Abstract

This review includes the methods and tactics involved in the detection of dishonest taxi drivers, explaining their vital significance within the transport sector. It looks into different studies and strategies such as detecting outlier patterns, deployment of AI, and examining social structures in order to expose wrongful actions and conduct of rogue drivers. Constant use of big transactional databases, GPS systems and details also raises the quality of fraud prevention mechanisms. The current study assesses the roles of clustering, classification and outlier detection respectively in identifying anomalies and other related frauds within the taxi service. The incorporation of time, place and money factors has been proven to be very important as it enhances the effectiveness and speed of fraud detection systems. Change is always accompanied by problems and in this instance it is the problem to adapt to the new conditions that move sovereignty further in the process and delay the answer to fraud detection challenges. In order to achieve these goals persistent efforts on development and research will be necessary. To conclude this review, tactical methods and approaches in the detection of fraudulent taxi drivers were discussed together with how service providers and other transportation agencies can reduce cost through enhancement of passenger’s security and the reputation of the sector.

Authors

Zainab S. Al-Sudani¹, Musaab Riyadha², Ali A. Titinchi³
Al-Iraqia University, Iraq¹, Mustansiriyah University, Iraq², University of Nizwa, Sultanate of Oman³

Keywords

Taxi Fraud, Global Position System (GPS), Machine Learning, Deep Learning, Decision Tree Algorithms, Shortest Path Problem