Study of Machine Learning Techniques for Transport

Abstract

This work deals with one of the important topics - the organization of safe and optimal movement of land transport vehicles. Organizing the right logistics leads to economic gain as well as a reduction of greenhouse gas emissions into the environment. The main difficulty in solving this problem is that the problem is multiparametric, and dynamically variable in time. Another problem is that because of the above features it is not possible to write an algorithm that will choose the optimal route at each moment of time due to the complexity and variability of the input parameters. An interesting task is the development of software allowing automatic collection of traffic information, and transmission to the input of an adaptive decision-making system for choosing the most correct route. It is therefore proposed to develop an adaptive system based on modern machine learning techniques and genetic algorithms. To meet these challenges, the authors developed machine learning and simulation approaches. The work includes an analysis of machine learning methods, especially the use of neural networks in reinforcement training, as well as an analysis of machine learning methods for the task of finding the optimum route of transport. As a result, software has been developed for the crucial automated transport task through machine learning methods, analysis of sustainability of transport solutions based on machine learning methods and analysis of machine learning supported by digital control systems.