Due to the large growth in the number of cars being bought and sold, used-car price prediction creates a lot of interest in analysis and research. The availability of used cars in developing countries results in an increased choice of used vehicles, and people increasingly choose used vehicles over new ones, which causes shortages. There is an important need to explore the enormous amount of valuable data generated by vehicle sellers. All sellers usually have the imminent need of finding a better way to predict the future behavior of prices, which helps in determining the best time to buy or sell, in order to achieve the best profit. This paper provides an overview of data-driven models for estimating the price of used vehicles in the Croatian market using correlated attributes, in terms of production year and kilometers traveled. In order to achieve this, the technique of data mining from the online seller “Njuškalo” was used. Redundant and missing values were removed from the data set during data processing. Using the method of supervised machine learning, with the use of a linear regression algorithm for predicting the prices of used cars and comparing the accuracy with the classification algorithm, the purpose of this paper is to describe the state of the vehicle market and predict price trends based on available attributes. Prediction accuracy increases with training the model with the second data set, where price growth is predicted by linear regression with a prediction accuracy of 95%. The experimental analysis shows that the proposed model predicts increases in vehicle prices and decreases in the value of vehicles regarding kilometers traveled, regardless of the year of production. The average value of the first data set is a personal vehicle with 130,000 km traveled and a price of EUR 10,000. The second set of data was extracted 3 months after the previously analyzed set, and the average price of used vehicles increased by EUR 1391 per vehicle. On the other hand, average kilometers traveled decreased by 8060 km, which justifies the increase in prices and validates the training models. The price and vehicle type are features that play an important role in predicting the price in a second-hand market, which seems to be given less importance in the current literature of prediction models.

Seaport Rijeka is located and connected to the strategic EU TEN-T transport routes (Mediterranean and Baltic–Adriatic Corridor). Seaport Rijeka represents the shortest connection between Central and Central-Eastern Europe, and overseas destinations, by land and sea, and is in an excellent position to take advantage of its location. Being the largest and busiest seaport in Croatia, with constant increase in cargo traffic, especially container traffic, with inadequate and incomplete transport infrastructure that creates congestion, Seaport Rijeka will soon reach its capacity limits. One of the possible solutions that would satisfy the increasing demand and mitigate existing problems is establishing a dry port. Establishing a dry port serving Seaport Rijeka on the EU transport routes would greatly contribute to the strategic and operational plans of the EU and Croatia. The focus of this paper is to determine the optimal dry port location for Seaport Rijeka. The AHP methodology was used to determine the optimal dry port location of the Seaport Rijeka, by analyzing a large set of influential factors. The analysis was performed for three groups of possible dry port locations (close, medium distance and distant). Results suggest that optimal dry port locations for Seaport Rijeka are in Miklavlje, Velika Gorica and Vinkovci.

Traveling through a transport network, or ordering and delivering packets, involves fundamental decision-making processes which can be approached by game theory: Rather than simply choosing a route, individuals need to evaluate routes in the presence of the congestion resulting from the decisions made by themselves and everyone else. In this paper, a game theory model for resolving route choices in transport network graphs is used. In the process of doing this, discovering a rather unexpected result known as Braess’s paradox, which shows that adding capacity to a network can sometimes actually cause congestion and an increase in transport costs. The decisions are made by non-cooperative players in a game theory environment known as prisoner’s dilemma. These methods are used to analyze routing problems by competing logistics operators on the transport network consisting of three Eastern Adriatic ports and an intermodal terminal in Budapest. The congestion game can be used in route selection regarding a decrease in transport costs for the carriers who are considered as rational players choosing the most sustainable solution.