Detecting Fraudulent Taxi Drivers: Overview

This review paper provides an overview of the techniques and approaches employed in the detection of fraudulent taxi drivers. By analyzing various studies and methodologies, the significance of addressing this issue in the transportation industry is highlighted. Different approaches, including anomaly detection techniques, machine learning algorithms, and network analysis, are discussed in the context of identifying suspicious patterns and behaviors exhibited by fraudulent taxi drivers. The utilization of large-scale transaction data, GPS data, and other relevant information is emphasized for effective fraud detection. The effectiveness of techniques such as clustering, classification, outlier detection, and feature selection in detecting anomalies and fraudulent activities in taxi services is explored. The integration of spatial, temporal, and cost factors is shown to enhance the accuracy and efficiency of fraud detection systems. However, challenges such as the evolving nature of fraudulent tactics, real-time detection requirements, and privacy concerns associated with data collection and analysis persist. Further research and development efforts are necessary to address these challenges and advance the field of detecting fraudulent taxi drivers. Overall, this paper provides a valuable overview of the techniques and approaches employed in the detection of fraudulent taxi drivers, enabling transportation authorities and service providers to proactively combat fraudulent activities, enhance passenger safety, and maintain the integrity of the taxi industry.