GLAD-TL: A Time-Sensitive Crowdsourced Model for Robust Detection of Fake Taxis in Urban Traffic Surveillance

Fake taxis pose serious risks in the transportation industry, potentially leading to traffic accidents and negative social consequences. A common approach compares a taxi’s GPS location with its visual location captured by surveillance cameras. However, accurate license plate number (LPN) recognition is often compromised by factors such as lighting, angle, and image quality, and some drivers deliberately avoid exposing plates to cameras. Accurately estimating each plate number is thus essential for identifying fake taxis, especially in high-traffic areas like airports and railway stations, where vehicles pass through multiple fields of view. This article introduces GLAD-TL, a crowdsourced probabilistic model with time-dependent low-rank constraints that enhances LPN accuracy by adapting to variations in camera performance. By modeling differences in camera capability and recognition task difficulty over time, GLAD-TL provides a robust and scalable solution for urban fake taxi detection without additional infrastructure. Experiments on real-world data demonstrate the model’s ability to achieve a detection accuracy of 76.10%, underscoring its potential for deployment in dense urban environments.