Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/101231
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorZang, Zen_US
dc.creatorXu, Xen_US
dc.creatorYang, Cen_US
dc.creatorChen, Aen_US
dc.date.accessioned2023-08-30T04:16:05Z-
dc.date.available2023-08-30T04:16:05Z-
dc.identifier.issn0968-090Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/101231-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2018 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Zang, Z., Xu, X., Yang, C., & Chen, A. (2018). A distribution-fitting-free approach to calculating travel time reliability ratio. Transportation Research Part C: Emerging Technologies, 89, 83-95 is available at https://doi.org/10.1016/j.trc.2018.01.027.en_US
dc.subjectAnalytical estimationen_US
dc.subjectCornish-Fisher expansionen_US
dc.subjectDistribution fittingen_US
dc.subjectTravel time reliability ratioen_US
dc.subjectTravel time variabilityen_US
dc.titleA distribution-fitting-free approach to calculating travel time reliability ratioen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationTitle on author’s file: "A DISTRIBUTION-FREE APPROACH TO ESTIMATING TRAVEL TIME RELIABILITY RATIO"en_US
dc.identifier.spage83en_US
dc.identifier.epage95en_US
dc.identifier.volume89en_US
dc.identifier.doi10.1016/j.trc.2018.01.027en_US
dcterms.abstractEmpirical studies have revealed that travel time variability (TTV) can significantly affect travelers’ behaviors and planners’ cost-benefit assessment of transportation projects. It is therefore important to systematically quantify the value of TTV (VTTV) and its impact. Recently, Fosgerau's valuation method makes this quantification possible by converting the value of travel time (VTT) and the VTTV into monetary unit. Travel time reliability ratio (TTRR), defined as a ratio of the VTTV to the VTT, is a key parameter in Fosgerau's valuation method. Calculating TTRR involves an integral of the inverse cumulative distribution function (CDF) of the standardized travel time distribution (STTD), i.e., the mean lateness factor. Using a well-fitted STTD is a straightforward way to calculate TTRR. However, it will encounter the following challenges: (1) determination of a well-fitted STTD; (2) non-existence of an algebraic expression for the CDF and its inverse CDF; and (3) lack of a closed-form expression to efficiently calculate TTRR. To circumvent the above issues, this paper proposes a distribution-fitting-free analytical approach based on the Cornish-Fisher expansion as an alternative way to calculate TTRR without the need to fit the whole CDF. The validity domain is rigorously derived for guaranteeing the accuracy of the proposed method. Realistic travel time datasets that cover 17 links are used to systematically explore the feature and accuracy of the proposed method in estimating TTRR. The comparative results demonstrate that the proposed method can efficiently and effectively estimate TTRR. When travel time datasets satisfy the validity domain, the proposed method outperforms the distribution fitting method in estimating TTRR.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research. Part C, Emerging technologies, Apr. 2018, v. 89, p. 83-95en_US
dcterms.isPartOfTransportation research. Part C, Emerging technologiesen_US
dcterms.issued2018-04-
dc.identifier.scopus2-s2.0-85044623099-
dc.description.validate202308 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberCEE-1853-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China; Shanghai Education Development Foundation; Shanghai Municipal Education Commission; Fundamental Research Funds for the Central Universitiesen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6831144-
dc.description.oaCategoryGreen (AAM)en_US
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