Author
Çoban, Selami, Sanchez-Anguix, V., Aydoğan, Reyhan
Publication Date
2020
Publication Place
-
Springer Nature
Subject
Smart cities, Transportation, Information fusion, Data science
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2194-5357
Record ID
7b3c578b-c863-4086-b902-b40c95cf87af
Library Location
Computer Science
Date
2020
Sample Text
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
