## 3 Materials and Methods
### Overview of the Applicable Methods
Environmental protection refers to the minimization of air and water pollution, impacts on natural habitats, fuel consumption, noise and vibration, etc. Safety refers to the minimization of fatalities, minimization of injuries, minimization of the number of accidents, etc., while the contribution to economic growth refers to the maximization of regional development, positive impact on tourism, positive impact on local employment, etc. MCDM techniques are increasingly used in transport decision-making today. Their key advantages are transparent decisions, better organization and management of large amounts of data, and complete controllability of the process. MCDM cannot be fully automated with tools, techniques, and algorithms, especially when it comes to making a decision/evaluating a problem that is related to people, such as transportation. Therefore, the goal of any MCDM technique is to provide guidance and help the decision-maker to discover the optimal solution to the problem, i.e., to achieve goals. Figure 2 shows an overview of MCDM techniques. In general, MCDM methods applicable to the transport sector can be divided into two following categories: methods for solving problems with a discrete set of options (a finite number of alternative solutions that are known at the outset) and methods for solving problems that require a choice from continuous sets of options (encompassing an infinite or very large number of alternative solutions that are not known at the outset). Methods related to a finite number of alternative solutions (options) are suitable for \"poorly structured\" problems, which implies the following: problems with complex objectives, unclear formulation, and the nature of the observed problem gradually changes during the process of solving the problem. These methods are usually called multiple-attribute decision-making (MADM) or multi-criteria analysis (MCA) models. They focus on solving problems by finding the best alternative or set of good alternatives with respect to defined attributes/criteria and their weights. The results of poorly structured problems are different criteria for evaluating solutions and variable constraints. Examples of MADM methods include simple additive weighting (SAW), multi-attribute value/utility theory (MAVT/MAUT), Elimination and Choice Translating of Reality (ELECTRE), the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), the analytic hierarchy process (AHP), etc. [44]. For \"well-structured\" problems, methods that include an infinite or at least a very large number of alternative solutions are suitable. Well-structured problems are those where the current state and the desired future state (goals) are known as the way to achieve the desired state. The model includes an infinite or a very large number of alternative  
Figure 2: Typical MCDM techniques in the transport sector [50]. solutions that are not explicitly known at the beginning, the limitations are analyzed, and the adequate solution is reached by solving the mathematical model. These methods are called multiple-objective decision-making models (MODMs) and consist of two phases [44], namely generating a set of effective solutions and exploring that set in order to find a \"compromise\" using interactive procedures. Examples of MODM methods are the global criterion method (GCM), utility function method (UFM), goal programming (GP), step method (STEM), genetic algorithms, etc. Recent methods include the fuzzy analytic hierarchy process (FAHP), analytic hierarchy process-fuzzy linear programming (AHP-FLP), and analytic hierarchy process-Taguchi loss function multi-choice goal programming (AHP-TLF MCGP). FAHP uses fuzzy logic to model uncertainty in the decision-making process, ideal for situations where data are imprecise. AHP-FLP combines the AHP methodology with linear programming, enabling optimization while taking uncertainty into account. It is used in resource optimization.