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Blockchain and Machine Learning for Intelligent Multiple Factor-Based Ride-Hailing Services

Zeinab Shahbazi, Yung-Cheol Byun*

IIST, Department of Computer Engineering, Jeju National University, Jeju-si, Jeju Special Self-Governing Province, 63243, Korea

* Corresponding Author: Yung-Cheol Byun. Email:

(This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)

Computers, Materials & Continua 2022, 70(3), 4429-4446.


One of the common transportation systems in Korea is calling taxis through online applications, which is more convenient for passengers and drivers in the modern area. However, the driver's passenger taxi request can be rejected based on the driver's location and distance. Therefore, there is a need to specify driver's acceptance and rejection of the received request. The security of this system is another main core to save the transaction information and safety of passengers and drivers. In this study, the origin and destination of the Jeju island South Korea were captured from T-map and processed based on machine learning decision tree and XGBoost techniques. The blockchain framework is implemented in the Hyperledger Fabric platform. The experimental results represent the features of socio-economic. The cross-validation was accomplished. Distance is another factor for the taxi trip, which in total trip in midnight is quite shorter. This process presents the successful matching of ride-hailing taxi services with the specialty of distance, the trip request, and safety based on the total city measurement.


Cite This Article

Z. Shahbazi and Y. Byun, "Blockchain and machine learning for intelligent multiple factor-based ride-hailing services," Computers, Materials & Continua, vol. 70, no.3, pp. 4429–4446, 2022.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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