Nisrine Makhoul

Smart city initiatives highlight the vital role of Intelligent Transportation Systems (ITS), which remain underexplored with limited AI-driven solutions integration in real-time urban traffic management across African cities. ITS is crucial to enhance urban mobility efficiency and sustainability to address growing mobility challenges in the era of swift African urbanization. This paper proposes an AI-driven predictive model for the Travel Time Index (TTI), a key metric quantifying urban traffic congestion and mobility performance. Using spatio-temporal analysis, neural networks, and advanced machine learning algorithms, the model processes real-time, multimodal traffic data, capturing congestion patterns, TTI fluctuations, and complex urban travel dynamics, focusing on Casablanca, Morocco, as a smart city case study. Five predictive modeling approaches were carefully selected and rigorously evaluated: Multivariate Linear Regression (MLR), Random Forest (RF), Gradient Boosting, Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Their performance was assessed using standard evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). All models achieved high accuracy, with Random Forest ranking highest (MAE = 0.315, R² = 0.985). Beyond prediction, the methodology incorporates feature importance analysis and hyperparameter tuning via GridSearchCV to improve operational performance and practical applicability across evolving traffic ecosystems. Hyperparameter optimization further enhanced Random Forest’s accuracy (MAE = 0.220, R² = 0.988). The findings demonstrate improved travel time estimation and congestion management capabilities, offering a scalable, adaptable framework to guide data-driven mobility strategies in diverse urban settings and provide actionable insights for urban planners, policymakers, and mobility stakeholders.

The rapid data growth in smart building environments requires advanced tools to integrate, interpret, and utilize this information effectively. Smart buildings generate vast and heterogeneous data streams, including sensor readings, occupancy metrics, and environmental conditions, which are critical for optimizing energy efficiency, enhancing occupant comfort, and enabling predictive maintenance. However, the lack of a structured approach to automatically connect and contextualize these data sources limits the insights that can be derived. To address these challenges, this paper presents a framework for assessing data in a knowledge graph by automatically retrieving entities and establishing relationships from diverse data sources, incorporating metadata standards and time-series data relevant to smart buildings. A standard ontology from the domain is used to drive the experiment, enabling the automatic construction of a semantic graph-based model for a real-world smart building environment. The framework's objective is to ensure information is comprehensible as a preliminary step to intelligent decision-making in data-driven smart buildings, enabling applications like fault detection, performance measurement, and energy auditing. The proposed approach explores the potential of large language models (LLMs) to automate data integration, reducing reliance on experts. This paper addresses existing literature gaps on metadata mapping and lays the groundwork for future advancements in digital twin technologies for smart building applications.

Geological and topographic site effects lead to variations in the spatial distribution of ground motion during large earthquakes. Despite the impact of such phenomena, they remain poorly understood. There is a lack of joint studies of numerical predictions and experimental observations on the geomorphological site effects. Therefore, a comparison between well-constrained models and experimental field observations is needed. Byblos is a seismic region in Lebanon surrounded by faults that historically generated destructive earthquakes. Its geological and geomorphological settings are interestingly characterized by fractured rocks and anthropic deposits altering seismic ground motions. Field surveys in Byblos gathered ambient vibration recordings and surface waves. It identified multiple resonant frequency peaks, suggesting impedance contrasts and lateral variations in subsurface stiffness, using Horizontal-to-Vertical Spectral Ratio (HVSR) and directivity. It also revealed soft, shallow layers with low velocities, indicating potential resonance during earthquakes, using Multichannel Analysis of Surface Waves (MASW) and 2D seismic arrays. Thus, our study on Byblos is a first step for seismic microzoning of the area that evaluated its heterogeneous subsoil, soft surface layers, and anthropic deposits. Finally, combining geophysical data and field measurements with a numerical model allowed a better understanding of Byblos seismic hazards and enhanced its resilience and sustainability.

Life cycle assessment (LCA) is a methodology used to analyze the environmental impacts of a product. Initially, it was applied to buildings only, but recently, it has also been applied to entire neighborhood. This expansion from individual buildings to the neighborhood scale requires additional inputs, such as the impacts of public spaces, transportation, public network systems, and roads. Additionally, conducting an LCA for a neighborhood requires software tools to simulate the neighborhood and databases to store the environmental impacts of all the components integrated into the neighborhood. This paper presents an LCA case study of a neighborhood in Dijon, France. The study aims to analyze the thermal impacts of buildings and the neighborhood with single-, double-, and triple-pane windows. The second part of the study involves two LCA studies using the 1996 version of ecoinvent. The first study is a continuation of the thermal simulation analysis, while the second study compares concrete, masonry, and timber-based construction designs for the neighborhoods. The results show that the heating demands inside the buildings are substantially reduced when transitioning from single- to triple-pane, while the cooling demands show the opposite effect. Furthermore, doubling the width of double-pane windows resulted in less profound environmental damage for the construction, use, and demolition phases compared to single-pane windows, with only the renovation phase being more damaging. Additionally, the comparison between different construction scenarios shows that the timber-based variant is slightly advantageous from an environmental point of view compared to the concrete and masonry designs.