Explaining Freeway Breakdown with Geometric Brownian Motion Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 9
Abstract
A traffic volume which can trigger a breakdown event at one point in time may not trigger it at another time. The critical question is why a roadway under the same loading is stable in one instance and unstable in another. This paper explains this behavior by using a linear first-order stochastic differential equation (SDE) model, a geometric Brownian motion (gBm) model. This simple stochastic (time-dependent) model of diffusion treats traffic volume, the load on the roadway system, as a random process. The model response variables are (1) the breakdown probability, which is the transition from a free-flow to a congested state for a given traffic loading; and (2) traffic delay. There are two major challenges. The first is formulating an approach, i.e., a mathematical model, that can reliably forecast traffic breakdown at a data collection site located upstream of a bottleneck where no data are collected. The second is selecting and calibrating an appropriate gBm model with extremely volatile data. The approach was assessed by performing match tests, assessing the field data summaries against model forecasts of traffic volume, breakdown probability, and delay. The potential for using the gBm modeling approach as an operational analysis tool was discussed.
Get full access to this article
View all available purchase options and get full access to this article.
References
Box, G. E., W. G. Hunter, and J. S. Hunter. 1978. “Statistics for experimenters.” In Wiley series in probability and mathematical statistics. New York: Wiley.
Brilon, W., J. Geistefeld, and M. Regler. 2005. “Reliability of freeway traffic flow: A stochastic concept of capacity.” In Proc., 16th Int. Symp. of Transportation and Traffic Theory. College Park, MD.
Daganzo, C. F. 1997. Fundamentals of transportation and traffic operations. Oxford: Permagon.
Dong, J., and H. Mahmassani. 2012. “Stochastic modeling of traffic flow breakdown phenomenon: Application to predicting travel time reliability.” IEEE Trans. Intell. Transp. Syst. 13 (4): 1803–1809. https://doi.org/10.1109/TITS.2012.2207433.
Elefteriadou, L. 2014. An introduction to traffic flow theory. New York: Springer.
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.
Gentile, G., L. Meschini, and N. Papola. 2007. “Spillback congestion in dynamic traffic assignment: A macroscopic flow model with time-varying bottlenecks.” Transp. Res. Part B: Methodol. 41 (10): 1114–1138. https://doi.org/10.1016/j.trb.2007.04.011.
Hamdar, S. H., H. S. Mahmassani, and M. Treiber. 2015. “From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment.” Transp. Res. Part B: Methodol. 78: 32–53. https://doi.org/10.1016/j.trb.2015.03.011.
Iacus, S. 2008. Simulation and inference for stochastic differential equations: With R examples. New York: Springer.
Iacus, S. M. 2016. “sde: Simulation and inference for stochastic differential equations.” Accessed April 13, 2015. https://CRAN.R-project.org/package=sde.
Kerner, B. S. 2013. “Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory: A brief review.” Physica A 392 (21): 5261–5-5282. https://doi.org/10.1016/j.physa.2013.06.004.
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.
Laflamme, E. M., and P. J. Ossenbruggen. 2017. “Effect of time-of-day and day-of-week on congestion duration and breakdown: A case study of a bottleneck in Salem, NH.” J. Traffic and Transp. Eng. 4 (1): 31–40.
Mahnke, R., J. Kaupuzs, I. Lubashevsky, and J. Tolmacheva. 2004. Vol. 5471 of Stochastic approach to highway traffic, 298–310. Maspalomas: Spain.
Mahnke, R., J. Kaupuzs, and J. Tolmacheva. 2005. “Stochastic description of traffic breakdown: Langevin approach.” In Proc., Traffic and Granular Flow’03, 205–210. Berlin, Heidelberg: Springer.
Mahnke, R., R. Kuehne, J. Kaupuzs, I. Lubashevsky, and R. Remer. 2003. “Stochastic description of traffic breakdown.” In Proc., SPIE—The International Society for Optical Engineering, 126–135. Bellingham, WA: International Society for Optical Engineering.
Ossenbruggen, P. J. 2016. “Assessing freeway breakdown and recovery: A stochastic model.” J. Transp. Eng. 142 (7): 04016025. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000852.
R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
TRB (Transportation Research Board). 2010. Highway capacity manual. Washington, DC: TRB National Research Council.
Trieber, M., and A. Kesting. 2013. Traffic flow dynamics: Data, models and simulation. New York: Springer.
Wang, H., K. Rudy, J. Li, and D. Ni. 2010. “Calculation of traffic flow breakdown probability to optimize link throughput.” Appl. Math. Modell. 34 (11): 3376–3389. https://doi.org/10.1016/j.apm.2010.02.027.
Xu, T.-D., Y. Hao, Z.-R. Peng, and L.-J. Sun. 2013. “Modeling probabilistic traffic breakdown on congested freeway flow.” Can. J. Civ. Eng. 40 (10): 999–1008. https://doi.org/10.1139/cjce-2012-0067.
Yeon, J., and L. Elefteriadou. 2008. “Development of a methodology for estimating traffic congestion on a freeway using discrete time Markov chains.” In Vol. 3 of Proc., 15th World Congress on Intelligent Transport Systems and ITS America Annual Meeting 2008, 2059–2070. New York.
Information & Authors
Information
Published In
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jan 28, 2017
Accepted: Jan 11, 2019
Published online: Jun 21, 2019
Published in print: Sep 1, 2019
Discussion open until: Nov 21, 2019
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.