GitHub repository: shuhang-cai/fare-anomaly
Primary language: Jupyter Notebook
Description: With approximately 7 million people using public transport everyday in Singapore, the stability of the Automated Fare Collection (AFC) system by LTA is crucial to sustain the day-to-day operations of public transport services. The AFC system undergoes frequent software and hardware changes to support new fare products or enhance existing performance. However, such changes can introduce software bugs and performance issues. While many of these errors are caught during the testing phase, some of these errors are deployed into production. Thus, there is a need to have an anomaly detection model that can quickly flag and identify errors.