DocumentCode :
1249570
Title :
Stochastic Modeling of Traffic Flow Breakdown Phenomenon: Application to Predicting Travel Time Reliability
Author :
Dong, Jing ; Mahmassani, Hani S.
Author_Institution :
Oak Ridge Nat. Lab., Oak Ridge, TN, USA
Volume :
13
Issue :
4
fYear :
2012
Firstpage :
1803
Lastpage :
1809
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 :
Monte Carlo methods; probability; reliability; road traffic; road vehicles; stochastic processes; Monte Carlo simulation; correlated-distributed behavioral parameters; following vehicles; freeway congestion; heterogeneous drivers; leading vehicle; probability; random flow breakdown generation; realistic macroscopic traffic flow behavior; stochastic modeling approach; stochastic phenomena; traffic flow breakdown phenomenon; travel time reliability prediction; wave propagation; Monte Carlo methods; Numerical models; Probability; Reliability; Stochastic processes; Traffic control; Car-following model; Monte Carlo simulation; duration model; flow breakdown probability; heterogeneous drivers; travel time reliability;
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2012.2207433
Filename :
6247508
Link To Document :
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