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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.RACCES086
Volume:
10 |
4 |
April 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Computer Science & Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
The rapid growth of digital taxi services has transformed urban transportation by providing convenient, affordable, and efficient mobility solutions. Mobile-based taxi booking platforms allow passengers to easily request rides while enabling drivers to provide services with better route management and real-time tracking. Despite these advantages, the increasing popularity of taxi platforms has also introduced several security challenges and fraudulent activities. Common fraudulent activities in taxi systems include fake ride bookings, fare manipulation, driver impersonation, unauthorized ride cancellations, and payment fraud. These issues negatively affect passengers, drivers, and service providers by reducing user trust and causing financial losses. In addition, the lack of real-time monitoring mechanisms in many taxi platforms allows fraudulent activities to occur without immediate detection. To address these challenges, this paper proposes the design and development of a Smart Taxi Fraud Detection and Prevention System. The proposed system integrates user authentication, ride monitoring, GPS tracking, and intelligent fraud detection mechanisms to identify suspicious activities. By analyzing ride data, traffic flow patterns, and transaction records, the system can detect abnormal behavior and generate alerts for administrators. The system aims to enhance transportation security by preventing fraudulent activities and ensuring transparency in taxi services. The proposed platform contributes to the development of secure smart transportation systems by improving reliability, safety, and operational efficiency.
KEYWORDS
This research presented the design and development of a Smart Taxi Fraud Detection and Prevention System aimed at improving the security and reliability of taxi services. The proposed system integrates GPS monitoring, traffic flow analysis, and statistical anomaly detection techniques to identify suspicious taxi behavior. By detecting fraudulent activities in real time, the system helps reduce financial losses and improve trust between passengers and service providers. The platform also contributes to the development of secure smart transportation systems. Future work may include the integration of machine learning algorithms and artificial intelligence techniques to further enhance the accuracy and efficiency of fraud detection in taxi services.
[1] A. Sharma and R. Gupta, “Intelligent Fraud Detection in Ride-Hailing Services Using Machine Learning,” International Journal of Intelligent Transportation Systems, vol. 12, no. 3, pp. 145–156, 2021. [2] M. Chen, Y. Li, and K. Wang, “Data-Driven Taxi Demand and Fraud Analysis Using GPS Trajectory Data,” IEEE Access, vol. 9, pp. 118764–118775, 2021. [3] S. Patel and P. Mehta, “Anomaly Detection in Transportation Systems Using Statistical Monitoring Techniques,” Journal of Transportation Technologies, vol. 11, no. 2, pp. 234–245, 2022. [4] L. Zhang and H. Zhao, “Urban Taxi Behavior Analysis Based on Traffic Flow and GPS Data,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5789–5798, 2022. [5] R. Kumar and S. Verma, “Smart Transportation Systems with Fraud Detection Mechanisms,” International Journal of Computer Applications, vol. 183, no. 45, pp. 15–21, 2023. [6] J. Wang, T. Liu, and X. Chen, “Real-Time Anomaly Detection in Ride-Sharing Platforms,” IEEE Access, vol. 11, pp. 45213–45224, 2023. [7] P. Singh and A. Kaur, “Fraud Detection in Online Taxi Booking Systems Using Data Analytics,” Journal of Smart Transportation, vol. 5, no. 1, pp. 41–52, 2024. [8] K. Lee and J. Park, “GPS-Based Vehicle Monitoring and Fraud Detection in Intelligent Transportation Systems,” Sensors, vol. 24, no. 8, pp. 3021–3035, 2024. [9] D. Anderson and M. Clark, “AI-Based Fraud Detection for Smart Mobility Platforms,” IEEE Transactions on Intelligent Systems, vol. 20, no. 2, pp. 155–168, 2025. [10] R. Gupta, S. Sharma, and P. Kumar, “Advanced Fraud Detection Techniques for Smart Taxi Services,” International Journal of Advanced Computing Research, vol. 15, no. 1, pp. 77–89, 2026.
Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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