Stochastic modeling of traffic flow breakdown phenomenon: Application to predicting travel time reliability

Jing Dong; Hani S. Mahmassani

doi:10.1109/TITS.2012.2207433

Stochastic modeling of traffic flow breakdown phenomenon: Application to predicting travel time reliability

Jing Dong^*, Hani S. Mahmassani

^*Corresponding author for this work

Civil and Environmental Engineering

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

Abstract

This paper presents a modeling approach in generating random flow breakdowns on congested freeways and capturing subsequent wave propagation among heterogeneous drivers. The approach is intended for predicting travel time variability caused by such stochastic phenomena. It is assumed that breakdown may occur at different flow levels with some probability and would sustain for a random duration. This is modeled at the microscopic level by considering speed changes that are initiated by a leading vehicle and propagated by the following vehicles with correlated-distributed behavioral parameters. Numerical results from a Monte Carlo simulation demonstrate that the proposed stochastic modeling approach produces a realistic macroscopic traffic flow behavior and can be used to generate travel time distributions.

Original language	English (US)
Article number	6247508
Pages (from-to)	1803-1809
Number of pages	7
Journal	IEEE Transactions on Intelligent Transportation Systems
Volume	13
Issue number	4
DOIs	https://doi.org/10.1109/TITS.2012.2207433
State	Published - 2012

Funding

Manuscript received November 6, 2011; revised March 19, 2012; accepted June 5, 2012. Date of publication July 23, 2012; date of current version November 27, 2012. This work of H. S. Mahmassani was supported in part by the National Science Foundation Civil Infrastructure Systems under Grant 0928577. The Associate Editor for this paper was W.-H. Lin. J. Dong is with Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA (e-mail: [email protected]). H. S. Mahmassani is with Northwestern University, Evanston, IL 60208 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TITS.2012.2207433

Keywords

Car-following model
Monte Carlo simulation
duration model
flow breakdown probability
heterogeneous drivers
travel time reliability

ASJC Scopus subject areas

Automotive Engineering
Mechanical Engineering
Computer Science Applications

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10.1109/TITS.2012.2207433

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title = "Stochastic modeling of traffic flow breakdown phenomenon: Application to predicting travel time reliability",

abstract = "This paper presents a modeling approach in generating random flow breakdowns on congested freeways and capturing subsequent wave propagation among heterogeneous drivers. The approach is intended for predicting travel time variability caused by such stochastic phenomena. It is assumed that breakdown may occur at different flow levels with some probability and would sustain for a random duration. This is modeled at the microscopic level by considering speed changes that are initiated by a leading vehicle and propagated by the following vehicles with correlated-distributed behavioral parameters. Numerical results from a Monte Carlo simulation demonstrate that the proposed stochastic modeling approach produces a realistic macroscopic traffic flow behavior and can be used to generate travel time distributions.",

keywords = "Car-following model, Monte Carlo simulation, duration model, flow breakdown probability, heterogeneous drivers, travel time reliability",

author = "Jing Dong and Mahmassani, {Hani S.}",

note = "Funding Information: Manuscript received November 6, 2011; revised March 19, 2012; accepted June 5, 2012. Date of publication July 23, 2012; date of current version November 27, 2012. This work of H. S. Mahmassani was supported in part by the National Science Foundation Civil Infrastructure Systems under Grant 0928577. The Associate Editor for this paper was W.-H. Lin. J. Dong is with Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA (e-mail: [email protected]). H. S. Mahmassani is with Northwestern University, Evanston, IL 60208 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TITS.2012.2207433",

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AU - Dong, Jing

AU - Mahmassani, Hani S.

N1 - Funding Information: Manuscript received November 6, 2011; revised March 19, 2012; accepted June 5, 2012. Date of publication July 23, 2012; date of current version November 27, 2012. This work of H. S. Mahmassani was supported in part by the National Science Foundation Civil Infrastructure Systems under Grant 0928577. The Associate Editor for this paper was W.-H. Lin. J. Dong is with Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA (e-mail: [email protected]). H. S. Mahmassani is with Northwestern University, Evanston, IL 60208 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TITS.2012.2207433

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N2 - This paper presents a modeling approach in generating random flow breakdowns on congested freeways and capturing subsequent wave propagation among heterogeneous drivers. The approach is intended for predicting travel time variability caused by such stochastic phenomena. It is assumed that breakdown may occur at different flow levels with some probability and would sustain for a random duration. This is modeled at the microscopic level by considering speed changes that are initiated by a leading vehicle and propagated by the following vehicles with correlated-distributed behavioral parameters. Numerical results from a Monte Carlo simulation demonstrate that the proposed stochastic modeling approach produces a realistic macroscopic traffic flow behavior and can be used to generate travel time distributions.

AB - This paper presents a modeling approach in generating random flow breakdowns on congested freeways and capturing subsequent wave propagation among heterogeneous drivers. The approach is intended for predicting travel time variability caused by such stochastic phenomena. It is assumed that breakdown may occur at different flow levels with some probability and would sustain for a random duration. This is modeled at the microscopic level by considering speed changes that are initiated by a leading vehicle and propagated by the following vehicles with correlated-distributed behavioral parameters. Numerical results from a Monte Carlo simulation demonstrate that the proposed stochastic modeling approach produces a realistic macroscopic traffic flow behavior and can be used to generate travel time distributions.

KW - Car-following model

KW - Monte Carlo simulation

KW - duration model

KW - flow breakdown probability

KW - heterogeneous drivers

KW - travel time reliability

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Stochastic modeling of traffic flow breakdown phenomenon: Application to predicting travel time reliability

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