Technological innovation has transformed the role of electronics in education, work, and society. However, rapid adoption and obsolescence of consumer electronics has also led to new concerns about resource consumption and waste management. Past research to address these sustainability challenges has been constrained by data that do not reflect nascent trends in product evolution and consumer adoption, thereby limiting the ability to create and assess proactive solutions. This study presents a dynamic analysis of electronic waste (e‐waste) in the United States using material flow analysis and highly resolved electronic product sales and material composition data. Findings contradict expectations that e‐waste is growing with mobile device proliferation, instead showing that the total mass of the e‐waste stream is actually declining (10% decrease since the estimated peak in 2015) with phase‐out of large, legacy products like cathode ray tube TVs. The evolving material profile of consumer electronics being purchased and disposed sees reduced risks of e‐waste toxicity from hazards like lead and mercury, but greater risks from reliance on scarce metals and product designs that limit recycling. This study highlights concerns that extended producer responsibility regulations currently implemented in many U.S. states for e‐waste management may become less effective if they continue to rely only on mass‐based collection targets. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Models of travel time perception and learning mechanisms in traffic networks are presented. Mechanisms for updating travel times in light of new experiences and for triggering and terminating the updating process are examined. Travel time perception and learning are modeled on the basis of concepts from Bayesian statistical inference. Mechanisms for triggering and terminating the learning process are modeled with the use of simple heuristic rules based on the inter-update period and thresholds that define the salience of travel times and acceptable confidence levels. These models are embedded inside a microscopic (agent-based) simulation framework and model to study their collective effects on the day-to-day behavior of traffic flows through a series of experiments. The effect of alternative travel time perception and learning mechanisms on system performance and its dynamic properties, in particular convergence, is explored.

Creating a sense of place and community is a guiding principle in designing livable and high-quality built environments. This paper presents a framework for understanding the relationship between design and people’s perceptions about a place, within an urban design context. While a large volume of literature on sense of place (SOP) already exists, the proposed framework and its application in the design field present a unique opportunity to add new knowledge to this interdisciplinary topic. This research will investigate the empirical relationship between architecture/urban design and people’s perceptions about a place and their contributions to SOP. Urban designers and architects play important and determining roles in defining the physical qualities and the characteristics of a place. However, it has always been challenging to quantify the relationship between a physical environment and a person’s emotional experience. Three urban sites were analyzed to illustrate this framework, and four physical characteristics and four perceptual qualities were cross-investigated and analyzed. This proposed framework will help architects and urban designers to gain a better understanding of SOP and placemaking techniques, eventually helping to improve urban design quality.