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- Publisher Website: 10.1016/j.trc.2018.12.014
- Scopus: eid_2-s2.0-85063749712
- WOS: WOS:000471361900001
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Article: Connected population synthesis for transportation simulation
Title | Connected population synthesis for transportation simulation |
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Authors | |
Keywords | Structural learning Exponential random graph model Population synthesis Cellular data Mixed integer programming Bayesian networks Agent-based modeling Transportation simulation |
Issue Date | 2019 |
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 Identifier | http://hdl.handle.net/10722/296190 |
ISSN | 2023 Impact Factor: 7.6 2023 SCImago Journal Rankings: 2.860 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Danqing | - |
dc.contributor.author | Cao, Junyu | - |
dc.contributor.author | Feygin, Sid | - |
dc.contributor.author | Tang, Dounan | - |
dc.contributor.author | Shen, Zuo Jun(Max) | - |
dc.contributor.author | Pozdnoukhov, Alexei | - |
dc.date.accessioned | 2021-02-11T04:53:01Z | - |
dc.date.available | 2021-02-11T04:53:01Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Transportation Research Part C: Emerging Technologies, 2019, v. 103, p. 1-16 | - |
dc.identifier.issn | 0968-090X | - |
dc.identifier.uri | http://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.language | eng | - |
dc.relation.ispartof | Transportation Research Part C: Emerging Technologies | - |
dc.subject | Structural learning | - |
dc.subject | Exponential random graph model | - |
dc.subject | Population synthesis | - |
dc.subject | Cellular data | - |
dc.subject | Mixed integer programming | - |
dc.subject | Bayesian networks | - |
dc.subject | Agent-based modeling | - |
dc.subject | Transportation simulation | - |
dc.title | Connected population synthesis for transportation simulation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.trc.2018.12.014 | - |
dc.identifier.scopus | eid_2-s2.0-85063749712 | - |
dc.identifier.volume | 103 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 16 | - |
dc.identifier.isi | WOS:000471361900001 | - |
dc.identifier.issnl | 0968-090X | - |