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A Visual Analytics Technique for Analyzing the Cause and Influence of Traffic Congestion
Mingyu Pi, Hanbyul Yeon, Hyesook Son, Yun Jang
http://doi.org/10.5626/JOK.2020.47.2.195
In this paper, we present a technique to analyze the causes of traffic congestion based on the traffic flow theory. We extracted vehicle flows from the traffic data, such as GPS trajectory and Vehicle Detector data. Also, vehicle flow changes were identified by utilizing the entropy from the information theory. Then, we extracted cumulative vehicle count curves (N-curve) that can quantify the vehicle flows in the congestion area. According to the traffic flow theory, unique N-curve patterns can be observed depending on the congestion type. We build a convolution neural network classifier that can classify N-curve into four different congestion patterns. Analyzing the cause and influence of congestion is difficult and requires considerable experience and knowledge. Apparently, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated our system that can analyze the cause of traffic congestion.
Spatiotemporal Data Visualization using Gravity Model
Seokyeon Kim, Hanbyul Yeon, Yun Jang
Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.
Visual Analytics for Abnormal Event detection using Seasonal-Trend Decomposition and Serial-Correlation
In this paper, we present a visual analytics system that uses serial- correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial- correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the ‘Gyeongju Mauna Resort collapse’ and the ‘Jindo-ferry sinking’.
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