Identifying driver heterogeneity in car-following based on a random coefficient model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Ikki | - |
dc.contributor.author | Kim, Taewan | - |
dc.contributor.author | Sohn, Keemin | - |
dc.date.accessioned | 2021-06-23T02:22:31Z | - |
dc.date.available | 2021-06-23T02:22:31Z | - |
dc.date.issued | 2013-11 | - |
dc.identifier.issn | 0968-090X | - |
dc.identifier.issn | 1879-2359 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/26678 | - |
dc.description.abstract | As computing capabilities have advanced, random coefficient models have emerged as the mainstream method of dealing with traveler behaviors in transport studies. Car-following models with random coefficients, however, are rarely used, although many kinds of car-following models have been attempted. For the present study, we proposed a rigorous methodology to calibrate a GM-type car-following model with random coefficients, which could account for the heterogeneity across drivers who respond differently to stimuli. To avert both the curse of dimensionality and the lack of empirical identification, which can be a part of dealing with a simulated likelihood, a robust algorithm called the expectation-maximization (EM) was adopted. The calibration results confirmed that random coefficients of the model fluctuated considerably across drivers, and were correlated with each other. The exclusion of these facts might be a potential reason for the difficulty in simulating real traffic situations based on a single car-following model with constant coefficients. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Pergamon Press Ltd. | - |
dc.title | Identifying driver heterogeneity in car-following based on a random coefficient model | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1016/j.trc.2013.08.003 | - |
dc.identifier.scopusid | 2-s2.0-84883792313 | - |
dc.identifier.wosid | 000327912800004 | - |
dc.identifier.bibliographicCitation | Transportation Research Part C: Emerging Technologies, v.36, pp 35 - 44 | - |
dc.citation.title | Transportation Research Part C: Emerging Technologies | - |
dc.citation.volume | 36 | - |
dc.citation.startPage | 35 | - |
dc.citation.endPage | 44 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Maximum principle | - |
dc.subject.keywordPlus | algorithm | - |
dc.subject.keywordPlus | calibration | - |
dc.subject.keywordPlus | car use | - |
dc.subject.keywordPlus | computer simulation | - |
dc.subject.keywordPlus | numerical model | - |
dc.subject.keywordPlus | road transport | - |
dc.subject.keywordPlus | traffic management | - |
dc.subject.keywordPlus | transportation development | - |
dc.subject.keywordPlus | transportation planning | - |
dc.subject.keywordAuthor | Car-following model | - |
dc.subject.keywordAuthor | Random coefficient | - |
dc.subject.keywordAuthor | Heterogeneity | - |
dc.subject.keywordAuthor | Expectation-maximization algorithm | - |
dc.subject.keywordAuthor | Maximum simulated likelihood | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0968090X13001678?pes=vor | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.