Research and Analysis on the Top Design of Smart Railway

Lin, Shaofu; Jia, Yafang; Xia, Sibin

doi:10.1088/1742-6596/1187/5/052053

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…In the realm of construction management, big data refers to the extensive volumes of data generated from various sources, characterized by their velocity, variety, and volume (Lin et al, 2019). Big data encompasses information from IoT sensors, Building Information Modeling (BIM), project management software, and other digital tools that provide real-time insights into project progress and performance metrics (Alam et al,…”

Section: Big Data In Construction Managementmentioning

confidence: 99%

Big Data-Driven Decision Making in Project Management: A Comparative Analysis

Rahaman,

Rozony,

Mazumder

et al. 2024

AJSTEME

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This study investigates the impact of big data-driven decision-making in construction project management through a qualitative comparative analysis. By conducting semi-structured interviews with project managers, data analysts, and construction workers across various types of construction projects, the research identifies key themes related to the benefits and challenges of integrating big data analytics. The findings highlight significant advantages such as enhanced operational efficiency, improved decision-making processes, cost reduction, budget management, timely project delivery, and quality control and assurance. However, challenges including data integration complexities, privacy concerns, the need for specialized skills, and organizational resistance to change are also revealed. The study underscores the importance of fostering a data-driven culture and strong leadership support to maximize the benefits of big data in construction project management, while also emphasizing the need for context-specific strategies tailored to different project types.

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Section: Big Data In Construction Managementmentioning

confidence: 99%

Big Data-Driven Decision Making in Project Management: A Comparative Analysis

Rahaman,

Rozony,

Mazumder

et al. 2024

AJSTEME

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“…Volume of information collected (𝑦 ), volume of information processing (𝑦 ), volume of information analysis (𝑦 ), programs of decision-making (𝑦 ), effectiveness of program implementation (𝑦 ) [29,54,55] Rate variables (10) Volume of information acquisition (𝑥 ), volume of information failure (𝑥 ), volume of information available (𝑥 ), volume of information loss (𝑥 ), volume of information after cleaning (𝑥 ), volume of information reference (𝑥 ), results of implementation analysis (𝑥 ), expert intelligence (𝑥 ), owner recognition (𝑥 ), volume of program implementation (𝑥 ) [29,56,57] Constants (17) Information on equipment monitoring (𝑐 ), information on inspection (𝑐 ), failure factor (𝑐 ), application rate of information management technology (𝑐 ), application rate of data cleaning methods (𝑐 ), loss factor (𝑐 ), project plan and strategy (𝑐 ), policies, regulations, and industry codes (𝑐 ), historical case information on this project (𝑐 ), information on similar projects (𝑐 ), information availability rate of similar projects (𝑐 ), decision constraint impact rate (𝑐 ), application rate of information analysis methods (𝑐 ), expert knowledge base (𝑐 ), participation rate of experts in decision-making (𝑐 ), application rate of decision modeling methods (𝑐 ), implementation rate of program (𝑐 ) [11,20,57] Establishing system variable equations and setting parameters are digital representations oriented to the interaction and the way they act among the elements of the system According to Figure 6, the system is mainly composed of 32 variables, including 5 level variables, 10 rate variables, and 17 constants, as shown in Table 3.…”

Section: Variable Type Name Of Variables Referencementioning

confidence: 99%

Research on Data-Driven Dynamic Decision-Making Mechanism of Mega Infrastructure Project Construction

Liu

et al. 2023

Sustainability

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The construction of mega infrastructure projects has the characteristics of repeatability, long duration, and high complexity. Therefore, it is particularly important to implement dynamic decision-making in projects. This study takes data-driven decision-making mechanisms as the entry point and constructs a dynamic decision-making system for mega infrastructure projects consisting of an information collection subsystem, an information processing and transformation subsystem, a human–computer collaborative decision-making subsystem and an evaluation and feedback subsystem. On this basis, we established a system dynamics model of dynamic decision-making for mega infrastructure projects. Vensim PLE 9.3.5 software was used to simulate and analyze the operation law of dynamic decision-making for mega infrastructure projects from a data-driven perspective, and the sensitivity of the application rate of information management technology, the application rate of data analysis methods, the participation rate of experts in decision-making, the historical case information on this project, and the information on similar projects on the effectiveness of program implementation were simulated and analyzed. The results of the study showed that all five key influencing factors have a positive impact on the effectiveness of program implementation. In addition, the application rate of information management technology and the application rate of information analysis methods have a higher sensitivity to the effectiveness of program implementation, the participation rate of experts in decision-making and historical case information on this project have average sensitivity to the effectiveness of program implementation, and information on similar projects has lower sensitivity to the effectiveness of program implementation. This study provides some ideas and suggestions to promote the effective use of information technology and digital technology by each participant in the construction of mega infrastructure projects while improving their dynamic decision-making efficiency, scientificity, and accuracy.

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“…The system known as smart management is one of the important pillars of smart station. Smart management is centralized on data utilization to obtain information regarding various supporting systems to predict capacity demands of people and goods, analyze transportation planning based on their needs, infrastructure resources, transportation resources operational efficiency, business planning, time estimation punctuation line plan optimization, and resource allocation adjustment [7].…”

Section: Advances In Social Science Education and Humanities Research...mentioning

confidence: 99%

Development of Smart Management Principles in Railway Station Facilities

Aquarista¹,

Hartanti²

2022

Advances in Social Science, Education and Humanities Research

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The increasing use of rail transportation in Indonesia encourages an increase in the services that must be provided by stations, including Pasar Senen Station in Jakarta. Based on data from research that has been found, there are several important points of public service at Pasar Senen Station that have not been fully fulfilled, so the quality needs to be improved. This increase will affect the utility value of the facilities at the station, which in turn can be an opportunity to add to the visitor experience and provide solutions to existing problems. The research was conducted online which started by obtaining secondary data regarding any problems that existed at Pasar Senen Station. These data are the results of published research found on the internet, then been analyzed using a qualitative descriptive approach. The results of existing research are analyzed to determine the type of problem that is in accordance with the type of technology that already exists as a solution. Utilization of technology is used to make it easier to find what visitors need at the train station and increase their visual experience to obtain information so that the station manager can answer passengers' expectations for the provision of facilities. In addition, the development of facilities with systems can make it easier for managers to control station operations. The application of technology that includes an intelligent management system as a management system can improve the quality of public services at Pasar Senen Station so that it becomes more efficient.

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Research and Analysis on the Top Design of Smart Railway

Cited by 10 publications

References 4 publications

Big Data-Driven Decision Making in Project Management: A Comparative Analysis

Big Data-Driven Decision Making in Project Management: A Comparative Analysis

Research on Data-Driven Dynamic Decision-Making Mechanism of Mega Infrastructure Project Construction

Development of Smart Management Principles in Railway Station Facilities

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