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

Sustainability

DohKyoum Shin¹  Hyeryun Jin²  Myungsik Do³  Wanhee Byun⁴ 

[1] Housing Institute, Daejeon 34047, Korea;DataWiz Ltd., Mokwon University, Daejeon 35349, Korea;Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea;;Future Strategy Research Center, Land &

关键词: machine learning;    ride-hailing service;    decision tree;    trip distance;   

DOI  :  10.3390/su11205615

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown