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Article: Estimating commuting matrix and error mitigation – A complementary use of aggregate travel survey, location-based big data and discrete choice models

TitleEstimating commuting matrix and error mitigation – A complementary use of aggregate travel survey, location-based big data and discrete choice models
Authors
KeywordsTransport modelling
Matrix estimation
Commuting
Mobile phone data
Location-based big data
Issue Date2021
Citation
Travel Behaviour and Society, 2021, v. 25, p. 102-111 How to Cite?
AbstractThe prevalence of location-based big data has opened a new research frontier for estimating origin–destination commuting matrices for cities where granular flow data are not yet available from official sources. However, investigations into estimation errors and potential correction methods have been rare in the literature. To address the research gap, this paper first compares the performance of two estimated commuting matrices for Shanghai, derived by two distinct matrix estimation methods, namely a big-data approach using mobile phone signalling data and a discrete choice model for simulating the residential location of commuters. The empirical results indicate an outstanding analytical complementarity of the two approaches. A novel method is then proposed for mitigating the errors associated with the big-data approach. The proposed method features a selective blending of the big-data based flow estimation and the model-based estimation. By comparing the blended flow estimation with benchmark travel statistics, we find that the proposed method would significantly reduce the estimation errors and hence improve the robustness of the estimated matrix. It is expected that the proposed method will set a new standard for correcting potential errors in big-data based flow estimation.
Persistent Identifierhttp://hdl.handle.net/10722/301875
ISSN
2020 Impact Factor: 4.983
2020 SCImago Journal Rankings: 1.695

 

DC FieldValueLanguage
dc.contributor.authorWan, Li-
dc.contributor.authorYang, Tianren-
dc.contributor.authorJin, Ying-
dc.contributor.authorWang, De-
dc.contributor.authorShi, Cheng-
dc.contributor.authorYin, Zhenxuan-
dc.contributor.authorCao, Mengqiu-
dc.contributor.authorPan, Haozhi-
dc.date.accessioned2021-08-19T02:20:55Z-
dc.date.available2021-08-19T02:20:55Z-
dc.date.issued2021-
dc.identifier.citationTravel Behaviour and Society, 2021, v. 25, p. 102-111-
dc.identifier.issn2214-367X-
dc.identifier.urihttp://hdl.handle.net/10722/301875-
dc.description.abstractThe prevalence of location-based big data has opened a new research frontier for estimating origin–destination commuting matrices for cities where granular flow data are not yet available from official sources. However, investigations into estimation errors and potential correction methods have been rare in the literature. To address the research gap, this paper first compares the performance of two estimated commuting matrices for Shanghai, derived by two distinct matrix estimation methods, namely a big-data approach using mobile phone signalling data and a discrete choice model for simulating the residential location of commuters. The empirical results indicate an outstanding analytical complementarity of the two approaches. A novel method is then proposed for mitigating the errors associated with the big-data approach. The proposed method features a selective blending of the big-data based flow estimation and the model-based estimation. By comparing the blended flow estimation with benchmark travel statistics, we find that the proposed method would significantly reduce the estimation errors and hence improve the robustness of the estimated matrix. It is expected that the proposed method will set a new standard for correcting potential errors in big-data based flow estimation.-
dc.languageeng-
dc.relation.ispartofTravel Behaviour and Society-
dc.subjectTransport modelling-
dc.subjectMatrix estimation-
dc.subjectCommuting-
dc.subjectMobile phone data-
dc.subjectLocation-based big data-
dc.titleEstimating commuting matrix and error mitigation – A complementary use of aggregate travel survey, location-based big data and discrete choice models-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.tbs.2021.04.012-
dc.identifier.scopuseid_2-s2.0-85109433230-
dc.identifier.volume25-
dc.identifier.spage102-
dc.identifier.epage111-

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