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Correlation analysis of factors affecting piggyback transportation in Uzbekistan

Ziyoda MukhamedovaTashkent State Transport University, Department of Transport and Cargo Systems, Temiryulchilar str., Tashkent, 100167, UzbekistanGulshan IbragimovaTashkent State Transport University, Department of Transport and Cargo Systems, Temiryulchilar str., Tashkent, 100167, UzbekistanAltinbek TulayevTashkent State Transport University, Department of Transport and Cargo Systems, Temiryulchilar str., Tashkent, 100167, UzbekistanShohrukh KayumovTashkent State Transport University, Department of Transport and Cargo Systems, Temiryulchilar str., Tashkent, 100167, UzbekistanАdylkhan KibishovKhoja Akhmet Yassawi International Kazakh-Turkish University, Computer Engineering Department, 29, Bekzat Sattarhanov Street, Turkestan, 161200, Kazakhstan
E3S Web of Conferencesjournal2024en
ABI

Abstract

Rail transport is traditionally used in the implementation of international and intercontinental piggyback transportation. Despite a significant level of computerization and informatization, the level of delays in the delivery of goods in the field of piggyback intermodal transportation is not decreasing. The unsatisfactory speed of piggyback trains is a significant factor in these delays. This situation has developed due to the lack of effective approaches to building management systems that would demonstrate a high level of efficiency in the face of uncertainty, which is a natural component of the transportation process. The article is devoted to the correlation analysis of factors affecting the cargo turnover of piggyback transportation in the Republic of Uzbekistan. The main factors affecting the cargo turnover of piggyback transportation were identified; the degree of the effect was established by statistical methods. Based on data obtained for the last ten years, a correlation matrix and a regression model of cargo turnover were built. The results obtained make it possible to build forecasts for the cargo turnover of piggyback transportation for 2–4 years with a 95% confidence interval.

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