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Article: Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity

TitleCalibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity
Authors
KeywordsSpeed heterogeneity
Stochastic link-based fundamental diagram
Random-parameter model
Bayesian hierarchical model
Rainfall intensity
Issue Date2021
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trb
Citation
Transportation Research Part B: Methodological, 2021, v. 150, p. 524-539 How to Cite?
AbstractThis study aims to establish a stochastic link-based fundamental diagram (FD) with explicit consideration of two available sources of uncertainty: speed heterogeneity, indicated by the speed variance within an interval, and rainfall intensity. A stochastic structure was proposed to incorporate the speed heterogeneity into the traffic stream model, and the random-parameter structures were applied to reveal the unobserved heterogeneity in the mean speeds at an identical density. The proposed stochastic link-based FD was calibrated and validated using real-world traffic data obtained from two selected road segments in Hong Kong. Traffic data were obtained from the Hong Kong Journey Time Indication System operated by the Hong Kong Transport Department during January 1 to December 31, 2017. The data related to rainfall intensity were obtained from the Hong Kong Observatory. A two-stage calibration based on Bayesian inference was proposed for estimating the stochastic link-based FD parameters. The predictive performances of the proposed model and three other models were compared using K-fold cross-validation. The results suggest that the random-parameter model considering the speed heterogeneity effect performs better in terms of both goodness-of-fit and predictive accuracy. The effect of speed heterogeneity accounts for 18%–24% of the total heterogeneity effects on the variance of FD. In addition, there exists unobserved heterogeneity across the mean speeds at an identical density, and the rainfall intensity negatively affects the mean speed and its effect on the variance of FD differs at different densities.
DescriptionHybrid open access
Persistent Identifierhttp://hdl.handle.net/10722/301255
ISSN
2023 Impact Factor: 5.8
2023 SCImago Journal Rankings: 2.660
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorBai, L-
dc.contributor.authorWong, SC-
dc.contributor.authorXu, P-
dc.contributor.authorChow, AHF-
dc.contributor.authorLam, WHK-
dc.date.accessioned2021-07-27T08:08:25Z-
dc.date.available2021-07-27T08:08:25Z-
dc.date.issued2021-
dc.identifier.citationTransportation Research Part B: Methodological, 2021, v. 150, p. 524-539-
dc.identifier.issn0191-2615-
dc.identifier.urihttp://hdl.handle.net/10722/301255-
dc.descriptionHybrid open access-
dc.description.abstractThis study aims to establish a stochastic link-based fundamental diagram (FD) with explicit consideration of two available sources of uncertainty: speed heterogeneity, indicated by the speed variance within an interval, and rainfall intensity. A stochastic structure was proposed to incorporate the speed heterogeneity into the traffic stream model, and the random-parameter structures were applied to reveal the unobserved heterogeneity in the mean speeds at an identical density. The proposed stochastic link-based FD was calibrated and validated using real-world traffic data obtained from two selected road segments in Hong Kong. Traffic data were obtained from the Hong Kong Journey Time Indication System operated by the Hong Kong Transport Department during January 1 to December 31, 2017. The data related to rainfall intensity were obtained from the Hong Kong Observatory. A two-stage calibration based on Bayesian inference was proposed for estimating the stochastic link-based FD parameters. The predictive performances of the proposed model and three other models were compared using K-fold cross-validation. The results suggest that the random-parameter model considering the speed heterogeneity effect performs better in terms of both goodness-of-fit and predictive accuracy. The effect of speed heterogeneity accounts for 18%–24% of the total heterogeneity effects on the variance of FD. In addition, there exists unobserved heterogeneity across the mean speeds at an identical density, and the rainfall intensity negatively affects the mean speed and its effect on the variance of FD differs at different densities.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trb-
dc.relation.ispartofTransportation Research Part B: Methodological-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectSpeed heterogeneity-
dc.subjectStochastic link-based fundamental diagram-
dc.subjectRandom-parameter model-
dc.subjectBayesian hierarchical model-
dc.subjectRainfall intensity-
dc.titleCalibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity-
dc.typeArticle-
dc.identifier.emailBai, L: lubai@hku.hk-
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.emailXu, P: pengxu@hku.hk-
dc.identifier.authorityWong, SC=rp00191-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.trb.2021.06.021-
dc.identifier.scopuseid_2-s2.0-85110061307-
dc.identifier.hkuros323651-
dc.identifier.volume150-
dc.identifier.spage524-
dc.identifier.epage539-
dc.identifier.isiWOS:000685516700003-
dc.publisher.placeUnited Kingdom-

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