Rapid urban growth in Middle Eastern cities has intensified congestion-related challenges, yet traffic data-based decision making remains limited. This study leverages crowd-sourced travel time data from the Google Maps API to evaluate temporal and spatial patterns of congestion across multiple strategic routes in Jeddah, Saudi Arabia, a coastal metropolis with a complex road network characterized by narrow, high-traffic corridors and limited public transit. A real-time Congestion Index quantifies traffic flow, incorporating free-flow speed benchmarking, dynamic profiling, and temporal classification to pinpoint congestion hotspots. The analysis identifies consistent peak congestion windows and route-specific delays that are critical for travel behavior modeling. In addition to congestion monitoring, the framework contributes to urban sustainability by supporting reductions in traffic-related emissions, enhancing mobility equity, and improving economic efficiency through data-driven transport management. To our knowledge, this is the first study to systematically use the validated, real-time Google Maps API to quantify route-specific congestion in a Middle Eastern urban context. The approach provides a scalable and replicable framework for evaluating urban mobility in other data-sparse cities, especially in contexts where traditional traffic sensors or GPS tracking are unavailable. The findings support evidence-based transport policy and demonstrate the utility of publicly accessible traffic data for smart city integration, real-time traffic monitoring, and assisting transport authorities in enhancing urban mobility.

In next-generation smart city infrastructure, monitoring emotional stability in real time is a crucial component. This paper introduces a powerful Edge-AI monitoring system suitable for all children, with a specific focus on those displaying special behavioral patterns or sensory sensitivities. The proposed model combines improved Frilled Lizard Optimization (FLO), the Histogram of Orientated Gradients (HOG), and the Teager–Kaiser Energy Operator (TKEO) to enhance the analysis of spontaneous behaviors. Then, the model is called FLHTKEO. The model demonstrates high accuracy in interpretating children’s facial expressions in real-time recorded datasets. To address the non-stationary noise and varying brightness typical of school bus interior, the TKEO is employed as a preprocessing tuning stage to amplify the immediate energy of features extracted via HOG. Additionally, the proposed model is implemented on a System-on-Chip (SoC) using a Raspberry Pi 4 to achieve real-time monitoring and generate automatic alarms when abnormal or uncertain behaviors are detected among children in the school vehicle. Real-time datasets were collected, simulating diverse school bus scenarios under varying lighting and behavioral conditions. The proposed model achieves a superior AUC-ROC ≅ 0.9613, proving a high degree of the precision recognition of children’s facial expressions. The system will help with some healthy measures during ongoing and future emergencies. Simulations and experimental results indicate that the proposed method is effective for the model, and automatic compliance monitoring systems are set to advance in the future. Finally, it is compared with the last piece of previous work.

Financial markets react continuously to corporate disclosures, yet stock prices may not adjust immediately to new information. One of the most widely studied anomalies associated with post-announcement return behavior is post-earnings announcement drift (PEAD), in which stock prices continue to move in the direction of earnings surprises after the announcement date. Although PEAD has been extensively examined in developed markets, limited research has investigated post-earnings announcement return behavior in emerging markets, particularly in the Saudi stock market. Existing studies largely rely on traditional statistical approaches or focus on developed-market datasets, leaving a gap in understanding how artificial intelligence (AI)-driven machine learning techniques can support financial risk analysis and data-driven financial econometrics by classifying post-earnings announcement return behavior across different firm sizes and post-announcement horizons. To address this gap, this study develops an AI-driven financial risk analytics framework for classifying operational PEAD-related post-announcement return observations using financial and technical indicators derived from companies listed on the Saudi Stock Exchange (Tadawul). The analysis utilizes five years of historical data (2020–2025) from 60 firms equally distributed across small-cap, mid-cap, and large-cap categories. Cumulative return is employed as the response variable and evaluated over three forward post-announcement horizons of 5, 21, and 30 trading days. Five supervised learning models, namely Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and CatBoost, are trained and evaluated using standard classification metrics. The results show that operational PEAD-related return classifications are observed across all market-capitalization categories, with the proportion of observations assigned to the high-return class ranging from 20.55% to 23.30%. These proportions arise from our study’s predefined upper-quartile return-based classification criterion and therefore should not be interpreted as estimates of the prevalence or persistence of classical PEAD. Classification performance varies across market segments and post-announcement horizons. Because the upper-quartile threshold produces an inherently imbalanced classification problem, accuracy results are interpreted alongside class-specific performance and F1 scores rather than in isolation. CatBoost achieved the highest reported accuracy of 77.8% at the 21-day horizon for large-cap firms, while also delivering the highest overall-market accuracy of 76.9% at the 30-day horizon. These results indicate that the selected financial and technical indicators contain information useful for classifying the predefined high-return post-announcement observations, although the reported accuracy levels should not be interpreted independently of the majority-class baseline. The proposed framework contributes to AI-enabled financial econometrics by providing a data-driven approach for classifying post-announcement return behavior and supporting market risk assessment, investor decision-making, earnings-based trading strategies, and market monitoring in emerging financial markets.