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003 OCoLC;

005 20190311081203.0;

006 m o d f ;

007 cr |||||||||||;

008 190308s2019 couab ob f100 0 eng d;

035 a| (Sirsi) o1089449747;

049 a| ERE#;

074 a| 0430-P-04 (online);

086 0 a| E 9.17:NREL/CP-5400-72704;

100 1 a| Hou, Yi,e| author.=| ^A1374029;

245 12 a| A deep learning approach for TNC trip demand prediction considering spatial-temporal features :b| preprint /c| Yi Hou [and four others].;

246 30 a| Deep learning approach for transportation network companies trip demand prediction considering spatial-temporal features;

264 1 a| Golden, CO :b| National Renewable Energy Laboratory,c| 2019.;

300 a| 1 online resource (7 pages) :b| color illustrations, color map.;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

490 1 a| Conference paper NREL/CP ;v| 5400-72704;

500 a| "Presented at Transportation Research Board (TRB) 98th Annual Meeting Washington, D.C., January 13-17, 2019.";

500 a| "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.";

500 a| CRDP Program record.;

504 a| Includes bibliographical references (7 pages).;

536 b| DE-AC36-08GO28308;

588 a| Description based on online resource, PDF version; title from title page (NREL, viewed Mar. 8, 2019).;

610 20 a| National Research Council (U.S.).b| Transportation Research Board.b| Annual Meeting.=| ^A839614;

650 0 a| Artificial intelligence.=| ^A15394;

710 2 a| National Renewable Energy Laboratory (U.S.)e| issuing body.=| ^A367790;

830 0 a| Conference paper (National Renewable Energy Laboratory (U.S.)) ;v| 5400-72704.=| ^A755847;

856 40 u| https://purl.fdlp.gov/GPO/gpo117172;

949 a| Click on web addressw| asish| joyner94;

596 a| 1;

998 a| 5090439;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5090439-1001l| JNETm| JOYNERr| Ys| Yt| JNE1DOu| 4/11/2019x| EDOCUMENTz| JERESOURCE;

A deep learning approach for TNC trip demand prediction considering spatial-temporal features : preprint / Yi Hou [and four others].

Author/creator	Hou, Yi author.
Other author	National Renewable Energy Laboratory (U.S.) issuing body.
Format	Electronic
Publication	Golden, CO : National Renewable Energy Laboratory, 2019.
Description	1 online resource (7 pages) : color illustrations, color map.
Supplemental Content	https://purl.fdlp.gov/GPO/gpo117172
Subjects	National Research Council (U.S.). Transportation Research Board. Annual Meeting. Artificial intelligence.

Portion of title	Deep learning approach for transportation network companies trip demand prediction considering spatial-temporal features
Series	Conference paper NREL/CP ; 5400-72704 Conference paper (National Renewable Energy Laboratory (U.S.)) ; 5400-72704. ^A755847
General note	"Presented at Transportation Research Board (TRB) 98th Annual Meeting Washington, D.C., January 13-17, 2019."
General note	"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."
General note	CRDP Program record.
Bibliography note	Includes bibliographical references (7 pages).
Funding information	DE-AC36-08GO28308
Source of description	Description based on online resource, PDF version; title from title page (NREL, viewed Mar. 8, 2019).
GPO item number	0430-P-04 (online)
Govt. docs number	E 9.17:NREL/CP-5400-72704

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A deep learning approach for TNC trip demand prediction considering spatial-temporal features : preprint / Yi Hou [and four others].

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