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.
Zainab S. Al-Sudani1, Musaab Riyadha2, Ali A. Titinchi3
Mustansiriyah University, Iraq1,2, University of Nizwa, Sultanate of Oman3
Taxi Fraud, Global Position System (GPS), Machine Learning, Deep Learning, Decision Tree Algorithms, Shortest Path Problem
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 16 , Issue: 1 , Pages: 3756 - 3762 )
Date of Publication :
April 2025
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