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Open AccessArticle

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

Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea
Future Strategy Research Center, Land & Housing Institute, Daejeon 34047, Korea
DataWiz Ltd., Mokwon University, Daejeon 35349, Korea
Center of Infrastructure Asset Management, Hanbat National University, Daejeon 34158, Korea
Author to whom correspondence should be addressed.
Sustainability 2019, 11(20), 5615;
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
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Do, M.; Byun, W.; Shin, D.K.; Jin, H. Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method. Sustainability 2019, 11, 5615.

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