Next Article in Journal
Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling
Previous Article in Journal
Corporate Social Responsibility as an Antecedent of Innovation, Reputation, Performance, and Competitive Success: A Multiple Mediation Analysis
Previous Article in Special Issue
Platform Growth Model: The Four Stages of Growth Model
Open AccessArticle

Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method

1
Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea
2
Future Strategy Research Center, Land & Housing Institute, Daejeon 34047, Korea
3
DataWiz Ltd., Mokwon University, Daejeon 35349, Korea
4
Center of Infrastructure Asset Management, Hanbat National University, Daejeon 34158, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(20), 5615; https://doi.org/10.3390/su11205615
Received: 19 August 2019 / Revised: 1 October 2019 / Accepted: 8 October 2019 / Published: 12 October 2019
(This article belongs to the Special Issue Sustainability in 2nd IT Revolution with Dynamic Open Innovation)
It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city. View Full-Text
Keywords: machine learning; ride-hailing service; decision tree; trip distance machine learning; ride-hailing service; decision tree; trip distance
Show Figures

Figure 1

MDPI and ACS Style

Do, M.; Byun, W.; Shin, D.K.; Jin, H. Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method. Sustainability 2019, 11, 5615.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop