User-Centered Visualization for Forecasting Data in Mobility Services; 사용자 중심 모빌리티 서비스 예측 데이터 시각화 연구

초록

Background This study focuses on the uncertainty inherent in forecasting data and examines the effectiveness of uncertainty representation for user-centered data visualization. We propose a practical design approach through a case study of mobility services and explore the potential for its extension to future forecasting systems. Methods A comprehensive literature review on forecasting data visualization was conducted, with a particular emphasis on uncertainty visualization theories including core concepts, principles of representation, and prior studies from a communication perspective. The study collected screens from actual mobility services to systematically extract forecasting types as well as to identify current visualization practices, and conducted a user study on uncertainty visualization by designing experimental stimuli for route congestion forecasting information. Results Although the reliability of existing forecasting data was generally high; users were significantly influenced by subjective factors which leads them to interpret and apply the information in varying ways in risk situations. Since the addition of uncertainty representation increases the cognitive load, stepwise color differentiation integrated with map elements was more effective than using numbers or graphs for visualization. Subsequently, final stimuli reflecting user requirement, such as variable integration, verbal expression, and probability notation, were statistically validated, revealing improvements in perceived information usefulness. Conclusions The representation of uncertainty in the forecasting visualization enhances users’ intuitive understanding and plays a meaningful role in traffic decision making. However, achieving a balanced design between informational clarity and visual complexity emerges as a key challenge. Future research should involve quantitative experiments using real-world data and conduct more context-specific evaluations of visualization strategies. © This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted educational and non-commercial use, provided the original work is properly cited.

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