Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/95130
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorZang, Zen_US
dc.creatorXu, Xen_US
dc.creatorYang, Cen_US
dc.creatorChen, Aen_US
dc.date.accessioned2022-09-14T08:20:12Z-
dc.date.available2022-09-14T08:20:12Z-
dc.identifier.issn0191-2615en_US
dc.identifier.urihttp://hdl.handle.net/10397/95130-
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 http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Zang, Z., Xu, X., Yang, C., & Chen, A. (2018). A closed-form estimation of the travel time percentile function for characterizing travel time reliability. Transportation Research Part B: Methodological, 118, 228-247 is available at https://doi.org/10.1016/j.trb.2018.10.012.en_US
dc.subjectCornish–Fisher expansionen_US
dc.subjectHeterogeneityen_US
dc.subjectPercentile functionen_US
dc.subjectTravel time distributionen_US
dc.subjectTravel time reliabilityen_US
dc.titleA closed-form estimation of the travel time percentile function for characterizing travel time reliabilityen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage228en_US
dc.identifier.epage247en_US
dc.identifier.volume118en_US
dc.identifier.doi10.1016/j.trb.2018.10.012en_US
dcterms.abstractTravel time reliability (TTR) has received great attention in the past decades. The majority of TTR measures rely on the travel time percentile function as a basic element for performance evaluation. There are two main approaches for deriving the travel time percentile function: simple unimodal probability distribution models and mixture/nonparametric models. Despite the tractability of the former approach, they cannot sufficiently capture the travel time distributions (TTDs) due to their heterogeneity, and also often encounters many issues such as the failure of significance tests and the indecisiveness among multiple fitted distributions. On the other hand, the latter approach possesses greater flexibility for capturing diverse TTDs, but it does not have a simple and closed-form travel time percentile function. Motivated by the above drawbacks, this paper proposes a closed-form and flexible approach for estimating the travel time percentile function of diverse TTDs based on the Cornish–Fisher expansion without the need to assume/fit a certain distribution type. To ensure a high-quality estimation, we introduce and integrate two improvements with theoretically proven foundation into the Cornish–Fisher expansion while guaranteeing a closed-form expression of the travel time percentile function. Specifically, the first improvement, logarithm transformation, increases the probability of satisfying the validity domain of the Cornish–Fisher expansion; while the second improvement, rearrangement, guarantees a monotone travel time percentile function when travel time datasets cannot satisfy the validity domain after the logarithm transformation. Realistic travel time datasets are used to examine the accuracy and robustness of the proposed method. Compared to five widely-used probability distributions, the proposed method is sufficiently adaptable to estimating percentile function of diverse TTDs with lower estimation error. More importantly, it has a closed-form expression of the travel time percentile function, which would facilitate characterizing TTR in large-scale network applications.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research. Part B, Methodological, Dec. 2018, v. 118, p. 228-247en_US
dcterms.isPartOfTransportation research. Part B, Methodologicalen_US
dcterms.issued2018-12-
dc.identifier.scopus2-s2.0-85055992251-
dc.identifier.eissn1879-2367en_US
dc.description.validate202209 bcfc-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberCEE-1599-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China; Chenguang Program sponsored by Shanghai Education Development Foundation; Shanghai Municipal Education Commission; Kwong Wah Education Foundation of the Research Institute for Sustainable Urban Development at the Hong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS20009646-
dc.description.oaCategoryGreen (AAM)en_US
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