Search Results (554)

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article

Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle emissions, mitigate urban heat island effects, and enhance the resilience of green infrastructure in peri-urban contexts. Using Deir Ghbar, a rapidly developing marginal area on Amman’s western edge, as a case study, we combine objective walkability metrics (street connectivity and residential and retail density) with GIS-based spatial regression analysis to examine relationships with residents’ sense of community. Employing a quantitative, correlational research design, we assess walkability using a composite objective walkability index, calculated from the land-use mix, street connectivity, retail density, and residential density. Our results reveal that higher residential density and improved street connectivity significantly strengthen social cohesion, whereas low-density zones reinforce spatial and socioeconomic disparities. Furthermore, the findings highlight the potential of targeted green infrastructure interventions, such as continuous street tree canopies and permeable pavements, to enhance pedestrian comfort and urban ecological functions. By visualizing spatial patterns and correlating built-environment attributes with community outcomes, this research provides actionable insights for policymakers and urban planners. These strategies contribute directly to several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by fostering more inclusive, connected, and climate-resilient neighborhoods. Deir Ghbar emerges as a model for scalable, GIS-driven spatial planning in rural and marginal peri-urban areas throughout Jordan and similar regions facing accelerated urban transitions. By correlating walkability metrics with community outcomes, this study operationalizes SDGs 11 and 13, offering a replicable framework for climate-resilient urban planning in arid regions. Full article

This study contributes to the broader sustainability discourse by evaluating public knowledge, awareness, and practices regarding air pollution among residents of Hanoi, Vietnam, focusing on its causes, health impacts, and mitigation strategies. A cross-sectional survey was conducted with 521 individuals in suburbs around Hanoi. A multistage sampling technique, combining cluster and simple random sampling, was used for participant recruitment. Three central and three suburban districts of Hanoi were randomly selected as clusters. One individual from each household was invited to participate and answer a structured survey, which assessed perceptions of air pollution, its human-induced causes, recognised health impacts, and individual and community-level mitigation behaviours. Nearly all participants (98.3%) were aware of air pollution, with 65.3% attributing it to human activities and 61.2% recognising specific air pollutants as primary contributors. The majority (93.9%) acknowledged health impacts, citing respiratory infections (55.1%) and sinus issues (51.2%) as prevalent concerns. Vulnerable groups, such as children under 5 (82.3%) and adults over 65 years old (77.4%), were identified as disproportionately affected. Social media (68.9%) and television (58.3%) were the dominant sources of information. Despite a recognition of air pollution’s importance (98.5%), there was limited engagement in systemic sustainability actions, such as supporting renewable energy initiatives. Most participants (84.3%) reported personal mitigation efforts, including energy-saving practices (35.5%) and walking instead of driving a car or bike (35.3%). While awareness of air pollution and its health impacts is high among Hanoi residents, proactive engagement in systemic solutions remains limited. Policymakers should prioritise community-based programs, public–private partnerships, sustainability education, and culturally tailored policy interventions to bridge gaps between awareness and action. Tailored interventions addressing demographic and cultural factors are essential to fostering socio-environmental sustainability in rapidly urbanising contexts. Full article

Many academic introductions suffer from inconsistencies and a lack of comprehensive structure, often failing to effectively outline the core elements of the research. This not only impacts the clarity and readability of the article but also hinders the communication of its significance and objectives to the intended audience. This study aims to automate the CaRS (Creating a Research Space) model using machine learning and natural language processing techniques. We conducted a series of experiments using a custom-developed corpus of 50 biology research article introductions, annotated with rhetorical moves and steps. The dataset was used to evaluate the performance of four classification algorithms: Prototypical Network (PN), Support Vector Machines (SVM), Naïve Bayes (NB), and Random Forest (RF); in combination with six embedding models: Word2Vec, GloVe, BERT, GPT-2, Llama-3.2-3B, and TEv3-small. Multiple experiments were carried out to assess performance at both the move and step levels using 5-fold cross-validation. Evaluation metrics included accuracy and weighted F1-score, with comprehensive results provided. Results show that the SVM classifier, when paired with Llama-3.2-3B embeddings, consistently achieved the highest performance across multiple tasks when trained on preprocessed dataset, with 79% accuracy and weighted F1-score on rhetorical moves and strong results on M2 steps (75% accuracy and weighted F1-score). While other combinations showed promise, particularly NB and RF with newer embeddings, none matched the consistency of the SVM–Llama pairing. Compared to existing benchmarks, our model achieves similar or better performance; however, direct comparison is limited due to differences in datasets and experimental setups. Despite the unavailability of the benchmark dataset, our findings indicate that SVM is an effective choice for rhetorical classification, even in few-shot learning scenarios. Full article

Reverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires either expensive tools suitable for the respective vehicles or experienced engineers who have developed individual approaches to identify specific signals. Access to the internal data enables reading the vehicle’s status, and thus, reducing the need for additional test equipment. This results in vehicles closer to their production status and does not require manipulating the vehicle under study, which prevents affecting future test results. The main focus of this approach is to reduce the cost of such analysis and design a more efficient benchmarking process. In this work, we present a methodology that identifies signals without physically manipulating the vehicle. Our equipment is connected to the vehicle via the On-Board Diagnostics (OBD)-II port and uses the Unified Diagnostics Service (UDS) protocol to communicate with the vehicle. We access, capture, and analyze the vehicle’s signals for future analysis. This is a holistic approach, which, in addition to decoding the signals, also grants access to the vehicle’s data, which allows researchers to utilize state-of-the-art methodologies to analyze their vehicles under study by greatly reducing necessary experience, time, and cost. Full article

This study proposes an adaptive car-following model based on the unscented Kalman filter algorithm to enable coordinated speed control in vehicle platoons and to address key limitations present in conventional car-following models. Traditional models generally assume a fixed maximum speed within the optimal velocity function, which constrains effective platoon speed regulation across road segments with varying speed limits and lacks adaptability to dynamic scenarios such as changes in the platoon leader’s speed or substitution of the lead vehicle. The proposed adaptive model utilizes state estimation based on the unscented Kalman filter to dynamically identify each vehicle’s maximum achievable speed and to adjust inter-vehicle constraints, thereby enforcing a unified speed reference across the platoon. By estimating these maximum speeds and transmitting them to individual follower vehicles via vehicle-to-vehicle communication, the model promotes smooth acceleration and deceleration behavior, reduces headway variability, and mitigates shockwave propagation within the platoon. Simulation studies—covering both single-leader acceleration and intermittent acceleration scenarios—demonstrate that, compared with conventional car-following models, the adaptive model based on the unscented Kalman filter achieves superior speed synchronization, improved headway stability, and smoother acceleration transitions. These enhancements lead to substantial improvements in traffic flow efficiency and string stability. The proposed approach offers a practical solution for coordinated platoon speed control in intelligent transportation systems, with promising application prospects for real-world implementation. Full article

The incorporation of Internet of Things (IoT) applications into 5G networks marks a significant step towards realizing the full potential of connected systems. 5G networks, with their ultra-low latency, high data speeds, and huge interconnection, provide a perfect foundation for IoT ecosystems to thrive. This connectivity offers a diverse set of applications, including smart cities, self-driving cars, industrial automation, healthcare monitoring, and agricultural solutions. IoT devices can improve their reliability, real-time communication, and scalability by exploiting 5G’s advanced capabilities such as network slicing, edge computing, and enhanced mobile broadband. Furthermore, the convergence of IoT with 5G fosters interoperability, allowing for smooth communication across diverse devices and networks. This study examines the fundamental technical applications, obstacles, and future perspectives for integrating IoT applications with 5G networks, emphasizing the potential benefits while also addressing essential concerns such as security, energy efficiency, and network management. The results of this review and analysis will act as a valuable resource for researchers, industry experts, and policymakers involved in the progression of 5G technologies and their incorporation with IT solutions. Full article

With the rapid development of intelligent transportation systems, voice assistants are increasingly integrated into driving environments, providing an effective means to mitigate the risks of fatigued driving. This study explored drivers’ interaction preferences with voice assistants under different fatigue states and proposed a fatigue-state-based dialogue-awakening mechanism. Using Grounded Theory and the Stimulus–Organism–Response (SOR) framework, in-depth interviews were conducted with 25 drivers from diverse occupational backgrounds. To validate the qualitative findings, a driving simulation experiment was carried out to examine the effects of different voice interaction styles on driver fatigue arousal across various fatigue levels. Results indicated that heavily fatigued drivers preferred highly stimulating and interactive voice communication; mildly fatigued drivers tended toward gentle and socially supportive dialogue; while drivers in a non-fatigued state preferred minimal voice interference, activating voice assistance only when necessary. Significant occupational differences were also observed: long-haul truck drivers emphasized practicality and safety in voice assistants, taxi drivers favored voice interactions combining navigation and social content, and private car owners preferred personalized and emotional support. This study enriches the theoretical understanding of fatigue-sensitive voice interactions and provides practical guidance for the adaptive design of intelligent voice assistants, promoting their application in driving safety. Full article

Climate change is one of our most critical challenges, requiring urgent and comprehensive action across all levels of society. Individual actions and their roles in mitigating and adapting to climate change remain underexplored, despite global efforts. Under this context, this study was conducted to evaluate the ecological footprint of individuals for climate change mitigation. A structured online survey was designed and distributed through email lists, social media platforms, and community organisations to over 200 potential participants in the northwest of the UK. Due to the anonymous nature of the survey, only 83 individuals from diverse demographics completed the questionnaire. A carbon footprint calculator using conversion factors has been employed, based on energy consumption, travel, and material goods use. Participants are categorised into four groups based on their annual CO₂ emissions, ranging from less than 2 tonnes to over 10 tonnes. Personalised recommendations provided by the calculator focus on practical strategies, including adopting renewable energy, minimising unnecessary consumption, and opting for sustainable transportation. Results showed that only 5.5% of participants who employed advanced technologies and smart home technologies, 1.8% were implementing water-saving practices and 65.4% preferred to use their own car over other modes of transportation. In addition, the study found that 67.3% of participants had no or only a very limited knowledge of renewable energy technologies, indicating a need for education and awareness campaigns. The findings also highlight the importance of addressing demographic differences in ecological footprints, as these variations can provide insights into tailored policy interventions. Overall, despite the study’s limited sample size, this research contributes to the growing body of evidence on the importance of individual action in combating climate change and provides actionable insights for policymakers and educators aiming to foster a more sustainable lifestyle. Future studies with larger samples are recommended to validate and expand upon these findings. Full article

Decades of migration and refugee influxes have driven Amman’s rapid urban growth, yet newer neighborhoods increasingly grapple with fragmented social cohesion. This study examines whether walkable design can strengthen community bonds, focusing on Deir Ghbar, a car-centric district in West Amman. Using GIS and mixed-methods analysis, we assess how walkability metrics (residential density, street connectivity, land-use mix, and retail density) correlate with sense of community. The results reveal that street connectivity and residential density enhance social cohesion, while land-use mix exhibits no significant effect. High-density, compact neighborhoods foster neighborly interactions, but major roads disrupt these connections. A critical mismatch emerges between quantitative land-use metrics and resident experiences, highlighting the need to integrate spatial data with community insights. Amman’s zoning policies, particularly the stark contrast between affluent low-density Zones A/B and underserved high-density Zones C/D, perpetuate socio-spatial segregation—a central critique of this study. We urge the Greater Amman Municipality’s 2025 Master Plan to prioritize mixed-density zoning, pedestrian retrofits (e.g., traffic calming and sidewalk upgrades), and equitable access to amenities. This study provides a replicable GIS and survey-based framework to address urban socio-spatial divides, aligning with SDG 11 for inclusive cities. It advocates for mixed-density zoning and pedestrian-first interventions in Amman’s Master Plan. By integrating a GIS with social surveys, this study offers a replicable model for addressing socio-spatial divides in cities facing displacement and inequality. Full article

The automotive industry is fully shifting towards autonomous connected vehicles. By advancing vehicles’ intelligence and connectivity, the industry has enabled innovative functions such as advanced driver assistance systems (ADAS) in the direction of driverless cars. Such functions are often referred to as cyber-physical features, since almost all of them require collecting data from the physical environment to make automotive operation decisions and properly actuate in the physical world. However, increased functionalities result in increased complexity, which causes serious security vulnerabilities that are typically a result of mushrooming functionality and hence complexity. In a world where we keep seeing traditional mechanical systems shifting to x-by-wire solutions, the number of connected sensors, processing systems, and communication buses inside the car exponentially increases, raising several safety and security concerns. Because there is no safety without security, car manufacturers start struggling in making lightweight sensor and processing systems while keeping the security aspects a major priority. This article surveys the current technological challenges in securing autonomous vehicles and contributes a cross-layer analysis bridging hardware security primitives, real-world side-channel threats, and redundancy-based fault tolerance in automotive electronic control units (ECUs). It combines architectural insights with an evaluation of commercial support for TrustZone, trusted platform modules (TPMs), and lockstep platforms, offering both academic and industry audiences a grounded perspective on gaps in current hardware capabilities. Finally, it outlines future directions and presents a forward-looking vision for securing sensors and processing systems in the path toward fully safe and connected autonomous vehicles. Full article

A virtual model enables the study of reality in a virtual environment using a theoretical model, which is a digital image of a real model. The complexity of the virtual model must correspond to the reality of the evaluated system, becoming as complex as necessary and nevertheless as simple as possible, allowing for computer simulation results to be validated by experimental measurements. The virtual model of the autonomous vehicle was created using the CarMaker software package version 12.0, which was developed by the IPG Automotive company and is extensively used in both the international academic community and the automotive industry. The virtual model simulates the real-time operation of a vehicle’s elementary systems at the system level and provides an open platform for the development of virtual test scenarios in the application areas of autonomous vehicles, ADAS, Powertrain, and vehicle dynamics. This model included the following virtual sensors: slip angle sensor, inertial sensor, object sensor, free space sensor, traffic sign sensor, line sensor, road sensor, object-by-line sensor, camera sensor, global navigation sensor, radar sensor, lidar sensor, and ultrasonic sensor. Virtual sensors can be classified based on how they generate responses: sensors that operate on parameters derived from measurement characteristics, sensors that operate on developed modeling methods, and sensors that operate on applications. Full article

Vehicles are rapidly evolving into objects of intelligent interconnection. Vehicle-to-Vehicle (V2V) communications enable the interconnection between vehicles, while also leading to new electromagnetic exposure scenarios. This paper integrates a monopole array antenna into a shark-fin antenna on the car roof for V2V communications and evaluates the specific absorption rate (SAR) and temperature rise of a human body in a smart mobility communication scenario operating at 5.9 GHz. The V2V antenna is modeled and placed on a 3D vehicle model using COMSOL Multiphysics (v.6.2) to numerically estimate the SAR in the head and body regions of the human body model (adult male) inside the vehicle. Both the localized and whole-body 30 min average SAR are lower than the International Commission on Non-Ionizing Radiation Protection (ICNIRP) occupational restrictions for electromagnetic field exposure from 100 kHz to 6 GHz, being equal in the worst-case scenario to 0.981 W/kg (for the head), which is 9.81% of the ICNIRP limit (10 W/kg), and 0.008728 W/kg (for the whole-body average), which is 2.18% of the ICNIRP limit (0.4 W/kg). The 30 min average human core temperature rise is 0.055 °C, which is 5.5% of the ICNIRP limit. This indicates that, in typical automotive scenarios, the electromagnetic exposure from a monopole array antenna for V2V communications does not pose threat to the human body. This study provides knowledge related to emerging exposure scenarios in intelligent mobility communication, which is beneficial for evaluating possible health impacts and designing public health management policies. Full article

In this paper, we study the stability of the stochastically heterogeneous traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Taking the stochasticity of vehicle arrivals and behaviors into account, a general robust $H_{\infty }$ platoon controller is proposed to address the communication delay and unexpected disturbances such as prediction or perception errors on HDV motions. To simplify the problem complexity from a stochastically heterogeneous traffic flow to multiple long vehicle control problems, three types of sub-platoons are identified according to the CAV arrivals, and each sub-platoon can be treated as a long vehicle. The car-following behaviors of HDVs and CAVs are simulated using the optimal velocity model (OVM) and the cooperative adaptive cruise control (CACC) system, respectively. Later, the robust $H_{\infty }$ platoon controller is designed for a pair of a CAV long vehicle and an HDV long vehicle. The time-lagged system and the closed-loop system are formulated and the $H_{\infty }$ state feedback controller is designed. The robust stability and string stability of the heterogeneous platoon system are analyzed using the $H_{\infty }$ norm of the closed-loop transfer function and the time-lagged bounded real lemma, respectively. Simulation experiments are conducted considering various settings of platoon sizes, communication delays, disturbances, and CAV penetration rates. The results show that the proposed $H_{\infty }$ controller is robust and effective in stabilizing disturbances in the stochastically heterogeneous traffic flow and is scalable to arbitrary sub-platoons in various CAV penetration rates in the heterogeneous traffic flow of road vehicles. The advantages of the proposed method in stabilizing heterogeneous traffic flow are verified in comparison with a typical car-following model and the linear quadratic regulator. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) is characterized by social communication challenges and repetitive behaviors. Children with ASD often exhibit selective eating habits that may result in nutritional deficiencies and exacerbate developmental issues. While food frequency questionnaires (FFQs) are effective for dietary assessment, the links between food preferences, ASD severity, and developmental outcomes remain underexplored, particularly in Bulgaria. This study examines these relationships using validated tools. Methods: The present report constitutes a pilot, hypothesis-generating substudy of the broader NutriLect project. This substudy involved 49 children aged 2–12 years diagnosed with ASD. Dietary patterns were evaluated with a modified FFQ, while ASD severity and developmental profiles were assessed using the Childhood Autism Rating Scale, Second Edition (CARS-2) and the Developmental Profile 3 (DP-3). Results: Among 49 ASD children (mean age = 6.89 ± 2.15 years; 86% boys), 73.4% consumed grains/potatoes daily. Only 34.7% met combined fruit and vegetable recommendations. Only 36.7% met the recommendation for daily milk or other dairy product consumption. Fish was consumed at least twice weekly by only 22,4%. Furthermore, children with more severe autism were approximately 9.4 times more likely to consume grains daily (χ² = 14.319, p = 0.006). Logistic regression analyses indicated that higher cognitive scores were strongly associated with lower grain (OR ≈ 0.044) and other dairy products consumption (OR ≈ 0.337), yet with greater fish intake (OR ≈ 3.317). In contrast, better communication skills corresponded to increased milk consumption (OR ≈ 5.76), and higher physical development scores predicted more frequent egg consumption (OR ≈ 4.40). Conclusions: The pronounced preference for grain and meat products, which are frequently ultra-processed, and avoidance of nutrient-dense foods in children with severe ASD symptoms underscore the need for tailored dietary interventions. These interventions must address sensory sensitivities, nutritional inadequacies, and the risks that selective nutrition can have on the nutritional status and development of the children. Full article