Towards Meaningful University Space: Perceptions of the Quality of Open Spaces for Students

The relation between barrier-free environment perception and campus commuting satisfaction

Introduction

The COVID-19 pandemic, which began in the last quarter of 2019, has had a significant impact on urban transportation. With increasing demand for urban transport, the internal roads and public spaces of university campuses play an important role in facilitating commuting and communication between various functional zones. While considerable research has been conducted on route planning, pedestrian-vehicle segregation, and safety management in the internal transportation environment of university campuses, empirical investigations exploring barrier-free inclusive campus environment design and the subjective evaluation of road and public space users in the aftermath of the COVID-19 pandemic are lacking. Recent developments in travel behavior models and positive psychology have led to an increased focus on the correlation among subjective perceptions, attitudes, emotions, and commuting satisfaction in urban transportation and planning design.

Methods

To elucidate this relationship, a study was conducted on the new campus of Central South University in Changsha, Hunan Province, China. Using 312 valid samples, a structural equation model was constructed to analyse the relationship between commuting satisfaction and the barrier-free environment perception of university students regarding the internal transportation environment of the campus.

Results

The results revealed that individuals' instantaneous barrier-free environment perceptions and long-term established positive emotions had a significant positive effect on commuting satisfaction. Furthermore, positive emotions were found to mediate the relationship between commuting attitudes induced by COVID-19, barrier-free environment perceptions, and commuting satisfaction.

Discussion

The results of this study provide a theoretical basis for the necessity of accessibility design in the post-COVID era. In addition, this study considers the perspective of users to provide ideas for the planning and construction of barrier-free campus environments that are based on convenient and inclusive design.

Rethinking Outdoor Courtyard Spaces on University Campuses to Enhance Health and Wellbeing: The Anti-Virus Built Environment

Responding to the events surrounding the COVID-19 pandemic, this study explores how to improve health and wellbeing and reduce infections in outdoor open spaces on university campuses to maximize their potential as a response to future crises. The study identifies the relationship between human behavior (social) and the various physical and environmental elements of these spaces. A case study and mixed-methods approach were undertaken, comprising four modes of inspection: user analysis layer using questionnaires and observations to survey students’ needs and behavior; context analysis layer using space syntax and CFD to examine the space’s physical and environmental conditions; design solutions reflecting an understanding of virus transmission; and a performance analysis layer to test the performance of ‘anti-virus’ courtyards. The findings demonstrated that students are willing to use the open spaces that they used before the pandemic, at the same frequency. This indicates a need to redesign the current spaces to prevent the spread of viruses. The study highlights the social, physical, and environmental implications to be considered in designs for outdoor anti-virus spaces. It provides a comprehensive process for transforming outdoor spaces on university campuses into anti-virus spaces that meet users’ needs. These findings have implications for the designing and retrofitting of open spaces to reduce infection.

Exploring Factors Promoting Recycling Behavior in Student Housing

As climate-related issues are important and concern all aspects of the built environment, there is a need to better understand the motives underlying household recycling behavior. The purpose of the present study is twofold: to investigate factors important for explaining the recycling behavior of young people and to explore respondents’ own ideas regarding barriers to recycling. This paper reports on a survey conducted from 2020 to 2021 among residents of student housing in Stockholm, Sweden. Eight hypotheses were formulated based on earlier research and a model was constructed. Answers from 1202 respondents were first analyzed by logistic regression to test factors affecting respondents’ self-reported recycling of paper, plastic, glass, and metal. Results show that the full model containing all predictors was statistically significant. The results showed that only four of the hypotheses were confirmed. Positive attitude toward recycling, personal norms, perceived behavioral control, and perceived convenience of recycling are positively affecting recycling behavior. In addition, 673 open answers were analyzed to provide information on unforeseen factors of importance for recycling behavior. This study adds to research by testing factors affecting recycling behaviors in a national context and by identifying new possible factors of importance. The results are also of benefit to business practitioners within the construction sector or within facility management in identifying activities that would add to sustainable development.

Revealing Microclimate around Buildings with Long-Term Monitoring through the Neural Network Algorithms

The profile of urban microclimates is important in many engineering fields, such as occupant’s thermal comfort and health, and other building engineering. To predict the profile of urban microclimate, this study applies the artificial neural network and long short-term memory network predictive models, and an urban microclimate dataset was obtained with a long-term monitoring from year 2017 to 2019 with 5-min resolution including temperature, relative humidity, and solar radiation. Two predictive models were applied, and the first (Model 1) is to apply the predictive techniques to predict the urban microclimate in the real-time sequence, and then extract the characteristics of urban microclimate, while the second (Model 2) is to directly extract the characteristics of the microclimate, and then predict the characteristics of the microclimate. Backpropagation artificial neural network (BP-ANN) and long-short term memory (LSTM) techniques were applied in both models. The results show Model 1 with as the time-series prediction can reach the best (99.92%) of correlation coefficient and 98% of the mean average percentage error (MAPE), for temperature, while 99.66% and 98.18% for relative humidity, respectively, while accuracies in Model 2 decreased to 79% and 88.6% of MAPE for temperature and relative humidity, respectively. The prediction of solar radiation using ANN and LSTM are 51.1% and 57.8% of the correlation coefficient, respectively.