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The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations..

The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time.

The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates.

The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock.

To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

The purpose of this study was to explore and critique the benefits and disbenefits that transport orientated development (TOD) brings to neighbourhoods in proximity to public transport hubs.

This is an exploratory study that may also be described as a rapid review that aims to provide coverage of the available literature in a systematic process that is simplified to produce information in a timely manner. Due to the relatively small number of available studies from peer-reviewed sources, the variety of methods and data used and the constrained time available for this study, the study did not immediately lend itself to a more thorough systematic literature review.

The literature shows the discourse on TOD upholds its promise to create a high-density mixed-use walkable neighbourhood supported by transport infrastructure, increasing accessibility, minimising vehicle dependency, reducing traffic congestion, moderating urban sprawl and reducing pollution. There are few articles on the negative aspects of TOD, particularly concerning social exclusion, crime, sustainability and concerns about gentrification of neighbourhoods.

The study did not immediately lend itself to a more thorough systematic literature review due to the relatively small number of available studies, the variety of methods and data used and the constrained time available for this study.

This study allows social investigators, policymakers and developers understand the benefits and disbenefits of TOD including policy implications regarding potential criminogenic factors.

Food delivery applications (FDAs) are becoming more and more well-liked across Generations X, Y and Z, with Asia experiencing the biggest growth. These three generations of consumers have distinct views toward using FDAs because they were influenced by various social environments, cultures and experiences. Therefore, marketers ought to present customers with various values. The purpose of this study is to investigate how the theory of consumption values (TCV) affects the intention of various generational cohorts to continued usage intentions.

Online surveys were distributed to 745 Thai customers who had previously ordered meals from FDAs to collect information. The data were analyzed using partial least squares structural equation modeling and multigroup analysis.

Results reveal that there are significant differences among Generations X, Y and Z, while emotional value is insignificant. Generation X is concerned about functional and conditional value, whereas Generation Y expresses the highest concern on epistemic and social value. Meanwhile, Generation Z pays attention to epistemic, functional and social value.

The novelty of this study contributes to TCV affecting the different FDAs consumption value among Generations X, Y and Z, while the previous research only focused on gender and age difference, and this study firstly combines FDAs with TCV to predict the consumers’ intention to continuously use FDAs in the post-COVID-19 outbreak.

The purpose of this paper is to investigate user satisfaction with digital health solutions by identifying and prioritizing different service attributes on the basis of their impact on improving user satisfaction.

Through a literature review and interviews with health professionals and patients, 20 attributes of digital health services provided in Italy have been identified. User satisfaction with these attributes has been evaluated by adopting the Kano model’s continuous and discrete analyses.

The findings reveal the essential attributes of digital health services that meet users' expectations, identify the attributes that users appreciate or dislike having and highlight unexpected attributes that lead to a significant boost in satisfaction when provided.

This study demonstrates the efficacy of the Kano model in assessing the nonlinear correlation between user satisfaction and the quality of digital health services, thus contributing to fill a gap in the literature in this area. The main limitation of this work is the use of a non-probabilistic sampling method.

This research suggests healthcare institutions and organizations consider user preferences when designing digital health solutions to increase their satisfaction. The results indicate different effects on user satisfaction and dissatisfaction for different categories of attributes in the Italian context.

Previous works studied customer satisfaction with digital health, assuming a linear relationship with service quality, or investigated consumer adoption intentions focusing on the technological factors. This work advances available knowledge by analyzing the nonlinear relationship between digital health attributes and users’ satisfaction and dissatisfaction.

This study explores the relationships among sustainability implementation barriers (resource, managerial and regulatory barriers), sustainability practices (sustainable construction materials, sustainable construction design, modern construction methods and environmental provisions and reporting) and sustainability performance (environmental, economic and social) in hill road construction (HRC).

Primary data were collected from the 313 HRC practitioners with the help of a questionnaire, and research hypotheses were tested employing structural equation modeling.

The findings reveal a mixed effect of sustainability implementation barriers. Resource (managerial) barriers are negatively related to all practices except environmental provisions and reporting (sustainable construction materials), while regulatory barriers only negatively impact modern construction methods. On the other hand, all sustainability practices positively impact environmental performance, whereas economic (social) performance is positively influenced by all practices, except environmental provisions and reporting (modern construction methods), and positively affects economic performance.

In order to transform HRC toward sustainability, the barriers to sustainability implementation, sustainability practices and performance need to be understood by practitioners; however, the relationships have not previously been empirically assessed in extant literature. Besides, past research appears to be predominantly focused on the environmental aspect, thereby neglecting economic and social aspects. This study is a modest attempt to bridge these research gaps.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

The COVID-19 pandemic has dramatically transformed higher education policy and practices across the globe, including Bangladesh. Higher education institutions (HEIs) were forced to deliver teaching and learning online. This chapter discusses the impact of the COVID-19 pandemic on teaching and learning practice in higher education in Bangladesh and highlights the cultural transformation of policy and practice. In addition, it proposes future directions on how to be prepared and addresses the challenges of emergencies and draws implications of the findings beyond the national context. In the beginning of the pandemic, most universities in Bangladesh had to initially shut down their operations without offering any alternatives. However, a number of universities gradually rolled out some online teaching and learning activities as the lockdowns kept extending. A large portion of the HEIs struggled to continue their online teaching and learning due to the lack of resources, i.e., devices, technological skills and training, lack of policy, negative mindset, poor network infrastructure, and high cost of internet. This unprecedented situation ushered in by the pandemic showed the lack of preparedness and below-par capacity to respond to emergencies for the continuation of higher education in Bangladesh. Furthermore, it highlighted that improving the higher education sector requires tremendous effort from the government, researchers, policymakers, practitioners, the universities, and other industries directly or indirectly related to the sector.

The smart destination can be defined as a destination that deploys information communications technologies (ICTs) and other technological tools for interactive/participative engagement with prospective visitors. The rationale is to enhance the residents' quality of life as well as the tourists' experiences of the destinations they visit. This chapter discusses about digitization strategies and on marketing superstructures affecting destinations. It advances a conceptual framework through the development of an Attracting, Stay, and Return (ASR) Model that is suitable for smart destinations. It relies on descriptive case studies to conceptualize smart tourism destinations. This contribution reiterates the importance of having a well-designed website that presents appropriate content to entice the prospective travelers' curiosity about destinations. It offers valuable insights and advances new knowledge on smart marketing approaches that are intended to increase the destination management organizations' outreach with tourists at each stage of the ASR Model.

Nowadays, transportation authorities in various countries are in tension as to whether to invest in railroads or roads. There are arguments for each side, and in the end, each transportation authority reaches a kind of balance between the investments. This study aims to anticipate how autonomous vehicles will influence this decision.

The roads' capacity in the era of autonomous vehicles is assessed and research has concluded that the anticipated increase in road capacity will encourage transportation authorities to invest much more in roads than in railroads.

The appearance of the autonomous vehicles will significantly change the balance in favor of the roads, because the roads' capacity will be increased substantially so the roads will be able to accommodate many more vehicles.

Currently, autonomous vehicles are still very rare.

The impact of autonomous vehicles on the decision whether to build more roads is explained.

The study explained why the transportation authorities in the various countries will be more inclined to switch to road construction and why the transition to more roads and fewer railroads will likely be done gradually as more autonomous vehicles enter service.

This study aims to model tourist activities in a network and explore the properties of the network. Such network enables the author to explain and quantify how tourist activities are connected in determining tourist consumption as well as the organization of destination supply.

The author developed a network formation mechanism to create edges between nodes based on the joint probability of a pair of activities undertaken by tourists at a destination. By adjusting network sparsity, the author created an ensemble of four topologically similar networks for empirical testing. The author used tourist activity data of Hong Kong inbound tourists to test the network model.

The author found a robust hub–periphery topological structure of the tourist activity network. In addition, the network is featured by high clustering, short diameter and positive correlations between four node centralities, namely, degree, closeness, betweenness and eigenvector centralities. The author also generated the k-cores of the networks to further unravel the structure of hub nodes. The author found that the k-cores are dominated by tourist activities related to shopping or sightseeing, suggesting the high complementarity of these activities.

This study provides a different lens through which tourist consumption can be understood from a macroscopic angle by examining network topology and from a microscopic angle by examining node centralities.

To the best of the author’s knowledge, this is the first study attempting to model tourist activity and consumption in a network and explore the properties of the network. Not only has this study provided a new real-world network for network research, but it has also suggested an innovative modeling approach for tourist behavior research.