T-Watcher: A New Visual Analytic System for Effective Traffic Surveillance

• Main Authors: PU, Jiansu, LIU, Siyuan, DING, Ye, QU, Huamin, NI, Lionel
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/3473; https://ink.library.smu.edu.sg/context/sis_research/article/4474/viewcontent/C54___T_Watcher_A_New_Visual_Analytic_System_for_Effective_Traffic_Surveillance__MDM2013_.pdf
• Institution: Singapore Management University

Nowadays, big cities are suffering from severe traffic congestion as a result of the continuing increase in vehicles. Taxis equipped with GPS can be viewed as sensors of the traffic situation in city. However, trajectory data generated by taxi’s GPS traces are often high-dimensional and contain large spatial and temporal attributes, which pose challenges for analysts. In this paper, based on taxi trajectory data, we present an interactive visual analytics system, T-Watcher, for monitoring and analyzing complex traffic situations in big cities. Users are able to use a carefully designed interface to monitor and inspect data interactively from three levels (region, road and vehicle views). We develop visualization method to monitor and analyze traffic patterns for abnormal behaviors detection. In the region view of our system, global temporal changes in spatial evolution will be presented to users and can be interactively explored. Road view shows temporal changes to the traffic situations of significant segments of roads. Vehicle view uses a novel visualization method to track individual vehicles. Furthermore, the three views integrate important statistical and historical information related to traffic, which illustrate temporal changes of the traffic. We find the fact that this design can help users explore past data while monitoring traffic. We test our system on a real-life vehicle dataset collected from thousands of taxis and obtained some interesting findings. The experimental results confirm the effectiveness and efficiency of the proposed visual detection method. The analysis of the results also shows that our system is capable of effectively monitoring traffic and detecting abnormal traffic patterns.