Effect of Geometry and Control on the Probability of Breakdown and Capacity at Freeway Merges
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 7
Abstract
This paper investigates the effect of geometric and operational features on freeway merge capacity by analyzing prebreakdown flow rates and using nonparametric and parametric (survival) analysis and semiparametric analysis (Cox’s proportional hazard model) techniques. The probability of breakdown and capacity were quantified as a function of the number of lanes, ramp flow rate, presence of lane drop, and presence of ramp metering. The number of lanes showed a negative relationship with the per-lane average prebreakdown flow rate. Sites with ramp meters also showed higher prebreakdown flow rates than nonmetered sites by 150 to . In survival analysis, the number of lanes and presence of ramp meters were found to have a statistically significant effect on the breakdown probability. Capacities, defined as the 15th percentile breakdown probability flow rate, were 2,043 and at three-lane and four-lane unmetered sites, respectively. Capacities were at three-lane and at four-lane metered sites. Higher ramp flows resulted in higher breakdown probability and lower capacities at two of the four sites that were investigated, suggesting that further research is needed to evaluate this relationship.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party (raw data). Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors would like to thank KC Scout, RITIS, and PeMS for providing the data.
References
Asgharzadeh, M., and A. Kondyli. 2018. “Comparison of highway capacity estimation methods.” Transp. Res. Rec. 2672 (15): 75–84. https://doi.org/10.1177/0361198118777602.
Asgharzadeh, M., and A. Kondyli. 2019. “Evaluating the effect of geometry and control on freeway merge bottleneck capacity.” In Proc., Transportation Research Board Annual Meeting. Washington, DC: Transportation Research Board.
Bender, R., T. Augustin, and M. Blettner. 2005. “Generating survival times to simulate Cox proportional hazards models.” Stat. Med. 24 (11): 1713–1723. https://doi.org/10.1002/sim.2059.
Brilon, W., J. Geistefeldt, and M. Regler. 2005. “Reliability of freeway traffic flow: A stochastic concept of capacity.” In Proc., 16th Int. Symp. on Transportation and Traffic Theory, 125–144. Bingley, UK: Emerald Group.
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.
Cassidy, M. J., and J. Rudjanakanoknad. 2005. “Increasing the capacity of an isolated merge by metering its on-ramp.” Transp. Res. Part B Methodol. 39 (10): 896–913. https://doi.org/10.1016/j.trb.2004.12.001.
Chow, A. H., X.-Y. Lu, and T. Z. Qiu. 2009. “Empirical analysis of traffic breakdown probability distribution with respect to speed and occupancy.” In Proc., 12th IFAC Symp. on Transportation Systems. Redondo Beach, CA.
Christensen, E. 1987. “Multivariate survival analysis using Cox’s regression model.” Hepatology 7 (6): 1346–1358. https://doi.org/10.1002/hep.1840070628.
Cox, D. R. 1992. “Regression models and life-tables.” In Breakthroughs in statistics, 527–541. New York: Springer.
Elefteriadou, L. 2015. Proactive ramp management under the threat of freeway-flow breakdown. Washington, DC: Transportation Research Board of the National Academies.
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.
Esfahani, H. N., and Z. Song. 2019. A new method for microsimulation model calibration: A case study of I-710. Salt Lake City, UT: Univ. of Utah.
Evans, J. L., L. Elefteriadou, and N. Gautam. 2001. “Probability of breakdown at freeway merges using Markov chains.” Transp. Res. Part B Methodol. 35 (3): 237–254. https://doi.org/10.1016/S0191-2615(99)00049-1.
Geistefeldt, J., and S. Shojaat. 2019. “Comparison of stochastic estimates of capacity and critical density for U.S. and German freeways.” In Proc., Transportation Research Board. Washington, DC: Transportation Research Board.
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.
Karimpour, A., A. Ariannezhad, and Y.-J. Wu. 2019. “Hybrid data-driven approach for truck travel time imputation.” IET Intell. Transp. Syst. 13 (10): 1518–1524. https://doi.org/10.1049/iet-its.2018.5469.
KC Scout (Kansas City Scout). 2018. “Kansas City's bi-state traffic management system.” Accessed December 15, 2018. http://kcscout.net/.
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.
Kondyli, A., B. S. George, L. Elefteriadou, and G. Bonyani. 2016a. “Defining, measuring, and modeling capacity for the highway capacity manual.” J. Transp. Eng. Part A Syst. 143 (3): 04016014. https://doi.org/10.1061/JTEPBS.0000017.
Kondyli, A., P. Gubbala, and L. Elefteriadou. 2016b. “The contribution of ramp demand in the capacity of merge bottleneck locations.” Transp. Res. Procedia 15: 346–355. https://doi.org/10.1016/j.trpro.2016.06.029.
Kondyli, A., D. K. Hale, M. Asgharzadeh, B. J. Schroeder, A. Jia, and J. Bared. 2019. “Evaluating the operational impact of narrow lanes and shoulders for the highway capacity manual.” Transp. Res. Rec. 2673 (10): 558–570. https://doi.org/10.1177/0361198119849064.
Kühne, R., R. Mahnke, and J. Hinkel. 2006. “Modelling the effects of corridor control systems on road capacity.” In Proc., 5th Int. Symp. on Highway Capacity and Quality of Service. Yokohama, Japan: Japan Society of Traffic Engineers.
Lorenz, M., and L. Elefteriadou. 2001. “Defining freeway capacity as function of breakdown probability.” Transp. Res. Rec. 1776 (1): 43–51. https://doi.org/10.3141/1776-06.
Lu, C., and L. Elefteriadou. 2013. “An investigation of freeway capacity before and during incidents.” Transp. Lett. 5 (3): 144–153. https://doi.org/10.1179/1942786713Z.00000000016.
Mann, H. B., and D. R. Whitney. 1947. “On a test of whether one of two random variables is stochastically larger than the other.” Ann. Math. Stat. 18 (1): 50–60. https://doi.org/10.1214/aoms/1177730491.
Mantel, N. 1966. “Evaluation of survival data and two new rank order statistics arising in its consideration.” Cancer Chemother. Rep. 50 (3): 163–170.
Nassiri, H., and R. Aghamohammadi. 2017. “A new analytic neuro-fuzzy model for work zone capacity estimation.” In Proc., Transportation Research Board. Washington, DC: Transportation Research Board.
Okamura, H., S. Watanabe, and T. Watanabe. 2000. “An empirical study on the capacity of bottlenecks on the basic suburban expressway sections in Japan.” In Proc., 4th Int. Symp. on Highway Capacity. Washington, DC: Transportation Research Board.
PeMS (Caltrans Performance Measurement System). 2018. “Welcome to PeMS.” Accessed December 12, 2018. http://pems.dot.ca.gov/.
Persaud, B., S. Yagar, D. Tsui, and H. Look. 2001. “Breakdown-related capacity for freeway with ramp metering.” Transp. Res. Rec. 1748 (1): 110–115. https://doi.org/10.3141/1748-14.
RITIS (Regional Integrated Transportation Information System). 2018. “A data-driven platform for transportation analysis, monitoring, and data visualization.” Accessed November 15, 2018. https://ritis.org/.
Shojaat, S., J. Geistefeldt, S. A. Parr, L. Escobar, and B. Wolshon. 2018. “Defining freeway design capacity based on stochastic observations.” Transp. Res. Rec. 2672 (15): 131–141. https://doi.org/10.1177/0361198118784401.
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.
Sohrabi, S., and A. Ermagun. 2018. “Optimum capacity of freeways: A stochastic approach.” J. Transp. Eng. Part A: Syst. 144 (7): 04018032. https://doi.org/10.1061/JTEPBS.0000156.
TRB (Transportation Research Board). 2016. Highway capacity manual: A guide for multimodal mobility analysis. 6th ed. Washington, DC: TRB.
Weather Underground. 2018. “Weather historical data.” Accessed December 18, 2018. https://www.wunderground.com/.
Yang, X., and N. Zhang. 2005. “The marginal decrease of lane capacity with the number of lanes on highway.” In Vol. 5 of Proc., Eastern Asia Society for Transportation Studies, 739–749. Tokyo: Japan Transport and Tourism Research Institute.
Zhang, L., and D. Levinson. 2010. “Ramp metering and freeway bottleneck capacity.” Transp. Res. Part A Policy Pract. 44 (4): 218–235. https://doi.org/10.1016/j.tra.2010.01.004.
Information & Authors
Information
Published In
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jun 7, 2019
Accepted: Jan 27, 2020
Published online: Apr 28, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 28, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.