Urban mobility has undergone and continues to undergo a profound transformation driven by the convergence of artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics in recent years. These technologies are redefining adaptive traffic control systems, enabling real-time decision-making and increasing the efficiency and safety of road networks. The main questions addressed in the review explore how the integration of advanced technologies such as IoT, AI in traffic systems, are useful in optimizing traffic flows, vehicle coordination and infrastructure adaptability in increasingly complex traffic environments. The integration of IoT-enabled devices and AI-based algorithms has been essential to enable data-driven approaches to urban traffic control. Predictive analytics improves emergency response mechanisms, improves traffic signal operations, and supports the deployment of autonomous and connected vehicles. Among the various methodologies evaluated, AI-based models combined with IoT sensors demonstrated superior performance, reducing average traffic delays by up to 30% and improving safety metrics in various urban environments. This systematic review underscores the transformative potential of integrating AI, IoT, and predictive analytics into urban traffic management, offering a blueprint for smarter, more sustainable urban transportation solutions.

In this paper, we studied the effects of using Microsoft HoloLens 2 in a Metaverse-based collaborative mixed reality environment on the driver’s social presence while using an autonomous driving system. In (semi-) autonomous vehicles the driver is the system’s monitor, and the driving process becomes a secondary task. Our approach is motivated by the advent of Microsoft Mesh XR technology that enables immersion in multi-person, shared mixed reality environments. We conducted a user study comparing the effects on social presence in two scenarios: baseline and mixed reality collaboration. During the baseline condition, participants communicated and interacted with another person using Skype/Meet which was installed on a mobile tablet. In the second scenario the participants used the Microsoft Mesh application installed on HoloLens 2 to collaborate in a mixed reality environment where each user is represented by an augmented 3D avatar. During the experiment, the participant had to perform a social interaction tell-a-lie task and a remote collaborative tic-tac-toe game, while also monitoring the vehicle’s behavior. The social presence was measured using the Harms and Biocca questionnaire, one of the most widely used tools for evaluating the user’s experience. We found that there are significant statistical differences for Co-presence, Perceived Emotional Interdependence, and Perceived Behavioral Interdependence, and participants were able to easily interact with the avatar in the mixed reality scenario. The proposed study procedure could be taken further to assess the driver’s performance during handover procedures, especially when the autonomous driving system encounters a critical situation.

Currently, biofuels represent a solution for the European Union in the transportation sector in order to reduce the greenhouse gas (GHG) emissions and the dependency of fossil fuels. Biodiesel from vegetable oils is a solution for countries with low GDP per capita to strengthen the internal agriculture, provide jobs, and reduce the use of fossil fuels. In this study, we model and simulate a temperature regulator designed for the biodiesel transesterification process in a discontinuous batch reactor, using methanol and a homogenous basic catalyst. The simulation was based on the kinetical model of the transesterification reaction and the mathematical model of the reactor. We considered molar ratios of alcohol/oil of 6:1 and 9:1, respectively, to shift the reaction equilibrium towards the production of fatty acid methyl esters. In the design of the simulation, the methanol boiling point was considered a restriction, therefore, temperatures below 65 °C were imposed. The results demonstrate that the increase in temperature results in a decrease in the reaction time and a higher yield, especially for the 6:1 molar ratio reaction, and that the optimum temperature for the batch reactor is of 60 °C. Automatic control improves the performance and costs of production.

This paper presents new results concerning the flow around the Ahmed body fitted with a rear underbody diffuser without endplates, to reveal the influence of the underbody geometry, shaped as a Venturi nozzle, on the main aerodynamic characteristics. The study is performed for different geometrical configurations of the underbody, radius of the front section, length and the angle of the diffuser being the parameters, which are varied. Later, based on a theoretical approach, the coefficients of the equivalent aerodynamic resistances of the front section of underbody and diffuser are computed, which help to evaluate the drag due to underbody geometry.

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions.

The tendency in the past few years has been to introduce tyres with lower rolling resistance coefficients to the market. This paper presents a mathematical method for determining the rolling resistance coefficients variation depending on the speed. The method uses power balance which results from automobile dynamics while rolling on chassis dynamometer. The rolling resistance coefficients of tyres obtained through ‘drum test method’, for which the rolling resistance coefficients variation is known in terms of vehicle speed, are considered as reference values, while than rolling resistance coefficients of tyres obtained through ‘MAHA roller dynamometer’ using the recorded lost drag power in the roll-out phase on the stand are considered as tested values. The rolling resistance coefficients variation could be determined up to the maximum permissible speed of the tyre, for all wheels trained on the stand and not just for one tyre, as determined in laboratory conditions. The test conditions are similar to those in real road conditions, where the temperature of the environment and wheels cannot be controlled. The values obtained by the authors’ proposed method were compared with the values obtained by the ‘drum test method’. The main contribution of the proposed method is to estimate the rolling resistance coefficients without using a very expensive test facility.