Features of Organisation of Transport and Logistics Cluster Prioritising Intelligent Transport Technologies Development

Abstract

The article examines the prospects for organizing a cluster as an effective tool for ensuring connectivity of territories of the Russian Federation through the systematic and integrated implementation of intelligent transport technologies, which corresponds to strategic directions of development of transport in the Russian Federation and determines the relevance of the topic. The objective of the study is to determine the features of organisation of the transport and logistics cluster prioritising development of intelligent transport technologies by analysing the prospects for their development, studying variability of characteristics and structure of the cluster under various conditions of its formation based on the methods of formal logic, grouping, analysis of statistical data, normative-legal information, information synthesis. The study resulted in identification of prerequisites for the most rapid development and effective implementation of intelligent transport systems within the cluster. The expediency of using this approach has been substantiated, despite its labour intensity and cost. The study suggests definitions of an innovative transport and logistics cluster, as wells as characteristics of the transport and logistics cluster prioritising intelligent transport technologies. The study revealed the specifics of organisation of this cluster, which is primarily determined by the presence of dual characteristics. The peculiarities of cluster formation are reflected in the proposed structure of the cluster under study. The role of the state in organizing an innovative transport and logistics cluster is also defined

Intelligent Identification of Logistics Packaging Products Based on Neural Network

“…In the image recognition of LP products, R-CNN is used to extract the image features of the packaging products, but the disadvantage is that it is necessary to extract features for each candidate frame that may be redundant. In order to solve this problem, some scholars propose a Fast R-CNN model, using ROI-Pooling in the network to fix the feature size, only need to extract features from the entire image and intercept the features of the corresponding candidate frame from the image, which greatly reduces the detection time [3]. In warehousing logistics management, RFID technology can reduce out-of-stock losses and achieve transparent management of warehousing and logistics.…”