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dc.contributor.authorZhuge, Chengxiang
dc.contributor.authorBithell, Mike
dc.contributor.authorShao, Chunfu
dc.contributor.authorLi, Xia
dc.contributor.authorGao, Jian
dc.date.accessioned2021-02-09T16:33:54Z
dc.date.available2021-02-09T16:33:54Z
dc.date.issued2019-09-05
dc.identifier.issn0049-4488
dc.identifier.others11116-019-10048-0
dc.identifier.other10048
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317402
dc.description.abstractAbstract: Coupling activity-based models with dynamic traffic assignment appears to form a promising approach to investigating travel demand. However, such an integrated framework is generally time-consuming, especially for large-scale scenarios. This paper attempts to improve the performance of these kinds of integrated frameworks through some simple adjustments using MATSim as an example. We focus on two specific areas of the model—replanning and time stepping. In the first case we adjust the scoring system for agents to use in assessing their travel plans to include only agents with low plan scores, rather than selecting agents at random, as is the case in the current model. Secondly, we vary the model time step to account for network loading in the execution module of MATSim. The city of Baoding, China is used as a case study. The performance of the proposed methods was assessed through comparison between the improved and original MATSim, calibrated using Cadyts. The results suggest that the first solution can significantly decrease the computing time at the cost of slight increase of model error, but the second solution makes the improved MATSim outperform the original one, both in terms of computing time and model accuracy; Integrating all new proposed methods takes still less computing time and obtains relatively accurate outcomes, compared with those only incorporating one new method.
dc.languageen
dc.publisherSpringer US
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArticle
dc.subjectActivity-based model
dc.subjectDynamic traffic assignment
dc.subjectMATSim
dc.subjectComputing time
dc.subjectAgent-based model
dc.subjectVarying time step-based approach
dc.subjectLarge-scale simulation
dc.titleAn improvement in MATSim computing time for large-scale travel behaviour microsimulation
dc.typeArticle
dc.date.updated2021-02-09T16:33:53Z
prism.endingPage214
prism.issueIdentifier1
prism.publicationNameTransportation
prism.startingPage193
prism.volume48
dc.identifier.doi10.17863/CAM.64515
rioxxterms.versionofrecord10.1007/s11116-019-10048-0
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1572-9435
pubs.funder-project-idNational Natural Science Foundation of China (51678044, 71401012)
pubs.funder-project-idNatural Science Foundation of Hebei Province (E2016513016)
pubs.funder-project-idERC Starting Grant (678799)
pubs.funder-project-idFundamental Research Funds for the Central Universities (2017JBZ106)
pubs.funder-project-idHong Kong Polytechnic University (BE2J)


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)