The current development of multimodal transport networks focuses on the realization of intelligent transport systems (ITS) to manage the prediction of traffic congestion and urban mobility of vehicles and passengers so that alternative routes can be recommended for transport, especially the use of public passenger transport, to achieve sustainable transport. In the article, we propose an algorithm and a methodology for solving multidimensional traffic congestion objectives, especially for intersections, based on combining machine learning with the templates method—the golden template algorithm with the multiagent game theory. Intersections are modeled as independent players who had to reach an agreement using Nash negotiation. The obtained results showed that the Nash negotiation with multiagents and the golden template modeling have superior results to the model predictive control (MPC) algorithm, improving travel time, the length of traffic queues, the efficiency of travel flows in an unknown and dynamic environment, and the coordination of the agents’ actions and decision making. The proposed algorithm can be used in planning public passenger transport on alternative routes and in ITS management decision making.

The surrounding reality can be analyzed due to the interaction of complex nonlinear dynamic systems. The article’s main objective is to develop and analyze the models that best describe the efficient behavior of the Romanian capital market that generated the analyzed time series. The empirical analysis carried out in this paper does not aim to classify the Romanian market capital as efficient or ineffective but rather to identify the degree of deviation from efficiency relative to other markets, namely, an analysis of the dynamics of the degree of deviation over time. To describe the distribution of returns, we focused on the family of generalized hyperbolic distributions, which have statistical properties similar to financial returns. The presence of wide tails in the distributions (of extreme values) suggests using statistical tests and measures to detect dependencies, which take this behavior into account. Statistical methods and efficiency indicators are used, such as the Hurst exponent, Taken’s theorem, and the fractal dimension, which facilitate the detection of the main types of dependencies that could be present in the return series; measures that are robust to the heteroscedastic behavior of the returns. These statistical measures are applied to the entire period and sliding windows.

This study employs sample data of Chinese A-share listed companies from 2008 to 2023, takes investment efficiency as a core indicator to measure corporate sustainable development, and adopts the difference-in-differences (DID) model to examine the impact of free trade zone policies on enterprise sustainable development. The research results show that the establishment of FTZs has significantly improved the level of enterprise sustainable development, and this conclusion has passed a series of robustness tests. In the mechanism analysis, the main paths of action include alleviating financing constraints, optimizing internal control, and reducing transaction costs. The heterogeneity analysis finds that there are different regional effects in FTZs, and the policy effects are more obvious in the eastern, western, provincial capital, sub-provincial, and non-coastal areas. This study deepens the understanding of the sustainable impact of FTZs on enterprises, presents how the interaction between institutions and the market promotes the efficiency improvement of different regions, and also provides key suggestions for enterprises to make wise investment decisions by using the policies of FTZs and find a sustainable development path.