Anomaly Detection in Region Mobility Utilization Using Daily Taxi Trajectory Dataset

Anomaly detection in urban big datasets is getting wide attention with the presence of different and various urban big data sources. Urban anomaly detection is an important application area because discovered anomalies in urban areas would provide essential information about urban areas and their utilization, especially human mobility analytics and traffic condition monitoring. In the literature, there are several studies performed for urban anomaly detection using taxi trajectory datasets, such as events detection, regional urban anomaly detection and traffic incident detection. In this study, anomaly detection in regional mobility utilization of daily taxi trajectory datasets is performed based on DBSCAN clustering algorithm. A new algorithm and a threshold value are proposed to detect taxi regions as normal and anomalous for both incoming and outgoing taxi trip records. Experiments are performed on New York taxi trajectory big dataset and the experimental results show that proposed algorithm is efficient on detecting regions as normal or anomalous based on daily taxi trip record counts.