An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike’s Information Criterion technique (AIC) has also shown that the proposed model has a higher performance.

Conventional traffic light control systems often exhibit rigid timing patterns, limited flexibility, and insufficient adaptability to changing traffic conditions. This paper specifically addresses the challenges of urban traffic management, addressing researchers and professionals in traffic engineering and intelligent transportation systems. To overcome these challenges, this paper presents an innovative traffic light control method, integrating a CNN-LSTM model for traffic state prediction, combined with a Proximal Policy Optimization (PPO) algorithm for traffic light control decision-making. The model is based on a representation of intersection states through key indicators (such as active green phase, congestion levels, congestion variations, and vehicle speeds) and employs deep reinforcement learning to optimize traffic light control strategies. The adopted method is compared with fixed traffic light control approaches and a reinforcement learning (Q-learning) approach in a simulated environment using SUMO. The simulations are based on various traffic scenarios and realistic parameters to maximize the validity of the results. The experimental results indicate that traffic efficiency is significantly improved by up to 92% (for the North-South scenario), while congestion indicators are decreased. These results illustrate the effectiveness of integrating spatiotemporal prediction and PPO-based control for dynamic and adaptive traffic management in urban networks.

One of the most difficult problems, in cluster analysis is the determination of the number of clusters in a data set. Solving this problem consists in detecting and finding the best number of clusters, which is an input parameter for the clustering problems. In this paper, we propose a new approach using the Maximum Stable Set Problem (MSSP) combined by Continuous Hopfield Network (CHN) to determine the number of clusters, which is a basic input parameter for K-Means method. By testing the theoretical results, the proposed approach was validated on a real application for the text mining. Some numerical examples and computational experiments assess the effectiveness of this approach as demonstrated in this paper.

Environmental challenges such as climate change have accelerated humanity's need for renewable alternative energy sources. For this reason, we propose in this paper a decision-making strategy that allows controlling the flows of energy into a micro-grid (MG) compound of solar energy, batteries, and diesel generator (DG), and connected to the distributed network (DN). Therefore, the power supply to the loads is obtained either from the energy produced by solar sources, from the batteries, from the DN, or from the DG when renewable energy (RE) and batteries are depleted. To make the final decision, we consider four parameters at the same time: the energy produced by solar energy, the requested load, the state of charge of batteries (SoC), and the purchase or sale price. Decision tree (DT) is used to build the energy management strategy to ensure the availability of power on demand by making logical decisions about charging batteries, discharging batteries, buying necessary energy from DN, selling excess energy to DN, and recovering necessary energy from DG. The suggested DT approach is applied to a real MG to minimize the cost-benefit balance, and the comparison analysis demonstrates good results when compared to related works.