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A deep learning approach for TNC trip demand prediction considering spatial-temporal features : preprint / Yi Hou [and four others].
- Format:
- Book
- Government document
- Author/Creator:
- Hou, Yi, author.
- Series:
- Conference paper (National Renewable Energy Laboratory (U.S.)) ; 5400-72704.
- Conference paper NREL/CP ; 5400-72704
- Language:
- English
- Subjects (All):
- National Research Council (U.S.). Transportation Research Board. Annual Meeting.
- National Research Council (U.S.).
- Artificial intelligence.
- Artificial Intelligence.
- artificial intelligence.
- Medical Subjects:
- Artificial Intelligence.
- Genre:
- proceedings (reports)
- Conference papers and proceedings
- Conference papers and proceedings.
- Physical Description:
- 1 online resource (7 pages) : color illustrations, color map
- Other Title:
- Deep learning approach for transportation network companies trip demand prediction considering spatial-temporal features
- Place of Publication:
- Golden, CO : National Renewable Energy Laboratory, 2019.
- Notes:
- "Presented at Transportation Research Board (TRB) 98th Annual Meeting Washington, D.C., January 13-17, 2019."
- "NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy, Operated by the Alliance for Sustainable Energy, LLC."
- Includes bibliographical references (7 page).
- Online resource, PDF version; title from title page (NREL, viewed March 8, 2019).
- OCLC:
- 1089449747
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