Urban traffic congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban traffic congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing traffic volume by detecting urban traffic congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of traffic congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of traffic congestion. In this paper, we propose a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, 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 that our system can classify the causes of traffic congestion and can be used efficiently in road planning.

It is not easy to predict incomplete panel data whose overall trend is not complete. For example, physical growth data for age 7-18 has been collected every a few months for up to seven years; therefore, only short growth pattern pieces exist in the data. When using previous prediction techniques, it is challenging to create a growth prediction model that reflects individual growth patterns. Also, uncertainties in predicted results emerge since the overall trend of the data is unknown. In this work, we present a predictive analysis and modeling to forecast incomplete data over time. We extend the Bayesian network model, which is relative data-driven approaches that explore similar data and weave them to create approximate inference margins. Besides, we propose a visual analytics system that enables us to design various predictive models that reflect individual growth pattern. Our visual analytics system assists us to discover new growth patterns in the process of analyzing the accuracy of previously designed predictive models. Moreover, the system allows us to optimize predictive models to fit unusual growth patterns.