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020 ▼a 9780438325180
035 ▼a (MiAaPQ)AAI10821706
035 ▼a (MiAaPQ)berkeley:17945
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 624
1001 ▼a Feygin, Sidney.
24510 ▼a Inferring Structural Models of Travel Behavior: An Inverse Reinforcement Learning Approach.
260 ▼a [S.l.] : ▼b University of California, Berkeley., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 135 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Alexey Pozdnukhov.
5021 ▼a Thesis (Ph.D.)--University of California, Berkeley, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0028.
650 4 ▼a Civil engineering.
650 4 ▼a Artificial intelligence.
690 ▼a 0543
690 ▼a 0800
71020 ▼a University of California, Berkeley. ▼b Civil and Environmental Engineering.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0028
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998406 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자