TY - JOUR
T1 - Stochastic modeling of traffic flow breakdown phenomenon
T2 - Application to predicting travel time reliability
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
PY - 2012
Y1 - 2012
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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U2 - 10.1109/TITS.2012.2207433
DO - 10.1109/TITS.2012.2207433
M3 - Article
AN - SCOPUS:84870543558
SN - 1524-9050
VL - 13
SP - 1803
EP - 1809
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 4
M1 - 6247508
ER -