Traffic has a direct impact on local and regional economies, on pollution levels and is also a major source of discomfort and frustration for the public who have to deal with congestion, accidents or detours due to road works or accidents. Congestion in urban areas is a common phenomenon nowadays, as the main arteries of cities become congested during peak hours or when there are additional constraints such as traffic accidents and road works that slow down traffic on road sections. When traffic increases, it is observed that some roads are predisposed to congestion, while others are not. It is evident that both congestion and urban traffic itself are influenced by several factors represented by complex geospatial data and the spatial relationships between them. In this paper were integrated mathematical models, real time traffic data with network analysis and simulation procedures in order to analyze the public transportation in Oradea and the impact on urban traffic. A mathematical model was also adapted to simulate the travel choices of the population of the city and of the surrounding villages. Based on the network analysis, traffic analysis and on the traveling simulation, the elements generating traffic congestion in the inner city can be easily determined. The results of the case study are emphasizing that diminishing the traffic and its effects can be obtained by improving either the public transport density or its accessibility.

In the face of pressing global challenges such as climate change, socioeconomic inequalities, and rapid urbanization, ensuring sustainable development in the regions has become essential. The COVID-19 pandemic has highlighted how vulnerable cities are to unforeseen crises and underscored the urgent need for proactive urban planning strategies capable of navigating dynamic and unpredictable futures. In this context, the use of geographic information systems (GIS) offers researchers and decision makers a distinct advantage in the study of spatial data and enables the comprehensive study of spatial and temporal patterns in various disciplines, including real estate valuation. Central to the integration of modern technology into real estate valuation is the need to mitigate the inherent subjectivity of traditional valuation methods while increasing efficiency through the use of mass appraisal techniques. This study draws on extensive academic literature comprising 103 research articles published between 1993 and January 2024 to shed light on the multifaceted application of GISs in real estate valuation. In particular, three main areas are addressed: (1) hedonic models, (2) artificial intelligence (AI), and mathematical appraisal models. This synthesis emphasizes the interdependence of numerous societal challenges and highlights the need for interdisciplinary collaboration to address them effectively. In addition, this study provides a repertoire of methodologies that underscores the potential of advanced technologies, including artificial intelligence, GISs, and satellite imagery, to improve the subjectivity of traditional valuation approaches and thereby promote greater accuracy and productivity in real estate valuation. By integrating GISs into real estate valuation methodologies, stakeholders can navigate the complexity of urban landscapes with greater precision and promote equitable valuation practices that are conducive to sustainable urban development.

In this paper we aimed to build a composite financial index for measuring the financial health of the companies listed in the AERO (Alternative exchange in Romania) market of the Bucharest Stock Exchange. We used a principal component analysis in order to build this composite financial index using the rates of return, liquidity and the management of 25 companies listed in the AERO market for the period 2011–2018. We conceived this composite indicator as a score function that established according to the numerical values that result from its application when a company was financially healthy, when it had a poor financial health and when it was financially stable. In order to test the financial health of the selected SMEs (small and medium enterprises), we used the one sample t-test under the model of the study and the three classifications of Z (Z < 0—companies with poor financial health, 0 ≤ Z ≤ 0.5—companies with good financial health and Z > 0.5—companies with very good financial health). In this study we also aimed to identify the possible correlations between the solvency rate and the financial health index and between solvency rate and the evolution of some economic and financial measures of the companies’ activities. The results of the regression analysis using panel data showed a positive and statistically significant relation between solvency and the three rates (rates of return, of liquidity and of management, respectively) determined using the analysis of the principal components. The former model of the solvency rate identified correctly 94.9% of the SMEs with poor financial health, 40% of the SMEs with stable financial health and 72.2% of the SMEs with good financial health.

As mentioned by the specialized media, in Romania, in the recent years the banking system and the companies are facing a massive reduction of the values for the commercial properties developed or acquired before the crisis. It is widely known that the value of the commercial properties is directly linked by their location. Considering these elements the current paper focuses on combining specific financial and accounting valuation techniques with location analysis techniques for a better assessment of the values for the commercial real estate properties. While the financial and accounting techniques use methods of calculation established according to the International Valuation Standards, on the other hand this task can be highly simplified if it uses land property management systems or information systems such as the GIS (Geographical Information System). The main function of the land property assessment system, is to define the quantitative and qualitative assessment of the property in order to assess the market price while the main function of GIS based systems is to use economic and cadastral information in coordination with other elements such as: physical factors, social factors, taxation, infrastructure, pollution and noise, economic factors and legal factors in order to facilitate a good assessment of the value of an industrial property. The paper analyzed both the economic factors: based on the International Valuation Standards and also the locational factors of a property valuation and identified the common factors which are used in both methods and which can provide excellent solutions in property valuation

Urban areas have developed organically over time, driven by the economic success of cities. However, this development has usually been accompanied by the side effects of urbanization, such as increased traffic and its associated problems: traffic congestion, increased accident rates and pollution. As urban populations grow and expand, the importance of GIS lies in its ability to collect a large amount of geospatial data, including human-generated data. This data is necessary to understand the complexity of the city, set priorities, solve complicated planning problems and perform a variety of spatial analysis, which shows not only the feasibility but also the consistency of the proposed infrastructure with the requirements of a sustainable city. In this paper, we demonstrate the benefits of integrating real-time traffic data with GIS technology and remote sensing data for analyzing the impact of infrastructure works and COVID-19 on traffic in Oradea, Romania. The case study was focused on the historical center of Oradea and was based on remote sensing data collected before, during, and after traffic restrictions. The study also shows the need for using GIS and crowdsourcing-based applications in traffic analysis and planning.

The Importance-Performance Analysis is a widely used diagnostic tool in many fields of economic activity, such as: transport, health, construction, public food industry, finance, banking, sustainable activities, etc. Despite its use for over 40 years in many economic and social fields, this tool has some important drawbacks that affect the accuracy of managerial decisions. Over time, many variants for improving the standard Importance-performance matrix have been proposed. The aim of our research is to propose a method to solve one of the biggest problems of the standard Importance-Performance Analysis, respectively a method of boosting confidence in the positioning of attributes in the matrix. We use a mathematical method, inspired by classification theory tools, to apply the nine categories division of attributes in the importance-performance plane. Moreover, we introduce a level of confidence in a nine categories Importance-performance matrix, which helps the practitioners to prioritize a decision on attributes, according to the resources, managerial plan, competition, etc. We test and discuss the effectiveness of the new method on two studies: on the green practices in educational restaurant operations and on the financial performance evaluation.