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.
Original languageEnglish
Title of host publicationDistributed Computing and Artificial Intelligence
EditorsF. Herrera, K. Matsui, S. Rodriguez-Gonzalez
Place of PublicationCham
Number of pages8
ISBN (Electronic)978-3-030-23887-2
ISBN (Print)978-3-030-23886-5
Publication statusPublished - 1 Jan 2020
EventDCAI 2019: The 16th International Symposium on Distributed Computing and Artificial Intelligence - Avila , Spain
Duration: 26 Jun 201928 Jun 2019
Conference number: 16th

Publication series

NameThe Advances in Intelligent Systems and Computing book series


ConferenceDCAI 2019
City Avila

    Research areas

  • Smart cities, Transportation, Information Fusion, Data Science

ID: 57748942