Modelling of traffic flows and supply chains based on geospatial knowledge

Abstract

The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.