Determining a suitable location for an intermodal terminal is a critical element of the terminal establishment process, a decision on which the functionality of the entire intermodal freight distribution chain depends. The purpose of this article is to evaluate the criteria used for deciding on suitable locations for intermodal terminals in Croatia. First, quality indicators of intermodal terminals were identified: flexibility, safety and security, reliability, time, and accessibility. Based on these quality indicators, and in compliance with the European traffic policy and efficient functioning of intermodal transport, location criteria were formed and evaluated: legislative, environmental, goods flows, spatial, technical-technological, and organizational. Each criterion is divided further into subcriteria and evaluated using the analytical hierarchy process (AHP) method. The results of the study show that the criterion of goods flows has the most significant impact on the selection of the terminal location, followed by the spatial criterion. A position that connects to the European traffic corridor network is essential for a Croatian terminal's success.

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

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.