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The COVID-19 pandemic spurred major, and possibly enduring, changes in paid work. In this chapter, we explore the continuity and change in several work day dimensions, including where it is performed, the amount of time spent working, the length of the work day, and who people are with when they work, as well as variation across population subgroups. We use nationally representative data from the American Time Use Survey (ATUS) to analyze change across the 2019 to 2021 period. While the shift to working primarily at home in 2020 is dramatic and continuing into 2021, working primarily at the workplace remains the modal experience for Americans. We find differences by gender, education, parental status, and age in which workers perform their jobs at home, and we find much more continuity in how much people work and when they work.

This research investigates how consumers who are most active on Facebook during the day vs in the evening differ, differ in their ad consumption, and how advertising effects vary as a function of a key moderator: gender.

Using a survey of 281 people, the research identifies Facebook users who are more intensely using mobile social media during the day versus in the evening, and measures five Facebook mobile advertising outcomes: brand and product recall, clicking on ads, acting on ads and purchases.

The results show that women who are using social media more intensely during the day are more likely to use Facebook to seek information, hence, Facebook mobile ads tend to be more effective for these users compared to those in the evening.

This contributes to the literature by analyzing how the time of day affects social media behavior in relation to mobile advertising effectiveness, and broadening the scope of mobile advertising effectiveness research from other than just clicks on ads to include measures like brand and product recall.

By analyzing the effectiveness of mobile advertising on social media as a function of the time of day, advertisers can be more targeted in their media buys, and so better use their social media budgets, i.e. advertising is more effective for women who use social media (Facebook) more intensely during the day than for those who use social media more intensely in the evening as the former tend to seek more information than the latter.

This research extends media ecology theory by drawing on circadian rhythm research to provide a first demonstration of how the time of day relates to different uses of mobile social media, which in turn relate to social media mobile advertising consumption.

While research on social media advertising has been steadily increasing, little has been explored on how users consume ads when they engage with social media at different periods along the day. This paper extends media ecology theory by investigating time of day, drawing on the circadian rhythm literature, and how it relates to social media usage.

Systematic efforts to study students' use of informal learning spaces are crucial for determining how, when and why students use such spaces. This case study provides an example of an effort to evaluate an informal learning space on the basis of students' usage of the space and the features within the space.

Use of heatmap camera technology and a semi-structured interview with a supervisor of an informal learning space supported the mixed-methods evaluation of the space.

Findings from both the heatmap outputs and semi-structured interview suggested that students' use of the informal learning space is limited due to the location of the space on campus and circumstances surrounding students' day-to-day schedules and needs.

Findings from both the heatmap outputs and semi-structured interview suggested that students' use of the informal learning space is limited due to the location of the space on campus and circumstances surrounding students' day-to-day schedules and needs. These findings are actively contributing to the authors’ institution’s efforts surrounding planning, funding and design of other informal learning spaces on campus.

While most research on instructors' and students' use of space has taken place in formal classrooms, some higher education scholars have explored ways in which college and university students use informal spaces around their campuses (e.g. Harrop and Turpin, 2013; Ramu et al., 2022). Given the extensive time students spend on their campuses outside of formal class meetings (Deepwell and Malik, 2008), higher education institutions must take measures to better understand how their students use informal learning spaces to allocate resources toward the optimization of such spaces. This mixed-methods case study advances the emerging global discussion on how, when and why students use informal learning spaces.

This paper aims to describe how one medical library implemented a new scheduling system, initiated data analysis and modified its regularly scheduled workshop program because of evidence-based decision-making. Academic libraries that struggle with workshop attendance may use this process as a model.

Workshop registration data analysis focused on registrants' affiliation, role and location, and how registrants learned of workshops. Workshop attendance data analysis focused on which workshops, days, times of the day and months had the highest attendance. The analysis led to changes in marketing and targeted scheduling of future workshops by the time of day, day of the week and month of the year.

Data collected for four years, fall 2018 – summer 2022 (12 semesters), shows a steady increase in the number of people attending library workshops. The increase in attendance and ROI experienced after the changes implemented at Ruth Lilly Medical Library (RLML) is significant as libraries often struggle with attendance, marketing and return on investment when offering ongoing educational workshops.

Many libraries offer ongoing workshops with low attendance. This article provides an example of how one library changed software and registration and implemented evidence-based decision-making related to scheduling which may have contributed to an increase in workshop attendance. Other academic libraries might consider adopting similar software and evidence-based decision-making to improve their library workshop service.

This chapter draws on longitudinal case study research that focused on the experiences of hospitality employees working in a UK university who worked split shifts in the morning and evening while completing NVQ 2 and 3 apprenticeship training. We show how fragmented working time (Rubery, Grimshaw, Hebson, & Ugarte, 2015) rather than long hours led to the apprenticeship training further eroding an already blurred work-life boundary as workers were required to complete training activities in their non-work time which for them is during the middle of the day. We argue current depictions of the positive impact of training and development on low paid workers are decontextualized from the challenges and priorities of workers whose work-life interface is already complex because of working fragmented hours across the day. This is complicated even further by the dynamic and evolving experiences of workers themselves as they experience the highs and lows of combining paid work and training. We situate the research in the context of wider conceptual debates that call for a more inclusive approach to research on the work-life interface (Warren, 2021) and highlight implications for HR practitioners who want to offer such opportunities to low paid workers in sectors such as hospitality, while also recognizing the complex challenges such workers may face.

An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Annual capacity is used for long-term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes, so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. This study aims to provide an empirical analysis of airport capacity and demand, specifically focusing on insights related to air traffic design hours and the relationship between capacity and delay.

By reviewing the empirical relationships between hourly and annual air traffic volumes and between practical capacity and delay at 50 European airports during the period 2004–2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. The authors use information from several data sources, including EUROCONTROL, ACI and OAG. This study provides functional links between design hours and annual volumes for different airport clusters. Additionally, the authors appraise different daily traffic distribution patterns and their variation by hour of the day.

The clustering of airports with respect to their capacity, operational and traffic characteristics allows us to discover functional relationships between annual traffic and the percentage of traffic in the design hour. These relationships help the authors to propose empirical methods to derive expected traffic in design hours from annual volumes. The main conclusion is that the percentage of total annual traffic that is concentrated at the design hour maintains a predictable behavior through a “potential” adjustment with respect to the volume of annual traffic. Moreover, the authors provide an experimental link between capacity and delay so that peak hour figures can be related to factors that describe the quality of traffic operations.

The functional relationships between hourly and annual air traffic volumes and between capacity and delay, can be used to properly assess airport expansion projects or to optimize resource allocation tasks. This study offers new evidence on the nature of airport capacity and the dynamics of air traffic design hours and delay.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.