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Inferring Structural Models of Travel Behavior: An Inverse Reinforcement Learning Approach
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Inferring Structural Models of Travel Behavior: An Inverse Reinforcement Learning Approach. |
| 개인저자 | Feygin, Sidney. |
| 단체저자명 | University of California, Berkeley. Civil and Environmental Engineering. |
| 발행사항 | [S.l.]: University of California, Berkeley., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 135 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438325180 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Alexey Pozdnukhov. |
| 요약 | Large volumes of digital human trajectories at high spatiotemporal resolution have become increasingly available to researchers and public entities. Derived from anonymized cellular records and social network postings, fine-grained mobility trac |
| 요약 | However, state-of-the-art machine-learning and discrete-choice frameworks do not consider the dynamics of daily mobility decisions at the individual level. Existing methods also do not take into account strategic, interdependent interactions bet |
| 요약 | Therefore, in order to take better advantage of future and emerging technologies as tools to forge cooperative and sustainable relationships between citizens, governments, and the built environment, this thesis develops a framework for data-driv |
| 일반주제명 | Civil engineering. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
