First-year students undergo several transformations like the transition from high school to university schedules, teaching methods, and discipline specificities to social changes that come with meeting new colleagues or moving to other locations far from family and friends. The COVID-19 outbreak brought additional concerns and uncertainties once educational systems implemented distance learning schemes for public health management. Nevertheless, higher educational organizations preferred to implement hybrid approaches for student engagement motivation and high dropout rate avoidance. In Romania, such an educational system has been applied with caution. Since the pandemic needs to be understood as an opportunity for adaptation and education improvements, the purpose of the present paper is to uncover lessons learned and to develop a systematized model based on students’ perception regarding face-to-face, online and hybrid systems. For this reason, a grounded theory approach has been preferred. Thus, 149 Romanian students enrolled in the first year in engineering specialities with ages between 18–26 years both male (50.3%) and female (49.7%) have answered the research questions in an online environment due to pandemic restrictions. Based on the online asynchronous student interviews, 220 codes and a further 13 categories have been developed. After a pertinent analysis of the relationships between categories and relevant literature sorting, a theoretical model for the Romanian higher educational current pandemic situation has been proposed. The main face-to-face and online education characteristics are outlined, the hybrid approach becoming a bridge between the two systems. Management implications are outlined together with further research directions.

The population growth and economic development increase the need for mobility. As consequences, we can mention the environmental impact of CO2 emissions and that the road accidents are among the top 10 causes of death worldwide. As a solution to this problem, the concept of Intelligent Transportation Systems emerged. Part of this concept is smart city approach, consisting in a combination of Internet of Things and Information and Communication Technology to manage city issues. After 2014, a lot of regulations were established by the European Parliament, regarding smart city concept implementation. The purpose of this paper is to present a comparison between the implementation of smart city approach in different locations from Romania.

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi–Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi–Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi–Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.

Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

Many cities around the world implement the Intelligent Transportation Systems (ITS) concept to reduce road traffic congestion. This concept implies the usage of Internet of Things (IoT) technologies to ensure a better traffic flow, especially in crowded urban areas. Sensors networks became part of road infrastructure and have as main responsibilities to collect real-time traffic data and to send them to Traffic Management Centres (TMCs) which control further the traffic lights timings. Today’s technologies meet some limitations in terms of efficiency. This paper starts with an overview of these limitations to create a path for discussing the challenges that the development of ITS will meet in the context of Future Networks (FN) 2030 concept. Based on this development path, the identification of current limitations and possible future challenges consists of an important step of this research. Further, the main contribution in this research is to show how the ITS shall adapt to meet the requirements of FN 2030 by providing an architecture for Future ITS 2030 that meets the general ITS needs.