Abstract
With technical breakthroughs in the telecommunications industry, the availability of technologies such as artificial intelligence (AI) and the internet of things (IoT), etc., on-demand taxi services have become increasingly popular. The conduct and behaviour of drivers is a crucial role in the success of the company. Increasing demand for app-driven and on-demand transportation has increased the importance of driver categorisation in ensuring universal acceptance, consumer happiness, and safety. The purpose of this study is to provide a method for classifying drivers based on speed, waiting time, trip cost, and driver earnings. The two crucial multicriteria decision-making (MCDM) approaches, entropy weight method (EWM) and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), were used to rank the drivers and then classify them according to the parameters. The growing availability of taxis on demand has resulted in fierce rivalry among taxi service providers for greater service quality, prompting the development of strategies to increase client satisfaction. On the basis of the adopted technique, drivers are classified into three categories based on their desirability to customers: desirable, less desired, and least desirable, which can directly benefit both employers and customers in various ways.
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