TY - JOUR
T1 - Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks
T2 - Linear mixed integer programming reformulations
AU - Yang, Lixing
AU - Zhou, Xuesong
N1 - Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Aiming to provide a generic modeling framework for finding reliable paths in dynamic and stochastic transportation networks, this paper addresses a class of two-stage routing models through reformulation of two commonly used travel time reliability measures, namely on-time arrival probability and percentile travel time, which are much more complex to model in comparison to expected utility criteria. A sample-based representation is adopted to allow time-dependent link travel time data to be spatially and temporally correlated. A number of novel reformulation methods are introduced to establish equivalent linear integer programming models that can be easily solved. A Lagrangian decomposition approach is further developed to dualize the non-anticipatory coupling constraints across different samples and then decompose the relaxed model into a series of computationally efficient time-dependent least cost path sub-problems. Numerical experiments are implemented to demonstrate the solution quality and computational performance of the proposed approaches.
AB - Aiming to provide a generic modeling framework for finding reliable paths in dynamic and stochastic transportation networks, this paper addresses a class of two-stage routing models through reformulation of two commonly used travel time reliability measures, namely on-time arrival probability and percentile travel time, which are much more complex to model in comparison to expected utility criteria. A sample-based representation is adopted to allow time-dependent link travel time data to be spatially and temporally correlated. A number of novel reformulation methods are introduced to establish equivalent linear integer programming models that can be easily solved. A Lagrangian decomposition approach is further developed to dualize the non-anticipatory coupling constraints across different samples and then decompose the relaxed model into a series of computationally efficient time-dependent least cost path sub-problems. Numerical experiments are implemented to demonstrate the solution quality and computational performance of the proposed approaches.
KW - Lagrangian relaxation
KW - On-time arrival path
KW - Percentile path
KW - Reliable path finding
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U2 - 10.1016/j.trb.2016.11.012
DO - 10.1016/j.trb.2016.11.012
M3 - Article
AN - SCOPUS:84999266716
SN - 0191-2615
VL - 96
SP - 68
EP - 91
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
ER -