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dc.contributor.authorMete, Süleyman
dc.contributor.authorÇelik, Erkan
dc.contributor.authorGül, Muhammet
dc.contributor.authorSerin, Faruk
dc.date.accessioned2022-07-04T15:19:38Z
dc.date.available2022-07-04T15:19:38Z
dc.identifier.citationSerin F., Mete S., Gül M., Çelik E., Deep Learning for Prediction of Bus Arrival Time in Public Transportation, "Logistics 4.0: Digital Transformation of Supply Chain Management", Turan Paksoy,Cigdem Gonul Kochan,Sadia Samar Ali, Editör, CRC, New York , Florida, ss.126-135, 2020
dc.identifier.othervv_1032021
dc.identifier.otherav_a91875e0-9d86-4bc2-9f63-ef3c21da9be7
dc.identifier.urihttp://hdl.handle.net/20.500.12627/184140
dc.identifier.urihttps://www.taylorfrancis.com/chapters/edit/10.1201/9780429327636-12/deep-learning-prediction-bus-arrival-time-public-transportation-faruk-serin-suleyman-mete-muhammet-gul-erkan-celik?context=ubx&refId=4712c58b-b234-46e7-b65f-6429cb94f3fd
dc.description.abstractThis chapter aims to apply the Long Short Term Memory (LSTM) model to predict accurate bus arrival time for public transportation system. It examines the improved methodology for real application utilization. Public transportation is an important issue for the city planner or decision maker. It has a direct impact on the all aspect of the community such as economy, education, health and entertainment activities. Number of transfers, total travel time and cost from origin to destination are important indicators for the passenger. These indicators should be optimized by passenger preferences. The bus arrival time information can decrease the passenger waiting time, make passenger informative and thus able to arrange their trip plans and choose suitable travelling routes. S. Hochreiter and J. Schmidhuber developed the LSTM network as a special kind of recurrent neural network. It has special structures of memory blocks and cells and has been successful in prediction for different application areas.
dc.language.isoeng
dc.publisherCRC, New York 
dc.subjectMühendislik ve Teknoloji
dc.subjectEndüstri Mühendisliği
dc.subjectMÜHENDİSLİK, ENDÜSTRİYEL
dc.subjectMühendislik
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.titleLogistics 4.0: Digital Transformation of Supply Chain Management
dc.typeKitapta Bölüm
dc.contributor.departmentMersin Üniversitesi , Mühendislik Fakültesi , Bilgisayar Mühendisliği Bölümü
dc.contributor.firstauthorID3407695


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