.: A Ride into the Future, 334–341. New York: IEEE. https://doi.org/10.1109/VNIS.1995.518858.
Von der Heiden, N., and J. Geistefeldt. 2016. “Capacity of freeway work zones in Germany.” Transp. Res. Procedia 15: 233–244. https://doi.org/10.1016/j.trpro.2016.06.020.
Wagenmakers, E.-J., and S. Farrell. 2004. “AIC model selection using Akaike weights.” Psychonomic Bull. Rev. 11 (1): 192–196. https://doi.org/10.3758/BF03206482.
Weng, J., and Q. Meng. 2013. “Estimating capacity and traffic delay in work zones: An overview.” Transp. Res. Part C: Emerging Technol. 35 (Oct): 34–45. https://doi.org/10.1016/j.trc.2013.06.005.
Weng, J., and Q. Meng. 2015. “Incorporating work zone configuration factors into speed-flow and capacity models.” J. Adv. Transp. 49 (3): 371–384. https://doi.org/10.1002/atr.1277.
Weng, J., and X. Yan. 2014. “New methodology to determine work zone capacity distribution.” Transp. Res. Rec. 2461: 25–31. https://doi.org/10.3141/2461-04.
Weng, J., and X. Yan. 2016. “Probability distribution-based model for work zone capacity prediction.” J. Adv. Transp. 50 (2): 165–179. https://doi.org/10.1002/atr.1310.
Xing, J., H. Takahashi, and K. Iida. 2010. “Analysis of bottleneck capacity and traffic safety in Japanese expressway work zones.” In Proc., 89th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board of the National Academies.
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© 2020 American Society of Civil Engineers.
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Received: May 21, 2019
Accepted: Aug 13, 2020
Published online: Oct 19, 2020
Published in print: Jan 1, 2021
Discussion open until: Mar 19, 2021
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