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dc.contributor.authorRongrong Hong
dc.contributor.authorWenming Rao
dc.contributor.authorDong Zhou
dc.contributor.authorChengchuan An
dc.contributor.authorZhenbo Lu
dc.contributor.authorJingxin Xia
dc.date.accessioned2020-03-16T21:16:23Z
dc.date.available2020-03-16T21:16:23Z
dc.date.created2020-03-15 00:34
dc.date.issued2020-02-01
dc.identifieroai:doaj.org/article:857bf66f0f4a4a36ae01d103de39e671
dc.identifier2071-1050
dc.identifier10.3390/su12051764
dc.identifierhttps://doaj.org/article/857bf66f0f4a4a36ae01d103de39e671
dc.identifier.urihttp://hdl.handle.net/20.500.12424/3926836
dc.description.abstractIdentifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters’ route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.
dc.languageEN
dc.publisherMDPI AG
dc.relation.ispartofhttps://www.mdpi.com/2071-1050/12/5/1764
dc.relation.ispartofhttps://doaj.org/toc/2071-1050
dc.sourceSustainability, Vol 12, Iss 5, p 1764 (2020)
dc.subjectcommuting pattern
dc.subjectcommuter feature extraction
dc.subjecthierarchical clustering
dc.subjectautomatic license plate recognition data
dc.subjectEnvironmental effects of industries and plants
dc.subjectTD194-195
dc.subjectRenewable energy sources
dc.subjectTJ807-830
dc.subjectEnvironmental sciences
dc.subjectGE1-350
dc.titleCommuting Pattern Recognition Using a Systematic Cluster Framework
dc.typeArticle
ge.collectioncode2071-1050
ge.dataimportlabelOAI metadata object
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ge.lastmodificationdate2020-03-15 00:34
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ge.oai.setnameLCC:Environmental effects of industries and plants
ge.oai.setnameLCC:Renewable energy sources
ge.oai.setnameLCC:Environmental sciences
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ge.linkhttps://doaj.org/article/857bf66f0f4a4a36ae01d103de39e671


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