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The purpose of this study is to investigate how proximity to metropolitan areas and local creative talent impact a company’s access to venture capital (VC). We analyze the interplay between these factors and test our hypotheses using USA county data.

This empirical study uses multivariate regression analyses to analyze VC investment distribution across the USA at the county level between the years 1990–2011.

Our findings suggest that an increase in the local creative workforce correlates with higher levels of VC funding, regardless of metro location, but has a more significant impact in metro areas, indicating the complementary nature of these factors. Furthermore, the tech industry benefits more from the local creative workforce and is less sensitive to geographic location. Our results suggest that non-metro locations with a rich local creative culture can be as effective in attracting VC as metro locations with a mediocre local creative culture. This study contributes to our understanding of the optimal geographic location for companies seeking VC.

One of the limitations of our research is the research timeline. Since “creative class” was not measured by the U.S. Department of Agriculture (USDA) after 2011, we cannot analyze the recent effects of creative class on VC. However, given the fact that technology-related industries increasingly dominated the VC industry in recent years, our results on tech-related industries can shed light on the future expectations of the creative class in the VC industry moving forward.

Some companies might find it advantageous to locate outside metro areas where the creative workforce is more abundant and accessible. Our results support this trend by demonstrating that companies must consider the tradeoff between these two factors and recognize that locating in metro areas may not always be the optimal choice for every company. A tradeoff may exist between location and the cost of accessing creative talent.

Our results suggest that non-metro locations with a rich local creative culture can be as effective in attracting VC as metro locations with a mediocre local creative culture.

The existing literature emphasizes the importance of studying various factors that can help distribute VC and entrepreneurial activities across the country instead of just being concentrated in specific areas like metro regions. Although previous studies have examined broader institutional and country-level factors, local creative culture has not been considered in the context of its impact on the geographical distribution of VC. Our research highlights creative culture as a new local factor that affects VC distribution among USA counties.

This paper aims to trial a novel method of improving the performance of rail systems. Accordingly, an evaluation of rail system dynamics (SD) using discrete event simulation (DES) will be undertaken. Globally, cities and their transportation systems face ongoing challenges with many of these resulting from complicated rail SD. To evaluate these challenges, this study utilized DES as the basis of the analysis of Melbourne Metro Rail's SD. The transportation SD processes including efficiency and reliability were also developed.

Using DES, this research examines and determines the Melbourne Metro Rail's SD. Although the Melbourne Metro Rail is still in progress, the DES developed in this research examined the system requirements of functionality, performance and integration. As the basis of this examination, the Melbourne Metro Rail's optimization was simulated using the developed DES. As the basis of the experiment, a total of 50 trials were simulated. This included 25 samples for each of efficiency and reliability. The simulation not only scrutinized the SD but also underlined some of its shortfalls.

This study found that information and communication technology (ICT) was the pinnacle of system application. The DES development highlighted that both efficiency and reliability rates are the essential SD and thus fundamental for Melbourne Metro Rail system functionality. Specifically, the three elements of SD, capacity, continuity and integration are considered critical in improving the system functionality of Melbourne Metro Rail.

This particular mega rail infrastructure system was carefully analyzed, and subsequently, the DES was developed. However, since the DES is at its inception, the results are relatively limited without inclusive system calibration or validation process. Nonetheless, with some modifications, such as using different KPIs to evaluate additional systems variables and setting appropriate parameters to test the system reliability measures at different intensities, the developed DES can be modified to examine and evaluate other rail systems. However, if a broader system analysis is required, the DES model subsequently needs to be modified to specific system parameters.

Through evaluation of Melbourne's Metro Rail in the manner described above, this research has shown the developed DES is a useful platform to understand and evaluate system efficiency and reliability. Such an evaluation is considered important when implementing new transport systems, particularly when they are being integrated into existing networks.

Efficient rail networks are critical for modern cities and such systems, while inherently complex, aid local economies and societal cohesion through predictable and reliable movement of people. Through improved system functionality and greater efficiencies, plus improved passenger safety, security and comfort, the traveling public will benefit from the enhanced reliability of the transportation network that results from research as that provided in this paper.

This research paper is the first of its kind specifically focusing on the application of DES on the Melbourne Metro Rail System. The developed model aligns with the efficiency optimization framework, which is central to rail systems. The model shows the relationship between increased efficiency and optimizing system reliability. In comparison with more advanced mathematical modeling, the DES presented in this research provides robust, but yet rapid and uncomplicated system enhancements. These findings can better prepare rail professionals to adequately plan and devise appropriate system measures.

The study aims to critically evaluate the Kochi Water Metro as a model for sustainable urban transportation and extract valuable lessons for policymakers and cities seeking similar initiatives. The study employs a qualitative data-driven approach, utilising a longitudinal case study design, open-ended interview questions, direct observations and qualitative content analysis. The Kochi Water Metro has emerged as a transformative urban mobility initiative, enhancing connectivity while simultaneously reducing congestion on roads. The project's sustainable practices have minimised its carbon footprint, contributing to environmental sustainability. Economic benefits have accrued through increased tourism, employment opportunities and improved local livelihoods. The project's adaptability to local conditions and changing needs further underscores its sustainability. Limited data from the project's early years necessitate further longitudinal analysis. The focus on benefits may overlook potential challenges, warranting broader investigation. Reliance on qualitative data may restrict generalisability. The Kochi Water Metro serves as a beacon for sustainable and equitable transportation solutions. It serves as a blueprint for policymakers, providing practical lessons in project implementation, especially its integration with existing modes enhances overall urban mobility. Policymakers should consider replicating elements such as electric boats and integrated networks, while emphasising environmental sustainability, accessibility and community engagement. The comprehensive evaluation provides valuable insights into the project's tangible impacts and replicable elements. The innovative approach blends sustainable practices, operational resilience and community engagement, serving as a model for cities seeking sustainable, equitable and efficient transportation solutions.

The purpose of this paper is to identify and analyze the interdependence of project complexity factors (PCFs) in metro rail projects using the Decision-Making Trial and Evaluation Laboratory (DEMATEL). The study provides qualitative and quantitative analysis of project complexities factors and their relationships. The results of the study facilitate effective project planning, proactive risk management and informed decision-making by stakeholders.

This study employs a case-based method for identifying PCFs and a DEMATEL method for analyzing the interdependence of complexity factors in metro rail projects. Initially, PCFs were identified through an extensive literature review. To validate and refine these factors, semi-structured interviews were conducted with thirty experienced professionals, each having 5–20 years of experience in roles such as project management, engineering, and planning. Further, elevated and underground metro rail projects were purposefully selected as cases, for identifying the similarities and differences in PCFs. A questionnaire survey was conducted with various technical experts in metro rail projects. These experts rated the impact of PCFs on a five-point Likert scale, for the evaluation of the interdependence of PCFs. The DEMATEL technique was used to analyze the interdependencies of the PCFs.

Metro rail projects are influenced by project complexity, which significantly impacts their performance. The analysis reveals that “design problems with existing structures,” “change in design or construction” and “land acquisition” are the key factors contributing to project complexity.

The study of project complexity in metro rail projects is limited because most of the studies have studies on examining complexity in mega projects. The existing literature lacks adequate attention in identifying project complexity and its effects on metro rail project performance. This research aims to bridge this gap by examining project complexity and interdependencies in metro rail projects.

The purpose of this research is to develop a project complexity index (PCI) model using the best and worst method (BWM) to quantitatively analyze the impact of project complexities on the performance of metro rail projects.

This study employed a two-phase research methodology. The first phase identifies complexities through a literature review and expert discussions and categorizes different types of complexities in metro rail projects. In the second phase, BWM, a robust multi-criteria decision-making (MCDM) technique, was used to prioritize key complexities, and a PCI model was developed. Further, the developed PCI was validated through case studies, and sensitivity analysis was performed to check the accuracy and applicability of the developed PCI model.

The analysis revealed that location complexity exerted the most substantial influence on project performance, followed by environmental, organizational, technological and contractual complexities. Sensitivity analysis revealed the varying impacts of complexity indices on the overall project complexity.

The study's findings offer a novel approach for measuring project complexity's impact on metro rail projects. This allows stakeholders to make informed decisions, allocate resources efficiently and plan strategically.

The existing studies on project complexity identification and quantification were limited to megaprojects other than metro rail projects. Efforts to quantitatively study and analyze the impact of project complexity on metro rail projects are left unattended. The developed PCI model and its validation contribute to the field by providing a definite method to measure and manage complexity in metro rail projects.

Metro stations have become a crucial aspect of urban rail transportation, integrating facilities, equipment and pedestrians. Impractical physical layout designs and pedestrian psychology impact the effectiveness of an evacuation during a metro fire. Prior research on emergency evacuation has overlooked the complexity of metro stations and failed to adequately consider the physical heterogeneity of stations and pedestrian psychology. Therefore, this study aims to develop a comprehensive evacuation optimization strategy for metro stations by applying the concept of design for safety (DFS) to an emergency evacuation. This approach offers novel insights into the management of complex systems in metro stations during emergencies.

Physical and social factors affecting evacuations are identified. Moreover, the social force model (SFM) is modified by combining the fire dynamics model (FDM) and considering pedestrians' impatience and panic psychology. Based on the Nanjing South Metro Station, a multiagent-based simulation (MABS) model is developed. Finally, based on DFS, optimization strategies for metro stations are suggested.

The most effective evacuation occurs when the width of the stairs is 3 meters and the transfer corridor is 14 meters. Additionally, a luggage disposal area should be set up. The exit strategy of the fewest evacuees is better than the nearest-exit strategy, and the staff in the metro station should guide pedestrians correctly.

Previous studies rarely consider metro stations as sociotechnical systems or apply DFS to proactively reduce evacuation risks. This study provides a new perspective on the evacuation framework of metro stations, which can guide the designers and managers of metro stations.

This paper aims to understand the spatiotemporal influence of metro rail connectivity on housing prices in surrounding areas. The study assesses the average annual price shift for apartments around metro stations in Delhi during the previous decade, specifically from 2010 to 2019. The authors examine the spatiotemporal extents to which housing prices are determined by the prominence of metro stations and spatial development around metro stations.

The authors perform the cross-tabulation analysis to calculate chi-square values to test the hypotheses concerning the responsiveness of the housing market in Delhi to the number of locational variables in the areas connected with the mass public transportation system.

The empirical findings verify the existence of a housing market overvaluation in Delhi around metro stations until 2013, which was eventually re-adjusted after 2014. The key findings of the study suggest the role of location variables concerning metro rails in the shooting up of the housing prices in the city. In addition, the research establishes the association of annual housing price shifts to the metro rails in the short-term, mid-term and long-term in conjunction with the distance from the metro station.

In the market, the prices are often overvalued by real estate agents due to better connectivity to the metro stations. The overvaluation eventually causes massive downfalls in housing markets and rollouts as a risk for the investors. However, the effect of mass transportation on housing prices is mixed in nature, limited to a certain extent only and not as influential as frequently portrayed by the market forces. This effect loses colour with time.

The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.

To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).

The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.

In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.

This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.

This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

The purpose of this paper is to explore the degree of inflation persistence across all US metro areas over the post-pandemic period.

Both the Multivariate Core Trend (MCT) model and a fractional integration model, that is the Multivariate Unobserved-Components Stochastic Volatility Outlier-adjusted (MUCSVO) model are estimated.

The findings provide clear evidence of a significant inflation persistence in ten metro areas and the absence of persistence in the remaining areas, implying that in the former areas, inflation clearly indicates a strong persistent pattern. In other words, in these ten areas, the persistent component dominates the evolution of the trend and stands as a significant driver of inflation.

The findings have important implications for US policymakers to consider implementing more targeted policies to address inflation in specific metro areas to reduce the overall inflation rate, or they may need to consider tailoring fiscal policies to address inflationary pressures in specific metro areas. The findings illustrate the need for targeted policy interventions to address inflationary pressures in specific areas, as well as the importance of understanding the drivers of inflation persistence to develop effective policy responses. The findings also provide insights to firms on how to mitigate the risks of inflation. They may need to diversify their products or supplier base so that they do not rely on areas experiencing persistent inflation.

This paper contributes to the literature by extending the discussion of the impact of the recent pandemic crisis on US regional inflation. The findings have important implications for US policymakers to consider implementing more targeted policies to address inflation in specific metro areas to reduce the overall inflation rate, or they may need to consider tailoring fiscal policies to address inflationary pressures in specific metro areas. The findings illustrate the need for targeted policy interventions to address inflationary pressures in specific areas, as well as the importance of understanding the drivers of inflation persistence to develop effective policy responses. The findings also provide insights to firms on how to mitigate the risks of inflation. They may need to diversify their products or supplier base so that they do not rely on areas experiencing persistent inflation.

Through the significance matrix, this paper aims to investigate and explore the main sustainability factors of mega transportation infrastructure projects. Sydney’s Metro mega transportation infrastructure is used as a case study. Sydney’s Metro was selected because of its sustainability challenges faced because of the areas’ diverse ecological zones. Sydney’s Metro is thus examined as the basis of best practice for the determination of the sustainability factors of transportation infrastructures.

Using the significance matrix as a methodology, this research evaluates the environmental impact assessment and environmental assessment processes, to alleviate the problems of the mega transportation infrastructure.

This research found that a more comprehensive determination is needed to further analyse the sustainability factors of mega transportation infrastructures, use of a significance matrix would further assess the environmental complexities of mega transportation infrastructures and the sustainability factors of mega transportation infrastructures should include a nonlinear and asymmetrical scheme highlighting its components and carefully outlining its integration and consolidation.

Although there is concurrent research into sustainability factors of mega transportation, this paper undertakes a new methodology for such infrastructure. While the significance matrix is not a new concept, it has never been used specifically for mega transportation infrastructure. Subsequently, using the significance matrix as a methodology, this research undertakes such environmental analysis and assessment and thus produces a qualitative risk analysis matrix. The findings from this research will ultimately assist the key stakeholders of mega transportation infrastructures to better plan, monitor and support similar projects.