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Article: Robust Shortest Path Problem with Distributional Uncertainty
Title | Robust Shortest Path Problem with Distributional Uncertainty |
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Authors | |
Keywords | distributionally robust optimization mean-excess travel time conditional value-at-risk Robust shortest path path reliability |
Issue Date | 2018 |
Citation | IEEE Transactions on Intelligent Transportation Systems, 2018, v. 19, n. 4, p. 1080-1090 How to Cite? |
Abstract | © 2000-2011 IEEE. Routing service considering uncertainty is at the core of intelligent transportation systems and has attracted increasing attention. Existing stochastic shortest path models require the exact probability distributions of travel times and usually assume that they are independent. However, the distributions are often unavailable or inaccurate due to insufficient data, and correlation of travel times over different links has been observed. This paper presents a robust shortest path (RSP) model that only requires partial distribution information of travel times, including the support set, mean, variance, and correlation matrix. We introduce a concept of robust mean-excess travel time to hedge against the risk from both the uncertainty of the random travel times and the uncertainty in their distributions. To solve the RSP problem, an equivalent dual formulation is derived and used to design tight lower and upper bound approximation methods, which adopt the scenario approach and semi-definite programming approach, respectively. To solve large problems, we further propose an efficient primal approximation method, which only needs to solve two deterministic shortest path problems and a mean-standard deviation shortest path problem, and analyze its approximation performance. Experiments validate the tightness of the proposed bounds and demonstrate the impact of uncertainty on the relative benefit and cost of robust paths. |
Persistent Identifier | http://hdl.handle.net/10722/296156 |
ISSN | 2023 Impact Factor: 7.9 2023 SCImago Journal Rankings: 2.580 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Yuli | - |
dc.contributor.author | Song, Shiji | - |
dc.contributor.author | Shen, Zuo Jun Max | - |
dc.contributor.author | Wu, Cheng | - |
dc.date.accessioned | 2021-02-11T04:52:57Z | - |
dc.date.available | 2021-02-11T04:52:57Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Transactions on Intelligent Transportation Systems, 2018, v. 19, n. 4, p. 1080-1090 | - |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296156 | - |
dc.description.abstract | © 2000-2011 IEEE. Routing service considering uncertainty is at the core of intelligent transportation systems and has attracted increasing attention. Existing stochastic shortest path models require the exact probability distributions of travel times and usually assume that they are independent. However, the distributions are often unavailable or inaccurate due to insufficient data, and correlation of travel times over different links has been observed. This paper presents a robust shortest path (RSP) model that only requires partial distribution information of travel times, including the support set, mean, variance, and correlation matrix. We introduce a concept of robust mean-excess travel time to hedge against the risk from both the uncertainty of the random travel times and the uncertainty in their distributions. To solve the RSP problem, an equivalent dual formulation is derived and used to design tight lower and upper bound approximation methods, which adopt the scenario approach and semi-definite programming approach, respectively. To solve large problems, we further propose an efficient primal approximation method, which only needs to solve two deterministic shortest path problems and a mean-standard deviation shortest path problem, and analyze its approximation performance. Experiments validate the tightness of the proposed bounds and demonstrate the impact of uncertainty on the relative benefit and cost of robust paths. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | - |
dc.subject | distributionally robust optimization | - |
dc.subject | mean-excess travel time | - |
dc.subject | conditional value-at-risk | - |
dc.subject | Robust shortest path | - |
dc.subject | path reliability | - |
dc.title | Robust Shortest Path Problem with Distributional Uncertainty | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TITS.2017.2709798 | - |
dc.identifier.scopus | eid_2-s2.0-85023755469 | - |
dc.identifier.volume | 19 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1080 | - |
dc.identifier.epage | 1090 | - |
dc.identifier.isi | WOS:000429017300007 | - |
dc.identifier.issnl | 1524-9050 | - |