Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks: Linear mixed integer programming reformulations

Lixing Yang and Xuesong Zhou

Transportation Research Part B: Methodological, 2017, vol. 96, issue C, 68-91

Abstract: 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.

Keywords: Reliable path finding; On-time arrival path; Percentile path; Lagrangian relaxation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (26)

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DOI: 10.1016/j.trb.2016.11.012

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