In order to improve the durability of road structures, this study investigated the influence of temperatures, vehicle speeds, and axle configurations on pavement deflections with the PLAXIS 3D, a three-dimensional finite element modeling specifically developed for analyzing geotechnical engineering projects. A total of 32 models were developed, considering the temperatures of 4°C, 10°C, 20°C, and 30°C, when combined with the moving load velocities of 60, 80, 100, and 120 km/h. The effects of uneven distributions of axle loads were examined to capture the realistic condition of traffic loading. The results indicated that when the axle loads on both wheels were identical, the maximum pavement settlement occurred at the midpoint between them. Under unequal axle loading, the maximum settlement shifted to the wheel carrying the heavier load. This study revealed that a rising temperature reduced the strength of pavement materials, thus leading to a greater deflection. Nevertheless, higher vehicle speeds reduced pavement deflections due to decreased load–pavement interaction time. The findings highlighted the coupled effects of thermal conditions, traffic speeds, and load distributions on pavement performance, thus providing useful insights for the improved design and maintenance of sustainable road structures.

Vehicles comprise several critical systems, including the braking, steering, transmission, and suspension systems, which operate in concert to ensure safe and efficient movement. Research has established that vehicle malfunctions, particularly in the braking system, contribute significantly to road accidents, with technical failures accounting for approximately 15% of crashes and brake system failures responsible for 17.4% of these incidents. In light of this, an investigation was conducted to identify the factors that influence the braking coefficient and the variability of braking force in vehicle service brakes. A total of 1,018 vehicles were involved in the study, with results indicating that variables such as vehicle production year, category, place of registration, engine power and displacement, gross and curb weight, and payload significantly affect the braking coefficient. Furthermore, the analysis revealed that factors such as vehicle production year, category, registration location, gross and curb weight, and payload are prominent in determining the braking force variability. Neural network analysis was employed to further assess these influential factors, highlighting that the year of manufacture, place of registration, and vehicle payload are particularly influential in predicting both compliance with minimum braking coefficient requirements and variations in braking force. The findings underscore the importance of these factors in the development of more precise models for vehicle brake performance, with potential implications for safety standards and regulatory frameworks.

The rapid expansion of Internet of Things (IoT) systems and networks has led to increased challenges regarding security and system reliability. Anomaly detection has become a critical task for identifying system flaws, cyberattacks, and failures in IoT environments. This study proposes a hybrid deep learning (DL) approach combining Autoencoders (AE) and Long Short-Term Memory (LSTM) networks to detect anomalies in real-time within IoT networks. In this model, normal data trends were learned in an unsupervised manner using an AE, while temporal dependencies in time-series data were captured through the use of an LSTM network. Experiments conducted on publicly available IoT datasets, namely the Kaggle IoT Network Traffic Dataset and the Numenta Anomaly Benchmark (NAB) dataset, demonstrate that the proposed hybrid model outperforms conventional machine learning (ML) algorithms, such as Support Vector Machine (SVM) and Random Forest (RF), in terms of accuracy, precision, recall, and F1-score. The hybrid model achieved a recall of 96.2%, a precision of 95.8%, and an accuracy of 97.5%, with negligible false negatives and false positives. Furthermore, the model is capable of handling real-time data with a latency of just 75 milliseconds, making it suitable for large-scale IoT applications. The performance evaluation, which utilized a diverse set of anomaly scenarios, highlighted the robustness and scalability of the proposed model. The Kaggle IoT Network Traffic Dataset, consisting of approximately 630,000 records across six months and 115 features, along with the NAB dataset, which includes around 365,000 sensor readings and 55 features, provided comprehensive data for evaluating the model’s effectiveness in real-world conditions. These findings suggest that the hybrid DL framework offers a robust, scalable, and efficient solution for anomaly detection in IoT networks, contributing to enhanced system security and dependability.

Bengaluru, a city renowned for its rapid urbanization and booming population, faces severe traffic congestion that threatens road safety, increases environmental pollution, and disrupts the daily lives of its residents. The persistent delays at traffic lights and extended commute times underscore the urgent need for effective solutions. In response to these challenges, this study focuses on employing advanced machine learning techniques, specifically, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector regressions (SVRs) to analyze and predict traffic congestion patterns within the city. By leveraging the strengths of CNNs, the system is designed not only to provide accurate congestion detection across multiple locations but also to offer optimal routing recommendations to road users, thereby potentially easing traffic flows. To comprehensively evaluate the proposed approach, its performance is benchmarked against LSTM and SVR models using key performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the R² coefficient. These metrics ensure a robust assessment of predictive accuracy and model effectiveness.

The increasing desire for people to own personal cars, combined with their reluctance to use public transportation, has led to traffic jams and delays in emergency vehicle arrivals. Traffic lights in densely populated cities pose a significant challenge because they rely on fixed or variable timings, yet are not particularly effective. As a result, they can worsen congestion or cause traffic jams instead of alleviating it. For example, a city like Baghdad faces severe traffic congestion, requiring intervention from traffic police. Additionally, there is no specific system in place for emergency vehicle passage, and public transportation remains ineffective, as people are hesitant to use buses due to longer congestion times and the difficulty in navigating, which is exacerbated by their larger size compared to private small cars. Unlike previous YOLO-based systems, our system integrates emergency vehicle and public transport buses prioritization. It adjusts timing based on vehicle type, number, and estimated speed, showing a 31.11% improvement in flow efficiency and reducing queue delays by 21.64% compared to fixed-time signal systems. The improved algorithm can recognize all four vehicle classes (fire trucks, ambulances, public transport buses, and cars) with an accuracy of 85-99%, depending on vehicle density and complex lighting conditions.

infrastructure and trade connectivity across Asia. This study assesses the impact of BRI-funded transport infrastructure projects on agricultural trade efficiency in Southeast Asia, focusing on key projects such as the China-Laos Railway and Malaysia’s East Coast Rail Link (ECRL). The research employs a mixed-methods approach combining trade flow analysis, policy document review, and semi-structured stakeholder interviews. The findings reveal that transport costs for agricultural products decrease by up to 50%, while transit times are halved, particularly benefiting perishable goods such as fruits and vegetables. Export volumes of staples such as rice and cassava increase substantially, with durian exports to China reaching USD 3 billion annually. Despite these achievements, challenges remain, including limited access for smallholder farmers, insufficient rural infrastructure, and logistical bottlenecks in cold-chain systems. By integrating recent data and insights, this study underscores the need for targeted policies, such as harmonised trade regulations and investments in rural connectivity, to maximise the equitable and sustainable benefits of BRI infrastructure for agricultural trade.

The study aims to evaluate sustainable traffic management strategies for congested intersections in medium-sized Iraqi cities, with a focus on Al-Sa’a Intersection and Al-Jari Street in Hit City. These nodes face severe traffic congestion, delays, and infrastructure limitations that compromise urban mobility and sustainability. A multi-criteria evaluation (MCE) framework was employed to analyze three categories of interventions—engineering, planning, and administrative—based on five weighted criteria: traffic efficiency (40%), delay reduction (25%), cost (20%), environmental impact (10%), and social acceptance (5%). The methodology combined field data collection (traffic counts, travel time, and delays), GIS-based spatial analysis, and stakeholder consultation to prioritize solutions and evaluate performance. The findings indicated that all proposed solutions improved traffic performance, but varied in scope and impact. Engineering solutions, such as street widening and grade separation, reduced congestion by up to 40%. Planning measures, including public transport enhancement and alternative routes, scored the highest (8.2/10) due to their long-term sustainability and balanced environmental impact. Administrative actions—optimized signal timing and truck regulation—offered low-cost, short-term improvements. The study demonstrates the value of integrated, GIS-supported, multi-criteria approaches in diagnosing and addressing urban traffic challenges in secondary cities. A phased implementation strategy is recommended: initiate with administrative measures, transition to planning-based interventions, and apply engineering upgrades where necessary. The framework can support future transport planning in similar urban contexts across Iraq.

Global urbanisation is evident in Sub-Saharan Africa, especially Nigeria, where the population has steadily increased by 3.2% annually. This increment necessitates the adoption of sustainable public transportation, with rail transport leading the advancement. However, train terminals are fraught with complex and poorly implemented approaches to pedestrian circulation. This study evaluated the implementation of pedestrian circulation strategies within three existing train terminals in Lagos, Nigeria, aimed at determining their influence on optimal user experience. The research method employed in this study is a mixed-method approach, which entailed the distribution of survey questionnaires to 60 respondents. Thereafter, descriptive statistics were thoroughly carried out using the IBM Statistical Package for Social Sciences (SPSS) version 27. The results show that the pedestrian circulation strategy that influenced user experience the most within the selected train terminals was the connection of corridors and lobbies with other facilities. Therefore, it is recommended that horizontal pedestrian circulation strategies should be appropriately spatially planned and dimensioned to accommodate high pedestrian traffic scenarios within train terminals.

The aviation industry is experiencing a rapid digital transformation driven by globalization, technological advancements, and evolving customer expectations. Among these technologies, AI-based chatbots have emerged as a powerful tool to streamline operations, enhance customer service, and support internal business functions. However, their adoption in air ticket reservation services is still in its early stages. This study aims to provide innovative insights into understanding the factors that determine the adoption of AI-based chatbots for air ticket reservations from the organization’s perspective. The study introduces two new constructs, diversity and sensibility, and conceptually integrates the “Technology Organization Environment” theory and the “Diffusion of Innovation” theory. Data from 154 respondents were modeled using PLS-SEM, suitable for models with many variables and small sample sizes. The finding reveals that the organization's technical capability is a key factor influencing the adoption. Diversity, referring to the chatbot’s multifunctionality, promotes broader acceptance. Moreover, the impact of sensibility on adoption intention posits that a user-friendly design of the chatbot that enhances the “look” and provides a sense of human touch significantly increases the adoption intention. The relative advantage of AI-based chatbots on adoption illustrates that among all other ticket reservation channels, they prove to be the most efficient and profitable. Also, the complexity and government involvement were identified as relevant predictors of adoption. This study provides valuable insights for organizations and stakeholders and offers both theoretical and practical implications. The study concludes with limitations and proposes directions for future research.

Advances in wireless communication and sensor technologies have enabled vehicle-to-vehicle (V2V) systems that enhance road safety and traffic efficiency. The objective of this study is to develop and evaluate a multi-agent V2V communication framework that enables cooperative driving, allowing autonomous vehicles to make real-time, informed decisions in complex traffic scenarios. The proposed system is implemented using the JADE multi-agent platform and incorporates reinforcement learning and cooperative decision-making strategies. Each vehicle is represented by a Generic Car Agent (GCA) with integrated sub-agents responsible for driver modeling, information integration, knowledge management, and active interface functions. Remote Car Agents (RCA) and Traffic Control Agents (TCA) facilitate communication across vehicles and traffic networks, enabling coordinated maneuvers such as lane changes and platooning. The framework is evaluated using real-world traffic data collected from urban and highway roads in Jordan, across five challenging driving scenarios. Simulation results show improved traffic flow, reduced collision risk, and enhanced fuel efficiency. The system is cost-effective, leveraging existing onboard sensors and standard wireless technologies, demonstrating practical potential for scalable deployment in intelligent transportation systems.