Predicting shuttle arrival time in istanbul

Name: Predicting shuttle arrival time in istanbul
Author: Çoban, Selami, Sanchez-Anguix, V., Aydoğan, Reyhan

İsim	Predicting shuttle arrival time in istanbul
Yazar	Çoban, Selami, Sanchez-Anguix, V., Aydoğan, Reyhan
Basım Tarihi:	2020
Basım Yeri	- Springer Nature
Konu	Smart cities, Transportation, Information fusion, Data science
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2194-5357
Kayıt Numarası	7b3c578b-c863-4086-b902-b40c95cf87af
Lokasyon	Computer Science
Tarih	2020
Örnek Metin	Nowadays, transportation companies look for smart solutions in order to improve quality of their services. Accordingly, an intercity bus company in Istanbul aims to improve their shuttle schedules. This paper proposes revising scheduling of the shuttles based on their estimated travel time in the given timeline. Since travel time varies depending on the date of travel, weather, distance, we present a prediction model using both travel history and additional information such as distance, holiday, and weather. The results showed that Random Forest algorithm outperformed other methods and adding additional features increased its accuracy rate.
Editör	Herrera, F., Matsui, K., Rodriguez Gonzalez, S.
DOI	10.1007/978-3-030-23887-2_6
Cilt	1003

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Özyeğin Üniversitesi

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Predicting shuttle arrival time in istanbul

Yazar Çoban, Selami, Sanchez-Anguix, V., Aydoğan, Reyhan

Basım Tarihi 2020

Basım Yeri - Springer Nature

Konu Smart cities, Transportation, Information fusion, Data science

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2194-5357

Kayıt Numarası 7b3c578b-c863-4086-b902-b40c95cf87af

Lokasyon Computer Science

Tarih 2020

Örnek Metin Nowadays, transportation companies look for smart solutions in order to improve quality of their services. Accordingly, an intercity bus company in Istanbul aims to improve their shuttle schedules. This paper proposes revising scheduling of the shuttles based on their estimated travel time in the given timeline. Since travel time varies depending on the date of travel, weather, distance, we present a prediction model using both travel history and additional information such as distance, holiday, and weather. The results showed that Random Forest algorithm outperformed other methods and adding additional features increased its accuracy rate.

Editör Herrera, F., Matsui, K., Rodriguez Gonzalez, S.

DOI 10.1007/978-3-030-23887-2_6

Cilt 1003