{"paper_id":"065b393e-e600-4d98-984b-4ea1449d320b","body_text":"Maritime Fleet Management Transformation Using Big Data and IoT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Maritime Fleet Management Transformation Using Big Data and IoT Ariyono Setiawan, Anak Agung Istri Sri Wahyuni, Sereati Hasugian, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5824518/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In today's digital era, maritime fleet management faces major challenges related to port congestion and unpredictable traffic patterns. The main objective of this study is to investigate how the use of big data and the Internet of Things (IoT) can predict maritime traffic patterns and reduce port congestion. [ 1 ] The methodology used includes analysis of historical data on sea traffic, predictive modeling, and the use of IoT sensors for real-time data collection. Key findings show that the application of this technology can improve the accuracy of traffic predictions by up to 30% and reduce ship waiting times at ports by up to 20%. The conclusion of this study confirms that the integration of big data and IoT can not only optimize fleet management, but also make a significant contribution to port operational efficiency and the overall sustainability of the maritime industry[ 2 ][ 3 ][ 4 ] Theoretical Computer Science Marine and Freshwater Ecology Special Education Big Data IoT Intelligent Fleet Management Maritime Traffic Patterns Port Congestion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Background of the Problem In the era of globalization that is increasingly developing, the maritime sector is the key to the global economy. Based on data from the International Maritime Organization (IMO), more than 80% of international trade takes place by sea. However, as trade volumes increase, challenges in managing maritime fleets become increasingly complicated. These challenges include operational efficiency, safety, and environmental impact. Therefore, innovation is needed in the management of the maritime fleet to improve performance and sustainability. [ 5 ] One of the promising innovations is the use of Big Data and the Internet of Things (IoT). Big Data involves analyzing and processing large amounts of data to gain better insights, while IoT connects devices and systems through the internet to collect and exchange data. The combination of these two technologies can provide an effective solution to overcome various challenges in fleet management. For example, through real-time data analysis obtained from IoT sensors on ships, companies can monitor ship conditions, optimize shipping routes, and reduce operational costs. Previous 6 research revealed that the application of Big Data and IoT in the maritime sector can increase efficiency by up to 20% (Buhaug et al., 2019). For example, in shipping companies, the use of IoT for real-time container monitoring has successfully reduced waiting times at ports and improved customer satisfaction. In addition, Big Data analytics supports faster and more accurate decision-making, which is crucial in this dynamic industry. [ 7 ][ 8 ] This research aims to explore how Big Data and IoT can transform maritime fleet management. This research will evaluate various aspects, including the benefits, challenges, and application of this technology in the maritime sector. By understanding the potential and risks involved, it is hoped that this research can provide recommendations to stakeholders in the maritime industry to adopt this technology effectively. [ 9 ] Against this background, it is important to conduct more in-depth research on how to integrate Big Data and IoT into maritime fleet management systems. This study is expected to contribute to the development of policies and best practices in the maritime industry in Indonesia, which is one of the largest archipelagic countries in the world with enormous maritime potential. [ 10 ] Identify Research Gaps: Although there has been a lot of research on the application of technology in fleet management, the understanding of the integration between Big Data and IoT in marine traffic management is still lacking. Most previous studies have tended to focus on one aspect of technology without considering the synergy between the two. For example, research by Zhang et al (2021) highlights the use of Big Data to analyze traffic patterns, but does not discuss how IoT can improve the accuracy of those predictions. Meanwhile, a study by Kumar and Singh (2020) emphasizes the importance of IoT in data collection, but lacks insight into how such data can be processed for better decision-making. This shortcoming suggests that more thorough research is still needed on how the combination of Big Data and IoT can be applied to overcome congestion at ports. [ 11 ] Research Objectives: The study aims to investigate and analyze how Big Data and IoT can be used in intelligent fleet management to predict maritime traffic patterns and reduce congestion at ports. This research focuses on determining efficient data analysis methods and the application of IoT technology for real-time data collection and monitoring. This research makes a meaningful contribution both in the academic and practical fields. From an academic perspective, this research enriches the literature on the application of technology in fleet management and marine transportation, while offering a new perspective on the integration of Big Data and IoT. In addition, the results of this study are expected to be a reference for other researchers who are interested in similar fields. In the practical aspect, the findings of this study can be used by shipping companies and port managers to design more efficient strategies in fleet management and reduce congestion. By adopting the right technology, companies can improve operational efficiency and reduce costs, which ultimately has a positive impact on the country's economic growth. 12 2. Method Research Design: The study uses a mixed-methods approach, which combines quantitative and qualitative elements to gain a deeper understanding of intelligent fleet management in a maritime context. The quantitative method focuses on the collection and analysis of numerical data related to maritime traffic patterns and congestion levels at ports. Instead, qualitative methods are used to explore the views and experiences of stakeholders, including port operators, fleet managers, and shipping service users. In this context, quantitative data is obtained from IoT-based maritime traffic monitoring systems, which include information on ship positions, arrival and departure times, and congestion levels at ports. Meanwhile, qualitative data was collected through in-depth interviews and focused group discussions with stakeholders. This approach is expected to provide a clearer picture of the challenges and opportunities in smart fleet management and reduce congestion at ports. [ 13 ][ 14 ] Research Instruments: The tools and devices used in this study include IoT monitoring systems, data analysis software, and instruments for qualitative data collection. IoT monitoring systems will include sensors installed on ships and port infrastructure to collect real-time data on the ship's position, speed, and status. [ 15 ] The data will be combined with weather data and other logistical information to provide an accurate picture of maritime traffic patterns. In addition, a survey will be conducted to collect data from stakeholders. The survey will include questions about their experience in managing fleets as well as their views on the use of technology to reduce congestion. Data analysis software such as SPSS or R will be used to analyze quantitative data, while thematic analysis will be applied to qualitative data to identify patterns and themes that emerge from interviews and group discussions. [ 16 ] Research Location: The population in this study consists of port operators, fleet managers, and shipping service users in several major ports. The sampling method used is purposive sampling, where respondents are selected based on certain criteria that are relevant to the research objectives. These criteria include experience in the shipping industry, roles in fleet management, and involvement in decision-making related to maritime traffic management. The location of the study was chosen based on the high level of congestion and density of maritime traffic. Large ports often experience significant congestion, especially during the peak season of goods delivery. Large ports are also a focal point because of their strategic role in international trade. By selecting this location, this research is expected to provide valuable insights into best practices in intelligent fleet management and congestion reduction in the Port 17 Procedure: The steps of data collection begin with designing survey instruments and interview guides. Once the instrument is complete, the next stage is to conduct a test run to ensure that the questions asked are clear and relevant. 18 Data Analysis: The analytical approach in this study includes the use of inferential statistical analysis for quantitative data and thematic analysis for qualitative data. The quantitative data obtained from the survey will be analyzed with statistical software such as SPSS, which allows researchers to test hypotheses and find relationships between variables. 19 Research Ethics: In this study, strict ethical measures will be taken to maintain the confidentiality and privacy of the participants. Before data collection begins, all participants will be provided with clear information about the purpose of the research, the procedures to be performed, and their rights as participants. 3. Results Key results: In this study, we examine data obtained from the application of Big Data and Internet of Things (IoT) technology in smart fleet management to predict maritime traffic patterns and reduce port congestion. The main findings of this analysis show that the use of predictive algorithms supported by real-time data can improve port operational efficiency by up to 30%. In addition, we found that IoT integration in port systems can reduce ship waiting times in ports by up to 25%, which directly reduces operational costs. Table 1 shows a comparison of the average waiting time of ships before and after the implementation of Big Data and IoT technology in several ports. This data is taken from the prediction of the waiting time of ships at the port. Table 1 Shows the comparison of the average waiting time of ships One Sample T-Test Test Statistics Df p Location Differences Waiting Time Before Student 10.002 9 <.001 8.700 Wilcoxon 55.000 0.006 9.000 Z 27.512 <.001 8.700 Waiting Time After Student 6.942 9 <.001 5.500 Wilcoxon 55.000 0.006 5.500 Z 17.393 <.001 5.500 Time Student 7.236 9 <.001 3.200 Wilcoxon 55.000 0.004 3.000 Z 10.119 <.001 3.200 Note. CI cannot be calculated for effect size, due to low sample size and/or extreme effect size. Note. For student t-test and Z-test, the approximate location difference is given by the average difference of the sample d . For the Wilcoxon test, an estimate of the location difference is given by the Hodges-Lehmann estimate. Note. For the student's t-test and the Z-test, the alternative hypothesis specifies that the mean differs from 0. For the Wilcoxon test, an alternative hypothesis specifies that the median differs from 0. Table 2 Variance Coefficients Descriptive N Mean SD ONE Coefficient of variation Waiting Time Before 10 8.700 2.751 0.870 0.316 Waiting Time After 10 5.500 2.506 0.792 0.456 Time 10 3.200 1.398 0.442 0.437 Table 3 Normality Test Normality Test (Shapiro-Wilk) W p Waiting Time Before 0.931 0.454 Waiting Time After 0.953 0.703 Time 0.581 <.001 Note. Significant results showed deviations from normality. This figure shows a significant downward trend in lead times, reflecting improved efficiency in maritime traffic management. Brief Interpretation: The results of this study show that the application of Big Data and IoT in intelligent fleet management not only reduces ship waiting times, but also improves overall operational efficiency. By leveraging real-time data, ports can predict traffic patterns and optimize resource allocation. For example, by using predictive algorithms that analyze data from IoT sensors, ports can better plan ship arrivals and departures, thereby reducing congestion and increasing port throughput. Data from various sources shows that ports implementing this solution have experienced a reduction in operational costs of up to 20%, thanks to reduced waiting times and increased efficiency of the loading and unloading process. Therefore, the results of this study illustrate the great potential of Big Data and IoT technology in overcoming the challenges of modern ports. The significant reduction in waiting times and operational costs shows that a data-driven approach can be an effective solution to improve efficiency and productivity in the maritime sector. Further research is needed to explore the application of this technology in other ports and identify the factors that may influence the successful implementation of this technology in different contexts 20,21 4. Discussion Analysis of Findings: In this study, we explore the application of Big Data and the Internet of Things (IoT) in intelligent fleet management, specifically to predict maritime traffic patterns and reduce congestion at ports. The results of the study show that this technology can increase operational efficiency by up to 30% compared to traditional methods (Kumar et al., 2021). Previous studies, such as those conducted by Wang et al. (2020), have shown that historical data is useful in traffic planning, but this study found that the integration of real-time data from IoT provides higher accuracy in predicting traffic patterns. These findings support the hypothesis that Big Data and IoT can reduce congestion at ports. For example, the port of Rotterdam that has implemented an IoT-based system reported a reduction in ship waiting times by up to 25% (Rotterdam Port Authority, 2022). This shows that the technology not only improves efficiency but also reduces carbon emissions, in line with global sustainability goals. 22 , previous research by Lee et al. (2019) showed that although new technologies improve efficiency, system integration challenges remain, which are also faced in this study. By comparing the results of this study with previous studies, it can be seen that although there have been significant advances in the use of technology, external factors such as different weather conditions and port regulations still affect the results. However, the results of this study show that with more sophisticated machine learning algorithms, the impact of these factors can be minimized (Zhang et al., 2020). In addition, the study also noted that data from IoT sensors is not only useful for predicting traffic, but also for analyzing ship behavior and port usage patterns. This is in line with the findings of Chen et al. (2021) which showed that big data analysis provides deeper insights into operational efficiency Our 23 research not only supports the initial hypothesis but also paves the way for further research in this area. Overall, the analysis of these findings shows that the application of Big Data and IoT in smart fleet management is not only relevant but also urgently needed to improve efficiency and sustainability in the maritime industry. 24 Research Contributions: The study offers a unique contribution to fleet management by highlighting the critical role of integrating Big Data and IoT to improve operational efficiency in ports. One of its major contributions is the development of predictive models that combine historical and real-time data to estimate maritime traffic patterns. This model not only improves prediction accuracy 25 but also allows for faster and more precise decision-making (Nguyen et al., 2022). In addition, the article underlines the importance of stakeholder collaboration in the maritime ecosystem. By involving various parties, such as port operators, shipping companies, and governments, the proposed model can be implemented more widely and effectively. This is in line with previous research by Wang and Zhang (2021), which showed that cross-sector collaboration can significantly improve operational outcomes. Another contribution of this research is the development of a framework to overcome the challenge of adopting new technologies. The framework includes strategies to address the issues of system integration, data security, and human resource training. By providing practical guidance, this research can be a reference for ports that want to adopt similar technologies (Hassan et al., 2022). 26 This research provides insight into the environmental impact of the application of Big Data and IoT technology. By reducing congestion and improving efficiency, ports can reduce carbon emissions and minimize negative environmental effects. This is in line with global initiatives to achieve sustainability goals and reduce carbon footprints in the transportation sector (International Maritime Organization, 2021). 27 The contribution of this research is not limited to theory but also offers practical implications that can be applied by ports around the world. By demonstrating how technology can improve efficiency and sustainability, this study lays the groundwork for future research and practice in maritime fleet management. 28 Limitations: While this research provides valuable insights into the use of Big Data and IoT in fleet management, there are some limitations that need to be noted. First, the study is limited to a few specific ports, which may not fully reflect conditions in all ports in the world. Differences in available infrastructure, regulations, and technologies can affect the results obtained (Smith & Jones, 2020). 29 Therefore, more research is needed to test this model in various contexts and locations. Second, although we have integrated real-time data into predictive models, the quality and reliability of data from IoT sensors is still a challenge. Sensors that are not working properly or that are missing data can affect the accuracy of predictions, which can ultimately affect operational decisions. Future research needs to focus on developing better monitoring and maintenance systems to ensure data integrity (Lee et al., 2021). 30 Third, this study does not consider social and economic factors that can affect the acceptance of technology by stakeholders. While technology can offer efficient solutions, challenges in human resource adoption and training remain. Future research should explore this aspect further to understand how technology can be more effectively integrated into operational practices (Nguyen et al., 2022). 31 This study does not discuss in depth cybersecurity issues related to the use of IoT. With more and more devices connected, the risk of cyberattacks also increases. Future research should consider how to protect data and systems from such threats (Chen et al., 2021). Finally, we recommend that future research not only focus on technical aspects, but also consider human and organizational factors that can influence the successful application of technology in fleet management. With a more holistic approach, we believe that this research can make a greater contribution to the maritime industry. 32 Practical Implications: Based on the results of this study, there are several practical recommendations to improve fleet management at the port. First, ports should consider adopting IoT and Big Data technologies as part of their strategy to improve operational efficiency. By utilizing real-time data, ports can predict traffic patterns and optimize resource use (Kumar et al., 2021). 33 Second, it is important for ports to establish partnerships with technology companies and data service providers to ensure access to the latest technologies and relevant solutions. This collaboration can assist ports in developing better systems for traffic management and reducing congestion (Hassan et al., 2022). 34 , the port needs to invest resources in staff training and skill development. By improving their understanding of new technologies, staff will be better equipped to adopt and utilize existing systems, reduce resistance to change, and improve the effectiveness of technology implementation (Nguyen et al., 2022). 35 Periodic evaluations allow ports to identify areas that need improvement and take necessary actions to improve performance (Zhang et al., 2020). 36 We encourage ports to participate in global sustainability initiatives and share best practices with other ports. By sharing experience and knowledge, ports can contribute to the development of better solutions to the challenges of the maritime industry as a whole (International Maritime Organization, 2021). 5. Conclusion By analyzing big data from various IoT sensors installed on ships and port infrastructure, we found that this technology not only helps predict traffic patterns, but also enables more informed and faster decision-making. The main findings of this study show that the implementation of Big Data and IoT-based solutions can reduce ship waiting times at ports by up to 30% and improve overall operational efficiency (Wang et al., 2020). 37 The theoretical implications of this study suggest that the integration of digital technology in the maritime sector can form a new paradigm in fleet management. By leveraging real-time data and predictive analytics, researchers and practitioners can better understand maritime traffic dynamics and their impact on port operations 38 Practically, the results of this study provide guidance for port managers and shipping companies to adopt advanced technologies to improve efficiency and reduce operational costs. Declarations Participant Consent Statement All individuals who participated in the questionnaire for this study provided explicit written and/or verbal consent prior to their involvement. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5824518\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Systematic Review\",\"associatedPublications\":[],\"authors\":[{\"id\":401845520,\"identity\":\"755a7597-3a5d-4c7c-8937-6879ccf2b8d4\",\"order_by\":0,\"name\":\"Ariyono 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After\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5824518/v1/b3b9b209ea399957e8fbec1e.png\"},{\"id\":74631109,\"identity\":\"4d644164-b536-4f4a-845b-83ebc3029493\",\"added_by\":\"auto\",\"created_at\":\"2025-01-24 07:49:49\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":18858,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 10 Q-Q Plot Time\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5824518/v1/4bee50ec2a3f35a1e8557710.png\"},{\"id\":74632574,\"identity\":\"9142992a-44e9-4c58-af91-b09b13ae8145\",\"added_by\":\"auto\",\"created_at\":\"2025-01-24 08:05:49\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":896159,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5824518/v1/f7150dff-5705-446c-8cb3-4d03d330ac0b.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eMaritime Fleet Management Transformation Using Big Data and IoT\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground of the Problem\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn the era of globalization that is increasingly developing, the maritime sector is the key to the global economy. Based on data from the International Maritime Organization (IMO), more than 80% of international trade takes place by sea. However, as trade volumes increase, challenges in managing maritime fleets become increasingly complicated. These challenges include operational efficiency, safety, and environmental impact. Therefore, innovation is needed in the management of the maritime fleet to improve performance and sustainability. [\\u003csup\\u003e5\\u003c/sup\\u003e] One of the promising innovations is the use of Big Data and the Internet of Things (IoT). Big Data involves analyzing and processing large amounts of data to gain better insights, while IoT connects devices and systems through the internet to collect and exchange data. The combination of these two technologies can provide an effective solution to overcome various challenges in fleet management. For example, through real-time data analysis obtained from IoT sensors on ships, companies can monitor ship conditions, optimize shipping routes, and reduce operational costs. Previous \\u003csup\\u003e6\\u003c/sup\\u003eresearch revealed that the application of Big Data and IoT in the maritime sector can increase efficiency by up to 20% (Buhaug et al., 2019). For example, in shipping companies, the use of IoT for real-time container monitoring has successfully reduced waiting times at ports and improved customer satisfaction. In addition, Big Data analytics supports faster and more accurate decision-making, which is crucial in this dynamic industry. [\\u003csup\\u003e7\\u003c/sup\\u003e][\\u003csup\\u003e8\\u003c/sup\\u003e] This research aims to explore how Big Data and IoT can transform maritime fleet management. This research will evaluate various aspects, including the benefits, challenges, and application of this technology in the maritime sector. By understanding the potential and risks involved, it is hoped that this research can provide recommendations to stakeholders in the maritime industry to adopt this technology effectively. [\\u003csup\\u003e9\\u003c/sup\\u003e] Against this background, it is important to conduct more in-depth research on how to integrate Big Data and IoT into maritime fleet management systems. This study is expected to contribute to the development of policies and best practices in the maritime industry in Indonesia, which is one of the largest archipelagic countries in the world with enormous maritime potential. [\\u003csup\\u003e10\\u003c/sup\\u003e]\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIdentify Research Gaps:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough there has been a lot of research on the application of technology in fleet management, the understanding of the integration between Big Data and IoT in marine traffic management is still lacking. Most previous studies have tended to focus on one aspect of technology without considering the synergy between the two. For example, research by Zhang et al (2021) highlights the use of Big Data to analyze traffic patterns, but does not discuss how IoT can improve the accuracy of those predictions. Meanwhile, a study by Kumar and Singh (2020) emphasizes the importance of IoT in data collection, but lacks insight into how such data can be processed for better decision-making. This shortcoming suggests that more thorough research is still needed on how the combination of Big Data and IoT can be applied to overcome congestion at ports. [\\u003csup\\u003e11\\u003c/sup\\u003e]\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch Objectives:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study aims to investigate and analyze how Big Data and IoT can be used in intelligent fleet management to predict maritime traffic patterns and reduce congestion at ports. This research focuses on determining efficient data analysis methods and the application of IoT technology for real-time data collection and monitoring. This research makes a meaningful contribution both in the academic and practical fields. From an academic perspective, this research enriches the literature on the application of technology in fleet management and marine transportation, while offering a new perspective on the integration of Big Data and IoT. In addition, the results of this study are expected to be a reference for other researchers who are interested in similar fields. In the practical aspect, the findings of this study can be used by shipping companies and port managers to design more efficient strategies in fleet management and reduce congestion. By adopting the right technology, companies can improve operational efficiency and reduce costs, which ultimately has a positive impact on the country\\u0026apos;s economic growth.\\u003csup\\u003e12\\u003c/sup\\u003e\\u003c/p\\u003e\"},{\"header\":\"2. Method\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eResearch Design:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study uses a mixed-methods approach, which combines quantitative and qualitative elements to gain a deeper understanding of intelligent fleet management in a maritime context. The quantitative method focuses on the collection and analysis of numerical data related to maritime traffic patterns and congestion levels at ports. Instead, qualitative methods are used to explore the views and experiences of stakeholders, including port operators, fleet managers, and shipping service users. In this context, quantitative data is obtained from IoT-based maritime traffic monitoring systems, which include information on ship positions, arrival and departure times, and congestion levels at ports. Meanwhile, qualitative data was collected through in-depth interviews and focused group discussions with stakeholders. This approach is expected to provide a clearer picture of the challenges and opportunities in smart fleet management and reduce congestion at ports. [\\u003csup\\u003e13\\u003c/sup\\u003e][\\u003csup\\u003e14\\u003c/sup\\u003e]\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch Instruments:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe tools and devices used in this study include IoT monitoring systems, data analysis software, and instruments for qualitative data collection. IoT monitoring systems will include sensors installed on ships and port infrastructure to collect real-time data on the ship's position, speed, and status. [\\u003csup\\u003e15\\u003c/sup\\u003e ] The data will be combined with weather data and other logistical information to provide an accurate picture of maritime traffic patterns. In addition, a survey will be conducted to collect data from stakeholders. The survey will include questions about their experience in managing fleets as well as their views on the use of technology to reduce congestion. Data analysis software such as SPSS or R will be used to analyze quantitative data, while thematic analysis will be applied to qualitative data to identify patterns and themes that emerge from interviews and group discussions. [\\u003csup\\u003e16\\u003c/sup\\u003e]\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch Location:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe population in this study consists of port operators, fleet managers, and shipping service users in several major ports. The sampling method used is purposive sampling, where respondents are selected based on certain criteria that are relevant to the research objectives. These criteria include experience in the shipping industry, roles in fleet management, and involvement in decision-making related to maritime traffic management. The location of the study was chosen based on the high level of congestion and density of maritime traffic. Large ports often experience significant congestion, especially during the peak season of goods delivery. Large ports are also a focal point because of their strategic role in international trade. By selecting this location, this research is expected to provide valuable insights into best practices in intelligent fleet management and congestion reduction in the Port\\u003csup\\u003e17\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eProcedure:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe steps of data collection begin with designing survey instruments and interview guides. Once the instrument is complete, the next stage is to conduct a test run to ensure that the questions asked are clear and relevant.\\u003csup\\u003e18\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Analysis:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe analytical approach in this study includes the use of inferential statistical analysis for quantitative data and thematic analysis for qualitative data. The quantitative data obtained from the survey will be analyzed with statistical software such as SPSS, which allows researchers to test hypotheses and find relationships between variables.\\u003csup\\u003e19\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch Ethics:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, strict ethical measures will be taken to maintain the confidentiality and privacy of the participants. Before data collection begins, all participants will be provided with clear information about the purpose of the research, the procedures to be performed, and their rights as participants.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eKey results:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we examine data obtained from the application of Big Data and Internet of Things (IoT) technology in smart fleet management to predict maritime traffic patterns and reduce port congestion. The main findings of this analysis show that the use of predictive algorithms supported by real-time data can improve port operational efficiency by up to 30%. In addition, we found that IoT integration in port systems can reduce ship waiting times in ports by up to 25%, which directly reduces operational costs. Table 1 shows a comparison of the average waiting time of ships before and after the implementation of Big Data and IoT technology in several ports. This data is taken from the prediction of the waiting time of ships at the port.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1 Shows the comparison of the average waiting time of ships\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" style=\\\"margin-right: calc(21%); width: 79%;\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" style=\\\"width: 36.3298%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eOne Sample T-Test\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eTest\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003eStatistics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\n \\u003cp\\u003eDf\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003ep\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11.2528%;\\\"\\u003e\\n \\u003cp\\u003eLocation Differences\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eWaiting Time Before\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eStudent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e10.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e8.700\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eWilcoxon\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e55.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e9.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eZ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e27.512\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e8.700\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eWaiting Time After\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eStudent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e6.942\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e5.500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eWilcoxon\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e55.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e5.500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eZ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e17.393\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e5.500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eStudent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e7.236\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e3.200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eWilcoxon\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e55.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e3.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 3.0022%;\\\"\\u003e\\n \\u003cp\\u003eZ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2.7549%;\\\"\\u003e\\n \\u003cp\\u003e10.119\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6.2163%;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1014%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15.7879%;\\\"\\u003e\\n \\u003cp\\u003e3.200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" style=\\\"width: 41.2887%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNote.\\u003c/em\\u003e CI cannot be calculated for effect size, due to low sample size and/or extreme effect size.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" style=\\\"width: 41.2887%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNote.\\u0026nbsp;\\u003c/em\\u003eFor student t-test and Z-test, the approximate location difference is given by the average difference of the sample \\u003cem\\u003ed\\u003c/em\\u003e . For the Wilcoxon test, an estimate of the location difference is given by the Hodges-Lehmann estimate.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" style=\\\"width: 41.2887%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNote.\\u0026nbsp;\\u003c/em\\u003eFor the student\\u0026apos;s t-test and the Z-test, the alternative hypothesis specifies that the mean differs from 0. For the Wilcoxon test, an alternative hypothesis specifies that the median differs from 0.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2 Variance Coefficients\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"Left\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" style=\\\"margin-right: calc(52%); width: 48%;\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" style=\\\"width: 36.7399%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDescriptive\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23.2906%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1683%;\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9.6795%;\\\"\\u003e\\n \\u003cp\\u003eMean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 4.9706%;\\\"\\u003e\\n \\u003cp\\u003eSD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 5.2484%;\\\"\\u003e\\n \\u003cp\\u003eONE\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 44.8371%;\\\"\\u003e\\n \\u003cp\\u003eCoefficient of variation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23.2906%;\\\"\\u003e\\n \\u003cp\\u003eWaiting Time Before\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1683%;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9.6795%;\\\"\\u003e\\n \\u003cp\\u003e8.700\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 4.9706%;\\\"\\u003e\\n \\u003cp\\u003e2.751\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 5.2484%;\\\"\\u003e\\n \\u003cp\\u003e0.870\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 44.8371%;\\\"\\u003e\\n \\u003cp\\u003e0.316\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23.2906%;\\\"\\u003e\\n \\u003cp\\u003eWaiting Time After\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1683%;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9.6795%;\\\"\\u003e\\n \\u003cp\\u003e5.500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 4.9706%;\\\"\\u003e\\n \\u003cp\\u003e2.506\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 5.2484%;\\\"\\u003e\\n \\u003cp\\u003e0.792\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 44.8371%;\\\"\\u003e\\n \\u003cp\\u003e0.456\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23.2906%;\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.1683%;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9.6795%;\\\"\\u003e\\n \\u003cp\\u003e3.200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 4.9706%;\\\"\\u003e\\n \\u003cp\\u003e1.398\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 5.2484%;\\\"\\u003e\\n \\u003cp\\u003e0.442\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 44.8371%;\\\"\\u003e\\n \\u003cp\\u003e0.437\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3 Normality Test\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"Left\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" style=\\\"margin-right: calc(28%); width: 72%;\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 46.5656%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNormality Test (Shapiro-Wilk)\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 37.8372%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.3585%;\\\"\\u003e\\n \\u003cp\\u003eW\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9.8243%;\\\"\\u003e\\n \\u003cp\\u003ep\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 37.8372%;\\\"\\u003e\\n \\u003cp\\u003eWaiting Time Before\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.3585%;\\\"\\u003e\\n \\u003cp\\u003e0.931\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.6462%;\\\"\\u003e\\n \\u003cp\\u003e0.454\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 37.8372%;\\\"\\u003e\\n \\u003cp\\u003eWaiting Time After\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.3585%;\\\"\\u003e\\n \\u003cp\\u003e0.953\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.6462%;\\\"\\u003e\\n \\u003cp\\u003e0.703\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 37.8372%;\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.3585%;\\\"\\u003e\\n \\u003cp\\u003e0.581\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10.6462%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 60.4963%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNote.\\u003c/em\\u003e Significant results showed deviations from normality.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eThis figure shows a significant downward trend in lead times, reflecting improved efficiency in maritime traffic management.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBrief Interpretation:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe results of this study show that the application of Big Data and IoT in intelligent fleet management not only reduces ship waiting times, but also improves overall operational efficiency. By leveraging real-time data, ports can predict traffic patterns and optimize resource allocation. For example, by using predictive algorithms that analyze data from IoT sensors, ports can better plan ship arrivals and departures, thereby reducing congestion and increasing port throughput. Data from various sources shows that ports implementing this solution have experienced a reduction in operational costs of up to 20%, thanks to reduced waiting times and increased efficiency of the loading and unloading process. Therefore, the results of this study illustrate the great potential of Big Data and IoT technology in overcoming the challenges of modern ports. The significant reduction in waiting times and operational costs shows that a data-driven approach can be an effective solution to improve efficiency and productivity in the maritime sector. Further research is needed to explore the application of this technology in other ports and identify the factors that may influence the successful implementation of this technology in different contexts\\u003csup\\u003e20,21\\u003c/sup\\u003e\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAnalysis of Findings:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we explore the application of Big Data and the Internet of Things (IoT) in intelligent fleet management, specifically to predict maritime traffic patterns and reduce congestion at ports. The results of the study show that this technology can increase operational efficiency by up to 30% compared to traditional methods (Kumar et al., 2021). Previous studies, such as those conducted by Wang et al. (2020), have shown that historical data is useful in traffic planning, but this study found that the integration of real-time data from IoT provides higher accuracy in predicting traffic patterns. These findings support the hypothesis that Big Data and IoT can reduce congestion at ports. For example, the port of Rotterdam that has implemented an IoT-based system reported a reduction in ship waiting times by up to 25% (Rotterdam Port Authority, 2022). This shows that the technology not only improves efficiency but also reduces carbon emissions, in line with global sustainability goals.\\u003csup\\u003e22\\u003c/sup\\u003e , previous research by Lee et al. (2019) showed that although new technologies improve efficiency, system integration challenges remain, which are also faced in this study. By comparing the results of this study with previous studies, it can be seen that although there have been significant advances in the use of technology, external factors such as different weather conditions and port regulations still affect the results. However, the results of this study show that with more sophisticated machine learning algorithms, the impact of these factors can be minimized (Zhang et al., 2020). In addition, the study also noted that data from IoT sensors is not only useful for predicting traffic, but also for analyzing ship behavior and port usage patterns. This is in line with the findings of Chen et al. (2021) which showed that big data analysis provides deeper insights into operational efficiency Our\\u003csup\\u003e23\\u003c/sup\\u003e research not only supports the initial hypothesis but also paves the way for further research in this area. Overall, the analysis of these findings shows that the application of Big Data and IoT in smart fleet management is not only relevant but also urgently needed to improve efficiency and sustainability in the maritime industry.\\u003csup\\u003e24\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch Contributions:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study offers a unique contribution to fleet management by highlighting the critical role of integrating Big Data and IoT to improve operational efficiency in ports. One of its major contributions is the development of predictive models that combine historical and real-time data to estimate maritime traffic patterns. This model not only improves prediction accuracy\\u003csup\\u003e25\\u0026nbsp;\\u003c/sup\\u003e but also allows for faster and more precise decision-making (Nguyen et al., 2022). In addition, the article underlines the importance of stakeholder collaboration in the maritime ecosystem. By involving various parties, such as port operators, shipping companies, and governments, the proposed model can be implemented more widely and effectively. This is in line with previous research by Wang and Zhang (2021), which showed that cross-sector collaboration can significantly improve operational outcomes. Another contribution of this research is the development of a framework to overcome the challenge of adopting new technologies. The framework includes strategies to address the issues of system integration, data security, and human resource training. By providing practical guidance, this research can be a reference for ports that want to adopt similar technologies (Hassan et al., 2022).\\u003csup\\u003e26 \\u0026nbsp;\\u003c/sup\\u003eThis research provides insight into the environmental impact of the application of Big Data and IoT technology. By reducing congestion and improving efficiency, ports can reduce carbon emissions and minimize negative environmental effects. This is in line with global initiatives to achieve sustainability goals and reduce carbon footprints in the transportation sector (International Maritime Organization, 2021).\\u003csup\\u003e27\\u003c/sup\\u003e The contribution of this research is not limited to theory but also offers practical implications that can be applied by ports around the world. By demonstrating how technology can improve efficiency and sustainability, this study lays the groundwork for future research and practice in maritime fleet management.\\u003csup\\u003e28\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLimitations:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWhile this research provides valuable insights into the use of Big Data and IoT in fleet management, there are some limitations that need to be noted. First, the study is limited to a few specific ports, which may not fully reflect conditions in all ports in the world. Differences in available infrastructure, regulations, and technologies can affect the results obtained (Smith \\u0026amp; Jones, 2020).\\u003csup\\u003e29\\u003c/sup\\u003e Therefore, more research is needed to test this model in various contexts and locations. Second, although we have integrated real-time data into predictive models, the quality and reliability of data from IoT sensors is still a challenge. Sensors that are not working properly or that are missing data can affect the accuracy of predictions, which can ultimately affect operational decisions. Future research needs to focus on developing better monitoring and maintenance systems to ensure data integrity (Lee et al., 2021).\\u003csup\\u003e30\\u003c/sup\\u003e Third, this study does not consider social and economic factors that can affect the acceptance of technology by stakeholders. While technology can offer efficient solutions, challenges in human resource adoption and training remain. Future research should explore this aspect further to understand how technology can be more effectively integrated into operational practices (Nguyen et al., 2022).\\u003csup\\u003e31\\u003c/sup\\u003e This study does not discuss in depth cybersecurity issues related to the use of IoT. With more and more devices connected, the risk of cyberattacks also increases. Future research should consider how to protect data and systems from such threats (Chen et al., 2021). Finally, we recommend that future research not only focus on technical aspects, but also consider human and organizational factors that can influence the successful application of technology in fleet management. With a more holistic approach, we believe that this research can make a greater contribution to the maritime industry.\\u003csup\\u003e32\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePractical Implications:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the results of this study, there are several practical recommendations to improve fleet management at the port. First, ports should consider adopting IoT and Big Data technologies as part of their strategy to improve operational efficiency. By utilizing real-time data, ports can predict traffic patterns and optimize resource use (Kumar et al., 2021).\\u003csup\\u003e33\\u003c/sup\\u003e Second, it is important for ports to establish partnerships with technology companies and data service providers to ensure access to the latest technologies and relevant solutions. This collaboration can assist ports in developing better systems for traffic management and reducing congestion (Hassan et al., 2022).\\u003csup\\u003e34\\u003c/sup\\u003e , the port needs to invest resources in staff training and skill development. By improving their understanding of new technologies, staff will be better equipped to adopt and utilize existing systems, reduce resistance to change, and improve the effectiveness of technology implementation (Nguyen et al., 2022).\\u003csup\\u003e35\\u003c/sup\\u003e Periodic evaluations allow ports to identify areas that need improvement and take necessary actions to improve performance (Zhang et al., 2020).\\u003csup\\u003e36\\u003c/sup\\u003e We encourage ports to participate in global sustainability initiatives and share best practices with other ports. By sharing experience and knowledge, ports can contribute to the development of better solutions to the challenges of the maritime industry as a whole (International Maritime Organization, 2021).\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eBy analyzing big data from various IoT sensors installed on ships and port infrastructure, we found that this technology not only helps predict traffic patterns, but also enables more informed and faster decision-making. The main findings of this study show that the implementation of Big Data and IoT-based solutions can reduce ship waiting times at ports by up to 30% and improve overall operational efficiency (Wang et al., 2020).\\u003csup\\u003e37\\u003c/sup\\u003e The theoretical implications of this study suggest that the integration of digital technology in the maritime sector can form a new paradigm in fleet management. By leveraging real-time data and predictive analytics, researchers and practitioners can better understand maritime traffic dynamics and their impact on port operations\\u003csup\\u003e38\\u003c/sup\\u003e Practically, the results of this study provide guidance for port managers and shipping companies to adopt advanced technologies to improve efficiency and reduce operational costs.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eParticipant Consent Statement All individuals who participated in the questionnaire for this study provided explicit written and/or verbal consent prior to their involvement. The consent process ensured that participants were fully informed about the purpose, scope, and nature of the study, and their participation was entirely voluntary. Confidentiality and anonymity of the participants\\u0026apos; responses were strictly maintained in accordance with ethical research guidelines.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAzhari, M., Sutabri, T. (2024) Smart City Analysis Using the Smart Society 5.0 Concept in Electronic Police Digital Transformation Technology\\u003c/li\\u003e\\n\\u003cli\\u003eQuan, X., Yang, J., Luo, Z. (2021) Correction for: A Model in Digital Business and Economic Forecasting Based on Infographic Data Visualization Technology Big Data Press. Ubiquitous Comput. 28, 445 reviews\\u003c/li\\u003e\\n\\u003cli\\u003eQuan, X., Yang, J., Luo, Z. (2021) Pulled Article: Models in Digital Business and Economic Forecasting Based on Big Data Personal and Ubiquitous Computing IoT Data Visualization Technology 28, 11\\u003c/li\\u003e\\n\\u003cli\\u003eStergiou, C., Bompoli, E., Psannis, K., E. (2023) Security and Privacy Issues in Iot-Based Big Data Cloud Systems in the Digital Twin Scenario of Applied Science\\u003c/li\\u003e\\n\\u003cli\\u003eHasibuan, N., Sihaloho, H., Zein, S., Manodohon, M., A. (2024) Analysis of Challenges and Opportunities for the Development of Economic Globalization in Business Law in Indonesia Vyavahara Duta\\u003c/li\\u003e\\n\\u003cli\\u003eSholicha, N., A., Irfandi, R., Turawan, C. (2023) Internet of Things-Based Livestock Management and Recording in the Goat Fattening Program Journal of Computer Science and Agri-Informatics\\u003c/li\\u003e\\n\\u003cli\\u003eArridha, R. (2021) Real-time Monitoring of River Water Quality Based on the Internet of Things and Big Data Journal of Information, Science and Technology\\u003c/li\\u003e\\n\\u003cli\\u003eBachtiar, A., Trilia, D., Hia, H., A., F., Zafirawan, R., A., Supriyadi, A. (2024) Effective Maritime Surveillance Through the Implementation of Automatic Identification System (AIS) for the Surabaya-Makassar Shipping Route , Globe Scientific Magazine\\u003c/li\\u003e\\n\\u003cli\\u003eSuartana, I., Putra, R., E., Prapanca, A. (2022) Classification of Network Traffic Data with the Big Data Analytics Framework Journal of Information Engineering and Educational Technology\\u003c/li\\u003e\\n\\u003cli\\u003eRizkiyani, H., M., Supriyadi, A., Dao, P., Y., Novitasari, D. (2024) Utilization of Geographic Information Systems in the Development of Indonesia\\u0026apos;s Maritime Security System, Globe Scientific Magazine\\u003c/li\\u003e\\n\\u003cli\\u003ePermadi, H. (2009) Designing Alternative Strategies for Cargo Business Units at PT. Nusantara Card Semesta (NCS) \\u003c/li\\u003e\\n\\u003cli\\u003eSuartana, I., Putra, R., E., Prapanca, A. (2022) Classification of Network Traffic Data with a Big Data Analysis Framework of the Journal of Information Engineering and Educational Technology\\u003c/li\\u003e\\n\\u003cli\\u003eSrijianto, O., B., Devi, P. (2023) Optimizing Payroll Management: A Case Study of the Implementation of a CV Payslip Website. Work by Ambassador of the National Journal of Computing and Information Technology (JNKTI)\\u003c/li\\u003e\\n\\u003cli\\u003eAlfanta, D., Alvaro, O., Suryani, S., Theophilus, T., Leonathan, L., Sondang, S. (2023) Making Surfactant-Free Organic Soap: Exploring Product Diversification and Consumer Preferences in Surabaya TRIDARMA: Community Service (PkM)\\u003c/li\\u003e\\n\\u003cli\\u003eSetiawan, R., Megawati, C., Palevi, B., R., P., D., Hadi, S. (2022) Development of a Borland Delphi and Lora Wireless Communication Solar Panel Power Monitoring System Database Jurnal Bumigora Information Technology (Bite)\\u003c/li\\u003e\\n\\u003cli\\u003eArisman, H., Hartono, B. (2021) Analysis of the Successful Implementation of the Weather Research and Forecast Information System in Supporting the Syntax Literate Weather Modification Technology Project; Indonesian Scientific Journal\\u003c/li\\u003e\\n\\u003cli\\u003ePuspitasari, R. (2017) Analysis of Service Quality in Dry Port Cikarang Using the Importance - Performance Analysis Method and Canoe 13, 121-134\\u003c/li\\u003e\\n\\u003cli\\u003eSofwan, D., M., P. (2015) Analysis The Experience of Joint Creation in a Creative City as a Tourism Destination and Its Impact on Revisit Intentions: A Survey of Indonesian Tourists Visiting the City of Bandung \\u003c/li\\u003e\\n\\u003cli\\u003eSimanjuntak, R., Sihombing, D., W., Wiwoho, B., Dwiyani, N., Kismantoro, T., Rosmayana, R. (2024) Statistical Analysis of Ship Completion Time in the Journal of Marine Transportation Research of Tanjung Priok Port\\u003c/li\\u003e\\n\\u003cli\\u003eMartohandoyo, H., A., R. 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(2020) (Business Process Improvement for IoT-Based Non-Communicable Disease Surveillance System)\\u003c/li\\u003e\\n\\u003cli\\u003eConsumption, P., Electricity, D., Panel, P., Use, L., Network, M., N., Khumaidi, A., Hasin, M., K., Pujiputra, A., P., Irsyad, S., Rinanto, N., Rachman, I., Budi, P., S., Malik, A., Binta, N., , , , , Article, I., History, A., Setia, P., , , (2024) Prediction of Electrical Power Consumption in Electrical Panels Using the Neural Network Method Journal of Industrial Electronics and Automation\\u003c/li\\u003e\\n\\u003cli\\u003eHadiwijaya, H., Prasetya, D., Widyanto, A., Kristian, B., Rahman, A., A., A., Mahardika, M., A. (2023) Digital Transformation in the Craft Industry: Dedy Geabah\\u0026apos;s Practical Approach Through the Adoption of the E-Catalog of the Journal of Community Service to the Nation\\u003c/li\\u003e\\n\\u003cli\\u003eUtomo, I., C. 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(2017) Evaluation and Designing an Accounting Information System for VAT Collection and Payment in an Effort to Improve the Performance Efficiency of the Financial Office of Pt. Pelabuhan Indonesia III (Persero) Tanjung Perak Branch Jurnal Airlangga Accounting and Business Research\\u003c/li\\u003e\\n\\u003cli\\u003ePriyambodo, B., I. (2023) The Utilization of Big Data for Business Improvement of Syntax Literacy Banks; Indonesian Scientific Journal\\u003c/li\\u003e\\n\\u003cli\\u003eWardhani, D., K., Soeharto, B. (2024) Digital-Based EOQ Method to Improve the Efficiency of Material Inventory Management in Digital Transformation Technology of FB Service Hotels\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Big Data, IoT, Intelligent Fleet Management, Maritime Traffic Patterns, Port Congestion\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5824518/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5824518/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIn today's digital era, maritime fleet management faces major challenges related to port congestion and unpredictable traffic patterns. The main objective of this study is to investigate how the use of big data and the Internet of Things (IoT) can predict maritime traffic patterns and reduce port congestion. [\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003e] The methodology used includes analysis of historical data on sea traffic, predictive modeling, and the use of IoT sensors for real-time data collection. Key findings show that the application of this technology can improve the accuracy of traffic predictions by up to 30% and reduce ship waiting times at ports by up to 20%. The conclusion of this study confirms that the integration of big data and IoT can not only optimize fleet management, but also make a significant contribution to port operational efficiency and the overall sustainability of the maritime industry[\\u003csup\\u003e2\\u003c/sup\\u003e][\\u003csup\\u003e3\\u003c/sup\\u003e][\\u003csup\\u003e4\\u003c/sup\\u003e]\\u003c/p\\u003e\",\"manuscriptTitle\":\"Maritime Fleet Management Transformation Using Big Data and IoT\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-01-24 07:41:44\",\"doi\":\"10.21203/rs.3.rs-5824518/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"3bdc7b4c-ec9b-4cdc-971c-dfe60134db6f\",\"owner\":[],\"postedDate\":\"January 24th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":42827181,\"name\":\"Theoretical Computer Science\"},{\"id\":42827182,\"name\":\"Marine and Freshwater Ecology\"},{\"id\":42827183,\"name\":\"Special Education\"}],\"tags\":[],\"updatedAt\":\"2025-01-24T07:41:44+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-01-24 07:41:44\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5824518\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5824518\",\"identity\":\"rs-5824518\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}