Political representation is responsible for choices regarding the supply and the management of transport infrastructure, but its decisions are sometimes in conflict with the will and the general interest expressed by citizens. This situation has progressively prompted the use of specific corrective measures in order to obtain socially sustainable decisions, such as the deliberative procedures for the appraisal of public goods. The standard Stated Choice Modelling Technique (SCMT) can be used to estimate the community appreciation for public goods such as transport infrastructure; but the application of the SCMT in its standard form would be inadequate to provide an estimation that expresses the general interest of the affected community. Hence the need to adapt the standard SCMT on the basis of the operational conditions imposed by deliberative appraisal procedures. Therefore, the general aim of the paper is to outline the basic conditions on which a modified SCMT with deliberative procedure can be set up. Firstly, the elements of the standard SCMT on which to make the necessary adjustments are identified; subsequently, modifications and additions to make to the standard technique are indicated; finally, the contents of an extensive program of experimentation are outlined.

While the notion of the smart city has grown in popularity, the backlash against smart urban infrastructure in the context of changing state-public relations has seldom been examined. This article draws on the case of Hong Kong’s smart lampposts to analyse the emergence of networked dissent against smart urban infrastructure during a period of unrest. Deriving insights from critical data studies, dissentworks theory, and relevant work on networked activism, the article illustrates how a smart urban infrastructure was turned into both a source and a target of popular dissent through digital mediation and politicisation. Drawing on an interpretive analysis of qualitative data collected from multiple digital platforms, the analysis explicates the citizen curation of socio-technic counter-imaginaries that constituted a consent of dissent in the digital realm, and the creation and diffusion of networked action repertoires in response to a changing political opportunity structure. In addition to explicating the words and deeds employed in this networked dissent, this article also discusses the technopolitical repercussions of this dissent for the city’s later attempts at data-based urban governance, which have unfolded at the intersections of urban techno-politics and local contentious politics. Moving beyond the common focus on neoliberal governmentality and its limits, this article reveals the underexplored pitfalls of smart urban infrastructure vis-à-vis the shifting socio-political landscape of Hong Kong, particularly in the digital age.

The article is dedicated to analyzing trends in the development of startup infrastructure in Ukraine, Latvia and Georgia. The article is based on concrete data, a comprehensive analysis of statistical and qualitative data on the development of startups in Ukraine, Latvia and Georgia. This provides a reliable basis for the arguments and conclusions. General patterns of startup infrastructure development in the three countries were identified. A PEST analysis of startup infrastructure development in Ukraine, Latvia and Georgia was conducted. Thus, the authors conduct a multidisciplinary analysis that includes not only economic, but also social and technological aspects of startup ecosystems and infrastructures. Suggestions for improving the startup infrastructure in these countries were developed.

In developing metropolitan cities, the expansion of urban areas due to the urbanization phenomenon has resulted in massive transport infrastructure development in suburban areas. This development has prompted many governments to begin introducing Transit-Oriented Development (TOD) to organize emerging transit hubs in suburban areas into their city plans. The approach adopted to introduce TOD may differ, depending on the existing context. Countries with similar socio-cultural background typically adopt a uniform approach, but not Jakarta and Kuala Lumpur as the most developing metropolitan cities in Southeast Asia with similar urbanization and socio-cultural Based on the situation, through the examining documents and spatial analysis, this study seeks to examine the impact of different policy approach between Jakarta and Kuala Lumpur on the progressions of transport infrastructure and TOD areas in suburban. The results showed that Kuala Lumpur had a more rapid progression in transport infrastructures development, accompanied by the establishment of several transit zones in urban and suburban areas. Meanwhile, Jakarta’s approach comprised the gradual development of infrastructures, initially focusing on TOD in central urban areas and only a limited number of suburban areas with significant commuter traffic. These results indicate that differences in policy approaches in the two regions with similar urbanization and socio-cultural contexts influence the evolution of transport infrastructure and TOD areas development. Several factors contribute to these discrepancies, including efficiency, synchrony, bias, clarity of organizational structure, and conceptual comprehension. At macro basis, policy makers must underline that the characteristics suitability between the approach and region critically determines the success of urban development.

This study explores the complex dynamics of handling augmented reality (AR) data in higher education in the United Arab Emirates (UAE). Although there is a growing interest in incorporating augmented reality (AR) to improve learning experiences, there are still issues in efficiently managing the data produced by these apps. This study attempts to understand the elements that affect AR data management by examining the relationship between the investigated variables: faculty readiness, technological limits, financial constraint, and student engagement on data management in higher education institutions in the UAE, building on earlier research that has identified these problems. The research analyzes financial constraints, technological infrastructure, and faculty preparation to understand their impact on AR data management. The study collected detailed empirical data on AR data management in UAE higher education environments using a quantitative research methods approach, surveys. The reasons for choosing this research method include cost-effectiveness, flexibility in questionnaire design, anonymity and confidentiality involved in the chosen methods. The results of this study are expected to enhance academic discourse by highlighting the obstacles and remedies to improving the efficiency of AR technology data management at higher education institutions. The findings are expected to enlighten decision-making in higher education institutions on maximizing AR technology’s benefits for improved learning outcomes.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.