Application of algorithmic models of machine learning to the freight transportation process

Abstract

The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined. They are vehicle routing, choice of cargo type, transportation type and vehicle type; forecasting fuel consumption by vehicles, disruptions in transportation, transport costs, duration of the order fulfillment; evaluation of the rolling stock fleet and the efficiency of carrying out the transport task. Based on the researched publications, the most common algorithmic models of machine learning in freight transportation were identified, and their effectiveness was analyzed. Linear and logistic regression models are simple enough; however, they do not always provide high simulation results. Deep learning models are quite widely applied to all identified areas. Decision tree and random forest models often show the highest simulation performance. Models of k-nearest neighbors and support vectors should be used both in classification tasks, for example, in choosing the type of cargo and type of transportation, and for forecasting the fuel consumption and the duration of the transport process.

Data Justification for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery

“…However, the use of modern approaches for forecasting specific fuel consumption involves the use of large databases describing transportation processes at specific enterprises. Among such approaches, machine learning methods are particularly relevant now [7][8][9]. Forecasting specific fuel consumption using machine learning methods allows for establishing regularities and identifying relationships between data.…”

Supervised Machine Learning Models for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery

“…This is important not only for elevating cargo delivery efficiency and mitigating risks but also for identifying essential efficiency factors. Concurrently, leveraging computational intelligence stands out as a potent method for enhancing the effectiveness of these models [3,4].…”