OPTIMIZATION OF FREIGHT TRANSPORTATION IN URBAN ENVIRONMENTS

Abstract

In today’s environment, high-quality freight transportation is crucial for ensuring uninterrupted supply chains and business growth. Optimizing these operations not only improves operational efficiency but also helps reduce costs and minimize environmental impact. This article examines key strategies and approaches to improving freight transportation, including the importance of planning, the use of modern technologies, and the implementation of sustainable practices. Applying these approaches enables companies to enhance the efficiency of their logistics processes and strengthen their position in a competitive market. Trucks perform long-haul transport operations consisting of several trips that are often not logistically interconnected. To effectively forecast demand for urban freight transport, it is necessary to develop and test alternative models, as traditional approaches are based on the classic four-stage planning scheme. This paper examines two main approaches to using entropy in transportation modeling and identifies key aspects and constraints that must be considered when developing relevant algorithms. Among the possible options for distributing traffic flows, those that provide the maximum number of feasible solutions while accounting for given constraints—specifically, the total number of trips at each node of the transportation network—are considered the most likely. The proposed trip-oriented entropy maximization model is designed to forecast freight flows while accounting for aggregate demand, i.e., the number of trips associated with each node. Its effectiveness has been confirmed through testing on real-world data. The application of the entropy model for forecasting freight transport demand in urban conditions allows for the determination of transport flows that most accurately reflect actual freight movement patterns.