| LDR | | 02874nmm uu200457 4500 |
| 001 | | 000000333191 |
| 005 | | 20240805172352 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438324398 |
| 035 | |
▼a (MiAaPQ)AAI10813998 |
| 035 | |
▼a (MiAaPQ)berkeley:17783 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 385 |
| 100 | 1 |
▼a Yin, Mogeng. |
| 245 | 10 |
▼a Activity-based Urban Mobility Modeling from Cellular Data. |
| 260 | |
▼a [S.l.] :
▼b University of California, Berkeley.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 105 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: A. |
| 500 | |
▼a Adviser: Alexei Pozdnoukhov. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a Transportation has been one of the defining challenges of our age. Transportation decision makers are facing difficult questions in making informed decisions. Activity-based travel demand models are becoming essential tools used in transportatio |
| 520 | |
▼a In this dissertation, we explore a framework that develops the state-of-the-art generative activity-based urban mobility models from raw cellular data, with the capability of inferring activity types for complementing activity-based travel deman |
| 520 | |
▼a To do so, we first present a method of extracting user stay locations from raw and noisy cellular data while not over-filtering short-term travel. Significant locations such as home and work places are inferred. Along this pre-processing pipelin |
| 520 | |
▼a With the processed yet unlabeled activity sequences, we improve the state-of-the-art generative activity-based urban mobility models step by step. First, we designed a method of collecting ground truth activities with the help from short range d |
| 520 | |
▼a We apply the models to the data collected by a major network carrier serving millions of users in the San Francisco Bay Area. Our activity-based urban mobility model is experimentally validated with three independent data sources: aggregated sta |
| 520 | |
▼a One direct application of the urban mobility model is travel demand forecasting. Predictive models of urban mobility can help alleviate traffic congestion problems in future cities. State-of-the-art in travel demand forecasting is mainly concern |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Transportation. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0709 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Civil and Environmental Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01A(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998100
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |