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.

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.

This research shows the relationship between the energy emission parameters and CO2 equivalents for conventional fossil fuel-powered vehicles (ICEV, Internal Combustion Engine Vehicles) and hybrid electric vehicles (HEV) to determine the life cycle costs of the vehicles. The combination of transport policy and alternative fuels has the purpose of creating a sustainable transport system. Transport policy focuses on increasing energy efficiency and reducing the price of electric vehicles as technology advances. The profitability for each vehicle type was also observed through current vehicle purchase prices. The main objective of this paper is to study the environmental impact of diesel vans, taking into account lifelong energy use, fuel consumption and CO2 equivalents through air pollution. Although the purchase price of the ICEV is less than the HEV, all electric vehicles are determined to have the lowest overall environmental impact during the operational phase. The goal of transport companies and logistics operators that own a fleet is to achieve quality service with maximum cost and negative environmental impact reduction.

The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations.