The intellectual approaches to data management in transport and freight operations
Keywords:
Freight transportation, document circulation, data flow, optimization, intelligent approaches, artificial intelligence
Abstract
In order to improve the efficiency of freight transportation services on railway networks, there is a need to automate and optimize the document circulation processes. The possibilities of optimizing data flows using artificial intelligence and data analysis methods are being explored.
References
[1] Abdullayev A. "Logistika tizimlarida ma'lumotlar oqimini boshqarish". Toshkent: Logistika nashriyoti, 2020.
[2] Karimov B. "Sun'iy intellekt va uning transport sohasidagi qo‘llanilishi". Transport va logistika jurnali, 2021, №3, 45-52-betlar.
[3] Smith J. "Intelligent Approaches to Data Flow Optimization in Freight Transportation". International Journal of Logistics, 2019, Vol. 22, Issue 4, pp. 345-360.
[4] Lee K., Park S. "Digital Transformation in Railway Freight Documentation". Journal of Transportation Technologies, 2020, Vol. 10, pp. 150-162.
[5] O‘zbekiston Respublikasi Transport vazirligi rasmiy sayti: www.mintrans.uz
[6] Chen Y. "Application of Machine Learning in Logistics Data Management". Logistics Research, 2018, Vol. 11, Issue 2, pp. 89-101.
[7] Ivanov D. "Big Data Analytics in Railway Freight Transportation". Procedia Computer Science, 2019, Vol. 159, pp. 1086-1095.
[8] Wang L., Zhang X. "Optimizing Data Flow in Supply Chain Management Using AI Techniques". International Journal of Supply Chain Management, 2021, Vol. 6, Issue 3, pp. 23-35.
[9] Anderson D. "Optimization Techniques in Logistics". Operations Research Journal, 2019, Vol. 16, Issue 2, pp. 100-115
[10] Turdiev O.A., Smagin V.A., Kustov V.N. Investigation Of The Computational Complexity Of The Formation Of Checksums For The Cyclic Redundancy Code Algorithm Depending On The Width Of The Generating Polynomial. В сборнике: CEUR Workshop Proceedings. Proceedings of the Workshop "Models and Methods for Researching Information Systems in Transport 2020" on the basis of the departments "Information and Computer Systems" and "Higher Mathematics". 2020. С. 129-135.
[2] Karimov B. "Sun'iy intellekt va uning transport sohasidagi qo‘llanilishi". Transport va logistika jurnali, 2021, №3, 45-52-betlar.
[3] Smith J. "Intelligent Approaches to Data Flow Optimization in Freight Transportation". International Journal of Logistics, 2019, Vol. 22, Issue 4, pp. 345-360.
[4] Lee K., Park S. "Digital Transformation in Railway Freight Documentation". Journal of Transportation Technologies, 2020, Vol. 10, pp. 150-162.
[5] O‘zbekiston Respublikasi Transport vazirligi rasmiy sayti: www.mintrans.uz
[6] Chen Y. "Application of Machine Learning in Logistics Data Management". Logistics Research, 2018, Vol. 11, Issue 2, pp. 89-101.
[7] Ivanov D. "Big Data Analytics in Railway Freight Transportation". Procedia Computer Science, 2019, Vol. 159, pp. 1086-1095.
[8] Wang L., Zhang X. "Optimizing Data Flow in Supply Chain Management Using AI Techniques". International Journal of Supply Chain Management, 2021, Vol. 6, Issue 3, pp. 23-35.
[9] Anderson D. "Optimization Techniques in Logistics". Operations Research Journal, 2019, Vol. 16, Issue 2, pp. 100-115
[10] Turdiev O.A., Smagin V.A., Kustov V.N. Investigation Of The Computational Complexity Of The Formation Of Checksums For The Cyclic Redundancy Code Algorithm Depending On The Width Of The Generating Polynomial. В сборнике: CEUR Workshop Proceedings. Proceedings of the Workshop "Models and Methods for Researching Information Systems in Transport 2020" on the basis of the departments "Information and Computer Systems" and "Higher Mathematics". 2020. С. 129-135.
Published
2025-03-31
How to Cite
Turdiev, O., Rasulmuhamedov, M., & Tuxtaxodjaev, A. (2025). The intellectual approaches to data management in transport and freight operations. Journal of Transport, 2(1), 5-8. Retrieved from https://jot.tstu.uz/index.php/jot-journal/article/view/26
Section
Transport Process Organization and Logistics
