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Due to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.

This study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.

This study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.

APMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

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 objectives of this study are to (a) identify spatial and temporal crime concentrations, (b) supplement the traditional place-based analysis that defines hot spots based on counted incidents with an analysis of crime severity and (c) add to the research of hot spots with an analysis of offender data.

This study explores crime concentration in mass transit settings, focusing on Edmonton’s Light Rail Transit (LRT) stations in 2017–2022. Pareto curves are used to observe the degree of concentration of crime in certain locations using multiple estimates; trajectory analysis is then used to observe crime patterns in the data on both places and offenders.

A total of 16.3% of stations accounted for 50% of recorded incidents. Train stations with high or low crime counts and severity remained as such consistently over time. Additionally, 3.6% of offenders accounted for 50% of incident count, while 5% accounted for 50% of harm. We did not observe differences in the patterns and distributions of crime concentrations when comparing crime counts and harm.

Hot spots and harm spots are synonymous in low-crime-harm environments: high-harm incidents are outliers, and their weight in the average crime severity score is limited. More sensitive severity measures are needed for high-frequenty, low-harm enviornments.

The findings underscore the benefits of integrating offender data in place-based applied research.

The findings provide additional evidence on the utility of place-based criminology and potentially cost-effective interventions.

The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times. These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals. The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.

To achieve this objective, the paper simulates actual train operations, incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station. The Monte Carlo simulation method is adopted to solve this problem. This approach transforms a nonlinear model, which includes constraints from probability distribution functions and is difficult to solve directly, into a linear programming model that is easier to handle. The method then linearly weights two objectives to optimize the solution.

Through the application of Monte Carlo simulation, the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model. By continuously adjusting the weighting coefficients of the linear objectives, the method is able to optimize the Pareto solution. Notably, this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.

The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times. The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement. Furthermore, the method’s ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.

A growing body of literature focuses on crime hotspots; however, less is known about the spatial distribution of crime at mass transit systems, and even less is known about trajectory patterns of hotspots in non-English-speaking countries.

The spatiotemporal behaviour of 1,494 crimes reported to the Hong Kong’s Railway Police District across a two-year period was examined in this study. Crime harm weights were then applied to offences to estimate the distribution of crime severity across the transit system. Descriptive statistics are used to understand the temporal and spatial trends, and k-means longitudinal clustering are used to examine the developmental trajectories of crime in train stations over time.

Analyses suggest that 15.2% and 8.8% of stations accounted for 50% of all counted crime and crime harm scores, respectively, indicating the predictability of crime and harm to occur at certain stations but not others. Offending persists consistently, with low, moderate and high counts and harm stations remaining the same over time.

These findings suggest that more localised crime control initiatives are required to target crime effectively.

This is one of the only studies focusing on hotspots and harmspots in the mass transit system.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

Railways are a well-known example of complex critical infrastructure, incorporating socio-technical systems with humans such as drivers, signallers, maintainers and passengers at the core. The technological evolution including interconnectedness and new ways of interaction lead to new security and safety risks that can be realised, both in terms of human error, and malicious and non-malicious behaviour. This study aims to identify the human factors (HF) and cyber-security risks relating to the role of signallers on the railways and explores strategies for the improvement of “Digital Resilience” – for the concept of a resilient railway.

Overall, 26 interviews were conducted with 21 participants from industry and academia.

The results showed that due to increased automation, both cyber-related threats and human error can impact signallers’ day-to-day operations – directly or indirectly (e.g. workload and safety-critical communications) – which could disrupt the railway services and potentially lead to safety-related catastrophic consequences. This study identifies cyber-related problems, including external threats; engineers not considering the human element in designs when specifying security controls; lack of security awareness among the rail industry; training gaps; organisational issues; and many unknown “unknowns”.

The authors discuss socio-technical principles through a hexagonal socio-technical framework and training needs analysis to mitigate against cyber-security issues and identify the predictive training needs of the signallers. This is supported by a systematic approach which considers both, safety and security factors, rather than waiting to learn from a cyber-attack retrospectively.

In the mid-2000s, the operator of New York City’s mass transit network committed more than a half-billion dollars to military contractor Lockheed Martin for a security technology capable, in part, of inferring threats based on analysis of data streams, of developing response strategies, and taking automated action toward alerts and calamities in light of evolving circumstances. The project was a failure. This chapter explores the conceptualization and development of this technology – rooted in cybernetics – and compares its conceptual underpinnings with some situated problems of awareness, communication, coordination, and action in emergencies as they unfold in one of the busiest transport systems in the world, the New York subway. The author shows how the technology, with all the theatrical trappings of a “legitimate” security solution, was apparently conceived without a grounded understanding of actual use-cases, and the degree to which the complex interactions which give rise to subway emergency can be anticipated in – and therefore managed through – a technological system. As a case-study, the chapter illustrates the pitfalls of deploying technology against problems which are not well-defined in the first place, to the neglect of investments against much more fundamental problems – such as inadequate communication systems, and unstable relationships with emergency response agencies – which might offer guaranteed benefits, and indeed lay a firm groundwork for future deployment of more ambitious technology.

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.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.