دانلود مقاله کاربرد کلان داده و داده‌کاوی در لجستیک جاده‌ای
چکیده
مقدمه
روش های جمع آوری داده های بارگیری زیرساخت های حمل و نقل برای منطقه مسکو
تجزیه و تحلیل داده های دریافتی
خلاصه
منابع
Abstract
Introduction
Methods for collecting data of the transport infrastructure loading for the Moscow region
Analysis of the received data
Summary
References
چکیده
این مقاله به کاربرد فناوری های مدرن داده کاوی و داده کاوی در لجستیک می پردازد. تجزیه و تحلیل مطالعات مربوط به کلان داده و داده کاوی در لجستیک انجام شده است. مشکلات جمع آوری، تجزیه و تحلیل و تفسیر داده ها در مورد حجم کار سیستم حمل و نقل منطقه مسکو در نظر گرفته شده است. این مقاله چشم‌اندازهایی را برای کاربرد نتایج برای شرکت‌های حمل‌ونقل مدرن و سازمان‌های دولتی در برنامه‌ریزی ساخت زیرساخت‌های حمل‌ونقل ارائه می‌کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The article deals with the application of modern Big Data and Data Mining technologies in logistics. The analysis of studies related to Big Data and Data Mining in logistics has been carried out. The problems of collecting, analyzing and interpreting data on the workload of the transport system of the Moscow region are considered. The paper presents prospects for the application of the results for modern transport companies and government agencies in the planning of transport infrastructure construction.
Introduction
The economic development of the country is the most important task in the management of the state. Creation of favorable conditions for running and developing business allows attracting additional investments in a market economy. According to The Global Competitiveness Report 2019, the Russian Federation is ranked 43rd based on the Global Competitiveness Index 4.0 (GCI). Looking at the metrics that affect the overall ranking of a country Road connectivity and Quality of road infrastructure one of it. Russia ranks 41 - in the road connectivity and 99th - in quality of road infrastructure. Thus, it can be argued that the road transport infrastructure is the most important factor in the development of the economic component. In logistics the development of the road transport system and its congestion directly affect for costs.
Results and analyses
The presented method of collecting information and the results obtained allow to make conclusion of using of such data processing technologies in logistics processes. An important problem in logistics is time losses, based on current statistical data and predictive models, conclusions can be drawn about traffic planning. By choosing time intervals for road movement with the least load by statistic it possible saves significant time resources. By integrating data on weather conditions and road traffic accidents into the presented model it is possible to form a predictive model for the road transport system. The presented model makes it possible to estimate the congestion by directions which can be used as a tool in planning the reconstruction and development of transport infrastructure.