Traffic control systems, encompassing both air traffic control (ATC) and vessel traffic service (VTS), play a pivotal role in ensuring the safety and efficiency of transportation across aviation and maritime domains. High situation awareness (SA), which includes the ability to perceive information (Level 1 SA), comprehend the meaning (Level 2 SA), and predict future states (Level 3 SA), is crucial for traffic controllers to manage their respective traffic environments effectively under various workload conditions. Eye-tracking (ET) is a prominent physiological method to quantitatively assess operators’ cognitive workload and situation awareness as eye movements are closely related to one’s attention and information-processing mechanisms. This study aims to investigate the effects of SA levels and workload conditions on traffic controllers’ eye movement patterns using ET technology. Using ATC as a case study, experiments were conducted to collect eye movement data of 26 participants while they monitored aircraft on a simulated radar screen and answered freeze-probe queries of different SA levels (Level 1 and Level 2). Workload conditions were varied by the number of aircraft participants needed to monitor. Two-way repeated measures ANOVA showed significant main effects and interaction effects of SA and workload levels on certain ET metrics (e.g., total fixation duration, average fixation duration). Insights from this study could significantly enhance the existing knowledge of SA levels in traffic control tasks and reinforce the evidence base linking eye movements to cognitive processes in a broader context. This could contribute to mitigate human error and elevate safety standards across different transportation sectors.

In modern society, the demands of sustainable urban development and the diverse needs of the populace are increasing. One of the approaches gaining attention to realize such urban planning is simulations based on urban data. However, traditional approaches often treat inhabitants as homogeneous entities. While some research has attempted to take in the complexity of individual behaviors, traditional studies have primarily modeled consumer utility using linear polynomials, which presents mathematical limitations. This study introduces a transdisciplinary approach that combines social scientific efforts to replicate complex urban conditions with engineering methods, particularly time series prediction, for simulating the decision-making processes of agents within a simulation. Utilizing urban pedestrians' data, the model constructs predictions for subsequent actions, adapting these predictions to enhance the decision-making models of agents in the simulation. The validation of this model, assessing its ability to replicate actual decision-making behaviors of individual users, indicates a promising level of reproducibility. This study provides significant insights for governmental agencies and urban developers, contributing to more efficient and effective urban planning and development strategies. Achieving sustainable urban development in this manner ensures the well-being of urban populations and the long-term viability of urban environments, demonstrating the model's potential to inform and enhance urban planning efforts.

Recent short-term usage-driven consumer behavior in the fashion industry continues to exacerbate environmental challenges. Existing sustainable strategies by brands tend to alleviate the consequences of this behavior rather than address the behavior itself. Therefore, understanding the underlying interactions in consumer decision-making is imperative. However, previous studies on sustainable fashion seem to overlook the mechanisms of the wearer's non-consciousness and the interconnectedness between wearer and the clothing. Broadening this perspective is crucial for a holistic understanding of sustainable consumption. To achieve this, the paper employs a Buddhist Mind-Only school perspective and model-based systems engineering to enable the clarification of the wearer's genuine requirements for clothing system by analyzing the interactions between clothing, wearer, and the public in the consumer decision-making process. The findings reveal that all perceptions of clothing are processed by wearer's past experiences and interpreted by themselves rather than seen as they are. Additionally, this paper identifies three wearer requirements, providing long-term value related to self-understanding across past, present, and future. By understanding themselves, wearers can recognize their needs and choose clothing that fulfills their long-term value, thereby extending the lifespan of clothing. Therefore, using mindful clothing education to teach people how to understand their needs when selecting clothing can reduce the environmental burden at the motivational level, serving as a key factor in preventing short-term usage. This paper provides insights for understanding the wearer's role in driving sustainable consumer behavior, emphasizing that a deeper self-understanding leads to more mindful purchasing decisions, ultimately reducing the environmental impact of consumption.

Abstract. In recent years, the complexity of customer behavior analysis has significantly increased due to the maturity of multichannel and omnichannel purchasing. This study introduces a novel perspective by directly incorporating business practitioners into the analysis process, challenging traditional engineering approaches. In collaboration with the Japanese clothing company Graniph, we conducted an in-depth analysis of consumer behavior using customer data, transaction history, and product information recorded from 2019 to 2022.

Our research aims to provide a comprehensive analysis workflow applicable across industries to facilitate interdisciplinary data analysis and interpretation, addressing common challenges encountered in real-world data analysis through practical application. The study is structured into three phases: data preprocessing, optimal customer segmentation, and machine learning training coupled with data visualization. It involves transforming data into a format suitable for analysis, including outlier detection and data rescaling, and integrates RFM time-series modeling with k-means clustering to categorize customers into distinct groups for behavioral analysis. The final stage utilizes XGBoost (eXtreme Gradient Boosting) machine learning to identify category features influencing purchase behavior within different customer groups, presented in order of impact. This approach, exemplifying data visualization, aims to support the development and implementation of new marketing strategies by tracking changes in customer behavior triggered by these strategies.

Our research not only contributes to the engineering field by applying advanced analytical techniques in a retail context but also enriches the social sciences with deeper insights into consumer behavior, showcasing the power of transdisciplinary engineering to transform traditional business practices and drive data-driven strategy innovation in the retail industry.