File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Connected population synthesis for transportation simulation

TitleConnected population synthesis for transportation simulation
Authors
KeywordsStructural learning
Exponential random graph model
Population synthesis
Cellular data
Mixed integer programming
Bayesian networks
Agent-based modeling
Transportation simulation
Issue Date2019
Citation
Transportation Research Part C: Emerging Technologies, 2019, v. 103, p. 1-16 How to Cite?
Abstract© 2019 Elsevier Ltd Agent-based modeling in transportation problems requires detailed information on each of the agents that represent the population in the region of a study. To extend the agent-based transportation modeling with social influence, a connected synthetic population with both synthetic features and its social networks need to be simulated. However, either the traditional manually-collected household survey data (ACS) or the recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behavior to generate connected synthetic populations. The generated populations coupled with recent advances in graph (social networks) algorithms can be used for testing transportation simulation scenarios with different social factors.
Persistent Identifierhttp://hdl.handle.net/10722/296190
ISSN
2023 Impact Factor: 7.6
2023 SCImago Journal Rankings: 2.860
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Danqing-
dc.contributor.authorCao, Junyu-
dc.contributor.authorFeygin, Sid-
dc.contributor.authorTang, Dounan-
dc.contributor.authorShen, Zuo Jun(Max)-
dc.contributor.authorPozdnoukhov, Alexei-
dc.date.accessioned2021-02-11T04:53:01Z-
dc.date.available2021-02-11T04:53:01Z-
dc.date.issued2019-
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2019, v. 103, p. 1-16-
dc.identifier.issn0968-090X-
dc.identifier.urihttp://hdl.handle.net/10722/296190-
dc.description.abstract© 2019 Elsevier Ltd Agent-based modeling in transportation problems requires detailed information on each of the agents that represent the population in the region of a study. To extend the agent-based transportation modeling with social influence, a connected synthetic population with both synthetic features and its social networks need to be simulated. However, either the traditional manually-collected household survey data (ACS) or the recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behavior to generate connected synthetic populations. The generated populations coupled with recent advances in graph (social networks) algorithms can be used for testing transportation simulation scenarios with different social factors.-
dc.languageeng-
dc.relation.ispartofTransportation Research Part C: Emerging Technologies-
dc.subjectStructural learning-
dc.subjectExponential random graph model-
dc.subjectPopulation synthesis-
dc.subjectCellular data-
dc.subjectMixed integer programming-
dc.subjectBayesian networks-
dc.subjectAgent-based modeling-
dc.subjectTransportation simulation-
dc.titleConnected population synthesis for transportation simulation-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.trc.2018.12.014-
dc.identifier.scopuseid_2-s2.0-85063749712-
dc.identifier.volume103-
dc.identifier.spage1-
dc.identifier.epage16-
dc.identifier.isiWOS:000471361900001-
dc.identifier.issnl0968-090X-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats