Journal of Transportation Research

Journal of Transportation Research

Design of a Knowledge Management Implementation Model in the Lorestan General Directorate of Railways Using a Data Mining Approach Based on Decision Trees and Rough Sets

Document Type : Original Article

Authors
Ali Bohloli 1
mahdi Mohamadzadeh 2
Kazem Taghandiki 3
1 Assistant Professor, Department of Computer Engineering‌, Technical and Vocational University (TVU), Tehran, Iran.
2 M.Sc., Grad., Computer Engineering (Software), Train Movement Department, Lorestan Railway General Administration, Doroud, Iran.
3 Assistant Professor, Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.
10.22034/tri.2026.584303.3463
Abstract
The present study was conducted with the aim of designing a knowledge management implementation model in the Lorestan Railway General Directorate. Given the specific conditions of railway lines in mountainous regions and the importance of traffic and operations management, the utilization and transfer of employees’ tacit knowledge play a crucial role in enhancing the safety and quality of railway services. In terms of purpose, this is an applied study, and methodologically, it follows a descriptive-survey design with a quantitative approach. The statistical population consisted of 220 employees from the Traffic and Operations Department of Lorestan Railway. Data collection was carried out using a researcher-made questionnaire based on the Lee and Hong (2014) model, the reliability of which was confirmed with a Cronbach’s alpha coefficient of 0.91. Data analysis was performed within the CRISP-DM framework utilizing Decision Tree algorithms and Rough Set Theory in Excel, Weka, and Rosetta software environments. The results indicated that the successful implementation of knowledge management is the outcome of the synergy among key components, including knowledge sharing (knowledge system), employee participation in decision-making and support for their creativity and innovation (organizational culture), and information systems (information technology system). A confusion matrix was employed to evaluate the models; the findings revealed that the Rough Sets model achieved an accuracy of 0.925 and a precision of 0.904, while the Decision Tree model yielded a precision of 0.90 and an accuracy of 0.869. The proposed model can serve as an operational framework to facilitate the transfer of experience, reduce operational errors, and enhance safety in the Lorestan Railway.
Keywords
Knowledge Management
Data Mining
Traffic and Operations Department
Lorestan Railway
Subjects
-جلیلوند، جمشید، شریفیان، اسماعیل، قهرمان تبریزی، کورش، (۱۴۰۲)، ارائه مدل مدیریت دانش بر مبنای فناوری اطلاعات در تربیت‌بدنی آموزش‌وپرورش ایران. پژوهش در ورزش تربیتی، دوره یازدهم، شماره سی و سوم،۱۳۷-۱۵۲.
-خادمی‌زاده، شهناز، محمدی، زینب، کوهی رستمی، منصور، مهرعلی‌زاده، یدالله (۱۴۰۳) ارائه الگوی مدیریت دانش در دانشگاه‌ها با رویکرد فراترکیب، مدیریت راهبردی دانش سازمانی، دوره هفتم، شماره دوم،
۱۰۶-۷۵.
-زمانی طبقدهی، لیلا، زمانی طبقدهی، عباس، مؤمنی مهموئی، حسین (۱۴۰۱). مؤلفه‌ها و شاخص‌های الگوی استقرار مدیریت دانش در سازمان آموزش‌وپرورش. آموزش و توسعه منابع انسانی، دوره سی و چهارم، شماره نهم،۲۱۷-۱۹۲.
-کریمی پویا، محمدرضا، قنبر، مهرداد، جمشیدی نوید، بابک، اسماعیل‌‌پور، منصور (۱۳۹۸). بررسی دقت ماشین‌های بادگیر در پیش‌بینی بازده حاصل از تغییر قیمت سهام با استفاده از مدل رافست، نزدیک‌ترین همسایه و درخت تصمیم‌گیری، مهندسی مالی و مدیریت اوراق بهادار، دوره دهم، شماره سی و هشتم،۲۳۴-۲۱۵.
-محمدزاده، مهدی، بهلول، علی، نبی‌زاده، محسن (۱۴۰۴). ارزیابی چالش‌های استقرار هوش مصنوعی در راه‌آهن لرستان بر پایه مدل دوبعدی انسان‌محور و سیستم‌محور. چهارمین کنفرانس بین‌المللی شهر هوشمند، چالش‌ها و راهبردها. مؤسسه آموزش عالی آپادانا.
-وفائی بصیر، حسین، اسماعیل‌پور، منصور، اسلامبولچی، علیرضا، صارم. علی‌اصغر (۱۴۰۰). تحلیلی نوین در شناسایی الگوهای موفقیت کارآفرینی در صنایع کوچک و متوسط با استفاده از داده‌کاوی،
پژوهش‌نامه بازرگانی، دوره بیست و پنجم شماره نود و هشتم،۵۸-۲۷.
-Abbas, Y., Martinetti, A., Rajabalinejad, M., Frunt, L., & van Dongen, L. A. (2021). Tacit knowledge sharing for system integration: A case of Netherlands railways in Industry 4.0. In Research Anthology on Cross-Industry Challenges of Industry 4.0, IGI Global Scientific Publishing, 480-493.
-Baysari, M. T., McIntosh, A. S., & Wilson, J. R. (2008). Understanding the human factors contribution to railway accidents and incidents in Australia. Accident Analysis & Prevention, 40(5), 1750–1757. doi.org/10.1016/j.aap.2008.06.013
-Bris-Peñalver, F. J., Verdecia-Peña, R., & Alonso, J. I. (2026). A Survey of AI-Enabled Predictive Maintenance for Railway Infrastructure: Models, Data Sources, and Research Challenges. Sensors, 26(3), 906.
-Brzozowska, J., Pizoń, J., Baytikenova, G., Gola, A., Zakimova, A., & Piotrowska, K. (2023). Data engineering in CRISP-DM process production data–case study. Applied Computer Science, 19(3).
-Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS inc, 9(13), 1-73.
-Cheng, A. (2023). Evaluating Fintech industry's risks: A preliminary analysis based on CRISP-DM framework. Finance Research Letters, 55, 103966.
-Duque, J. (2024). Data mining for knowledge management. Procedia Computer Science, 239, 257-264.
-Dykan, V., Ovchynnikova, V., Dykan, V., Kalicheva, N., & Korin, M. (2019). Strategic Knowledge Management as Means of Ensuring the Competitiveness of Railway Transport Enterprises. In SHS Web of Conferences, EDP Sciences.Vol. 67, 01003.
-Foroutani, S., Fahimian, N., Jalalinejad, R., Hezarkhani, M., Mahmoudi, S., & Gharleghi, B. (2024). Navigating knowledge management implementation success in government organizations: A type-2 fuzzy approach. arXiv.
-Galgotia, D., & Lakshmi, N. (2024). RETRACTED ARTICLE: Development of IOT-based methodology for the execution of knowledge management using artificial intelligence in higher education system: D. Galgotia, N. Lakshmi. Soft Computing,28(Suppl 2), 599-599.
-Georgiev, N. (2013). Knowledge management system for improvement of quality of railway transport service. Machenes, technologies, materials. ISSN, 1313-0226.
-Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.
-Henz, J. L., & Oliveira, M. (2024). Knowledge management implementation: A systematic literature review. Knowledge and Process Management, 31(4), 284-294.
-Henz, J. L., & Oliveira, M. (2024). Knowledge management implementation: A systematic literature review. Knowledge and Process Management, 31(4), 284-294.
-Křižan, L., Vojtek, M., Široký, J., Gašparík, J., & Dedík, M. (2024). Human Resource Efficiency in Sustainable Railway Transport Operation. Sustainability, 16(22), 10095.
-Lee, M. C., & Hong, J. J. (2014). The combination of knowledge management and data mining with knowledge warehouse. In Proceedings of the 2014 International Conference on Informatics, Networking and Intelligent Computing (ICINIC).
 -Maataoui, S., Bencheikh, G., & Bencheikh, G. (2023, July). Predictive maintenance in the industrial sector: A CRISP-DM approach for developing accurate machine failure prediction models. In 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications, IEEE. (ACTEA),223-227.
-Morant, A., Westerberg, M., & Larsson-Kråik, P. O. (2014). Knowledge management in a railway network: The case of signalling systems. In International Conference on Railway Technology: Research, Development and Maintenance: 08/04/2014-11/04/2014.Civil-Comp Press.
-Nisbet, R., Elder, J., & Miner, G. (2009). Basic algorithms for data mining: A brief overview. Handbook of statistical analysis and data mining applications, 13(3), 121-150.
-Pachl, J. (2020). Railway signalling principles. Technische Universität Braunschweig, 1-83.
Pachl, J. (2020). Railway signalling principles. Technische Universität Braunschweig, 1-83.
-Queiroz, M. M. (2023). Knowledge Management in Public Transport: A Portuguese Empirical Study. In European Conference on Knowledge Management,Vol. 2, 1662-R36. Academic Conferences International Limited.
-Reason, J. (2016). Managing the risks of organizational accidents. Routledge.
-Sathyanarayanan, S., & Tantri, B. R. (2024). Confusion matrix-based performance evaluation metrics. African Journal of Biomedical Research, 27(4S), 4023-4031.
-Tabaghdhi, Z., & Hoseini Tabahgdehi, L. (2023). Components and indicators of the model of establishing knowledge management in the organization. Quarterly Journal of Training and Development of Human Resources, 34(34), 192.
Journal of Transportation Research
Volume 23, Issue 4
Autumn 2026
Pages 449-464