TY  - JOUR
T1  - A Visual Analytics Technique for Analyzing the Cause and Influence of Traffic Congestion
AU  - Pi, Mingyu 
AU  - Yeon, Hanbyul 
AU  - Son, Hyesook 
AU  - Jang, Yun 
JO  - Journal of KIISE, JOK
PY  - 2020
DA  - 2020/1/14
DO  - 10.5626/JOK.2020.47.2.195
KW  - traffic congestion causes
KW  - visual analytics
KW  - traffic flow theory
KW  - convolutional neural network
AB  - 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.