title: Querying and Extracting Timeline Information from Road Traffic Sensor Data.
abstract: The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with limited analysis results because of the inability to cope with several types of analytical queries. In this paper, we propose the QET (querying and extracting timeline information) system-a novel analytical query processing method based on a timeline model for road traffic sensor data. To address query performance, we build a TQ-index (timeline query-index) that exploits spatio-temporal features of timeline modeling. We also propose an intuitive timeline visualization method to display congestion events obtained from specified query parameters. In addition, we demonstrate the benefit of our system through a performance evaluation using a Busan ITS dataset and a Seattle freeway dataset.
authors: Department of Big Data, Pusan National University, Busan 46241, Korea. ardi@pusan.ac.kr.
authors: Ardi
authors: A
authors: Imawan
authors: Department of Big Data, Pusan National University, Busan 46241, Korea. fitri.indra@pusan.ac.kr.
authors: Fitri Indra
authors: FI
authors: Indikawati
authors: Department of Big Data, Pusan National University, Busan 46241, Korea. jhkwon@pusan.ac.kr.
authors: Joonho
authors: J
authors: Kwon
authors: Department of Computer Science & Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA. raopr@umkc.edu.
authors: Praveen
authors: P
authors: Rao
keywords: TQ-index; historical traffic sensor data; timeline model; traffic data query processing; traffic sensor data