Traffic-Cascade: Mining and visualizing lifecycles of traffic congestion events using public bus trajectories

• Main Authors: KWEE, Agus Trisnajaya, CHIANG, Meng-Fen, PRASETYO, Philips Kokoh, LIM, Ee-peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Data mining; Anomaly detection; Visualization; Traffic congestion; Databases and Information Systems; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/4260; https://ink.library.smu.edu.sg/context/sis_research/article/5263/viewcontent/Traffic_Cascade_2018_pv_oa.pdf
• Institution: Singapore Management University

As road transportation supports both economic and social activities in developed cities, it is important to maintain smooth traffic on all highways and local roads. Whenever possible, traffic congestions should be detected early and resolved quickly. While existing traffic monitoring dashboard systems have been put in place in many cities, these systems require high-cost vehicle speed monitoring instruments and detect traffic congestion as independent events. There is a lack of low-cost dashboards to inspect and analyze the lifecycle of traffic congestion which is critical in assessing the overall impact of congestion, determining the possible the source(s) of congestion and its evolution. In the absence of publicly available sophisticated road sensor data which measures on-road vehicle speed, we make use of publicly available vehicle trajectory data to detect the lifecycle of traffic congestion, also known as congestion cascade. We have developed Traffic-Cascade, a dashboard system to identify traffic congestion events, compile them into congestion cascades, and visualize them on a web dashboard. Traffic-Cascade unveils spatio-temporal insights of the congestion cascades.