Time-persistent regions discovery of taxi trajectory big datasets based on regional spatio-temporal velocity

With the increasing number of residents and motor vehicles in urban areas, traffic-related problems have emerged. Traffic analysis and prediction systems provide information about the city dynamics and traffic estimation on a regional basis. Several studies are performed for traffic analysis and prediction in urban datasets, including taxi trajectory datasets, however, these studies do not focus on regional traffic analysis and time-persistent regions discovery. Time-persistent regions refer to the regions that have a stable utilization and relatively stationary velocity in terms of traffic activities. In this study, a novel method is proposed to discover time-persistent regions based on regional daily velocity values using taxi trajectory big datasets. A new algorithm, namely Time-Persistent Regions Discovery algorithm (TPRD algorithm), is proposed based on the proposed method. The proposed TPRD algorithm is experimentally evaluated on TLC Taxi Trip Records big dataset of New York City and the results show that the proposed algorithm could discover time-persistent regions based on proposed interest measures and threshold values.