Light rail passenger demand forecasting by artificial neural networks

Dilay Çelebi*, Bersam Bolat, Demet Bayraktar

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

10 Atıf (Scopus)

Özet

The success of strategic and detailed planning of public transportationhighly depends on accurate demand information data. Short-term forecasting isthe key to the success of transportation operations planning such astime-tabling and seat allocation. This study adopts neural networks to developshort-term passenger demand forecasting models to be used in operationalmanagement of light rail services. A multi-layer perceptron (MLP) model ispreferred due to not only its simple architecture but also proven success ofsolving approximation problems. For eliminating the significant seasonality intime slots, each time slot is handled independent of the others, and anartificial neural network based on daily data is developed for each. Regardingto the 74 different time slots, 74 different neural networks are trained byhistory data. Three illustrative examples are demonstrated on one of the timeslots and performance of the forecast models are evaluated based on mean squareerrors (MSE) and mean absolute percentage errors (MAPE).

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2009 International Conference on Computers and Industrial Engineering, CIE 2009
YayınlayanIEEE Computer Society
Sayfalar239-243
Sayfa sayısı5
ISBN (Basılı)9781424441365
DOI'lar
Yayın durumuYayınlandı - 2009
Etkinlik2009 International Conference on Computers and Industrial Engineering, CIE 2009 - Troyes, France
Süre: 6 Tem 20099 Tem 2009

Yayın serisi

Adı2009 International Conference on Computers and Industrial Engineering, CIE 2009

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???2009 International Conference on Computers and Industrial Engineering, CIE 2009
Ülke/BölgeFrance
ŞehirTroyes
Periyot6/07/099/07/09

Parmak izi

Light rail passenger demand forecasting by artificial neural networks' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

Alıntı Yap