Hasil untuk "Transportation and communications"

Leila Pirmohammadi Pourfard, Sharareh Kamfar, Boshra Yaghoobi et al.

Information has a crucial role in the management of Phenylketonuria (PKU). This study aimed to investigate information acquisition and avoidance behavior among parents of children with PKU and determine the associated factors. This cross-sectional survey was conducted on 144 parents of children with PKU at a teaching hospital in Tehran, Iran. Information acquisition behavior and information avoidance were evaluated using the questionnaire. Information-seeking and scanning about PKU were reported by 132 (91.7%) and 45 (31.3%) parents, respectively. The primary sources for seeking information about PKU were physicians (99/132, 75%), informative websites (77/132, 58.3%), scientific websites (21/132, 15.9%), and friends and family (20/132, 15.2%). There was a significant relationship between information-seeking behavior and parents' educational level (P=0.03), which remained significant in multivariable analysis after controlling for confounding variables (95% CI: 0.05-0.90, P=0.03). Also, parents' online information-seeking behavior was associated with their age (P=0.03) and educational degree (P=0.008). Thirty parents (20.9%) avoided PKU information at least occasionally. Physicians and the Internet were the primary sources of information among parents of children with PKU. Although PKU is a non-acute disease, information avoidance was reported by the parents. Considering that avoiding information can lead to misunderstanding and disrupt the treatment process, physicians must pay attention to this issue.

Ala Alobeidyeen

Abstract Bus bunching remains a critical challenge in urban transit systems, leading to service unreliability, increased passenger wait times, and operational inefficiencies. While existing solutions often address components of this problem in isolation—such as headway-based holding or transit signal priority—they lack a holistic approach that integrates real-time data with adaptive, passenger-centric optimization. This paper introduces the bus operational control strategy (BOCS), a novel integrated framework that synthesizes comprehensive vehicle-to-everything (V2X) communications (V2V, V2I, V2P) with advanced reinforcement learning (RL) to mitigate bunching through real-time adaptive multi-objective optimization. BOCS distinguishes itself from existing frameworks through three unique contributions: (1) a comprehensive V2X data layer that provides unprecedented situational awareness beyond conventional V2I-only systems, (2) a dynamically weighted multi-objective optimization function with adaptive normalization that balances headway adherence with passenger experience metrics in real time, and (3) a hybrid RL architecture using Soft Actor-Critic algorithm with continuous action spaces and physics-informed state representations. The framework was validated through high-fidelity microsimulation with fifty independent replications across two real-world bus routes (Routes 35 and 37) from Gainesville Regional Transit System and two additional simulated routes representing diverse operational contexts. Statistical analysis using paired t-tests and Cohen’s d effect size calculations confirmed significant improvements: BOCS achieved 65–71 per cent reduction in headway deviation (P < 0.001, d = 2.15), 29 per cent reduction in average waiting time (P < 0.001, d = 1.87), and 51 per cent reduction in overcrowding (P < 0.001, d = 1.92) compared to robust headway-based controllers, Q-learning approaches, and multi-agent deep deterministic policy gradient methods. Comprehensive sensitivity analysis demonstrated robustness to V2X communication degradation (maintaining >80 per cent performance at 75 per cent V2X reliability) and generalizability across diverse demand patterns. Ablation studies quantified the contribution of each V2X component, revealing that V2I provides the foundation (72 per cent performance), while V2V and V2P integration enhances performance to 92 per cent. Computational analysis confirmed real-time feasibility with average decision latency of 128 ms per control cycle, well within operational requirements. These results confirm that the integration of comprehensive connectivity, adaptive artificial intelligence, and human-centric design is fundamental to advancing resilient and efficient public transit systems.

Shihan Wang, Ying Ni, Chengsheng Miao et al.

Alessandro Martulli, Kristin Brandt, Florian Allroggen et al.

Yousef Sanjalawe, Salam Al-E'Mari, Salam Fraihat et al.

The rapid evolution of energy systems, coupled with the emergence of intelligent communication technologies, has necessitated a paradigm shift in the design and operation of modern Smart Grids. While traditional grids are limited in flexibility, scalability, and responsiveness, integrating Artificial Intelligence (AI) and Sixth Generation (6G) communication networks presents transformative opportunities for secure, autonomous, and sustainable grid infrastructures. However, this convergence also introduces new challenges, including system heterogeneity, latency constraints, cyber threats, and data privacy concerns, which are gaps that existing studies have only partially addressed. Motivated by these challenges, this paper presents a comprehensive survey that investigates the synergistic potential of AI and 6G in advancing the Smart Grid landscape. The main objectives include (i) a critical analysis of Smart Grid architectures and applications, (ii) an exploration of AI-driven enhancements in forecasting, optimization, and anomaly detection, (iii) an in-depth assessment of 6G capabilities tailored to grid requirements, and (iv) a synthesis of security and privacy mechanisms suitable for next-generation grids. The paper introduces a novel conceptual framework, SAFES-6G, that integrates edge intelligence, scalable AI, and explainable cybersecurity solutions to address latency, trust, and interoperability challenges. The findings highlight significant opportunities in edge-native intelligence, quantum-safe encryption, and federated learning for privacy-preserving analytics. Ultimately, this paper aims to guide researchers and practitioners toward building future-proof, resilient, and ethical energy systems that align with global sustainability and digital transformation goals.

Daniel Kajánek, Luboš Halimovič, Martina Jacková et al.

In this study was investigated the effect of a novel surface treatment technique, called laser ablation or laser surface cleaning (LSC), on the electrochemical properties of AZ80 magnesium alloys. Its effect on corrosion resistance was compared to conventional techniques represented by grinding and polishing. The corrosion stability of the produced surfaces was determined by potentiodynamic (PD) polarization tests and electrochemical impedance spectroscopy in 0.1M NaCl. The results showed that the LSC process produced a regular, uniform surface morphology. Although the LSC treatment led to the passivation of the AZ80 surface, the overall corrosion resistance was compromised compared to surfaces treated by grinding and polishing.

Tanayut Chaitongrat, Wuttipong Kusonkhum, Thamonwan Tharasombat et al.

Modern epidemiological research increasingly integrates machine learning and data-driven methods to enhance the prediction and mitigation of COVID-19 and other respiratory virus outbreaks. Herein, a long short-term memory (LSTM)-based classification model was developed to predict high-risk COVID-19 transmission zones on Thailand’s Mass Rapid Transit Purple Line platforms. Using sequential passenger flow data and temporal patterns, platform areas were classified into low- and high-risk areas based on key inputs including station, date, time, and crowd density. Hyperparameter optimization using RandomizedSearchCV yielded an optimal configuration of 64 LSTM units, a learning rate of 0.001, a batch size of 32, and 30 epochs. The model achieved 98% test accuracy, 98.22% cross-validation accuracy, and 99.11% peak validation accuracy. For high-risk detection, it obtained precision and recall of 0.95 and 0.96, respectively. The results highlight the robustness and real-time applicability of the approach in urban transit systems. The findings offer actionable insights for targeted interventions such as dynamic crowd management and optimized resource allocation, thereby reducing exposure risks and strengthening preparedness for future public health crises.

David Levinson, Isaac Mann

We introduce Access Surplus as a welfare measure that frames accessibility in a market-like form: the inverse cumulative cost to reach the next opportunity is the 'supply,' and the willingness to pay for one more choice is the 'demand.' The area where demand exceeds supply, up to a natural stop point, is Access Surplus . The metric avoids arbitrary cut-offs, is additive over residents, links clearly to project effects, and stays transparent when only origin--destination times and counts are available.

Roberto D. Rosario, Arjel Alvarez, Ronnel C. Quinto et al.

David Goez, Esra Aycan Beyazit, Luis A. Fletscher et al.

Traffic Classification (TC) systems are designed to identify the applications generating network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques, surpassing traditional methods in complex scenarios, including those with encrypted traffic. Notably, state-of-the-art DL-based TC systems have been developed for wireless networks using Physical Layer (L1) packets. This approach overcomes the common limitation in TC research that assumes traffic flows within a wired network under a single network management domain. Despite their benefits, DL-based TC systems often demand significant computational resources, typically available only in cloud environments. Consequently, deploying models at the edge is often infeasible due to their resource-intensive nature, given their original training and optimization for high-resource environments. The inherent challenge lies in adapting these systems for edge computing scenarios, including deployment at access points. In this paper, we propose a novel methodology that exploits expert knowledge in combination with recent advances in Multi-Task Learning (MTL) and Deep Neural Network (DNN) optimization to allow spectrum-based TC systems to run on constrained devices. This paper propose a well-defined and innovative methodology for resource-efficient, spectrum-based TC to address this issue, combining MTL with DNN optimization techniques. Performance evaluations on an NVIDIA Jetson TX2 demonstrate that our most optimized MTL model, handling four TC tasks, can reduce memory requirements by a factor of 2.65x and improve execution time by 3.6x compared to sequential execution of four Single-Task Learning (STL) models in a server-grade configuration, with minimal accuracy impact (less than a 0.5% drop) and energy efficiency of 0.97 millijoules per sample at inference. Compared to other edge platforms such as the Raspberry Pi model 3B+ (RPI3B+) with a low-power Artificial Intelligence (AI)-accelerator such as the Coral Tensor Processing Unit (TPU), the NVIDIA Jetson achieves a 12-fold improvement in energy efficiency with no impact on accuracy.These are the first available results to provide a benchmark for different performance metrics (memory, computing, energy) over heterogeneous constrained devices for this type of TC system.

Fyrooz Anika Khan, Nawshin Tabassum

The Central Business Districts (CBDs) of Dhaka are characterized by heavy traffic congestion, air pollution and peak office hour commuting density. Pedestrian-friendly environment is crucial for accessing the CBDs as it reduces auto-mobile dependency and encourages transit use. As of now, no research has assessed the walkability of Dhaka’s CBDs considering meso-scale factors such as land use diversity, density, street connectivity and micro-scale factors such as footpath continuity and quality, accessibility, safety, security, amenity and comfort of the streets. This paper evaluates the built environment attributes of total 15 wards across core CBDs of Dhaka North City Corporation (DNCC) and Dhaka South City Corporation (DSCC): Motijheel, Karwan Bazar, Mohakhali, Gulshan-Banani, and Mirpur to objectively assess walkability at both meso and micro scales. At meso-scale, a walkability index is generated that categorizes the wards into the most and least walkable using GIS, with Mirpur DNCC Ward-03 identified as the most walkable and Motijheel DSCC Ward-08 as the least walkable. At micro-scale, the walking environments in Mirpur Ward-03 and Motijheel Ward-08 are assessed based on Multi-Criteria Decision-Making (MCDM) approach. Results reveal several challenges such as narrow and discontinuous footpaths, presence of multiple barriers including utility poles, pillars, trees and, limited accessibility in Mirpur Ward-03 and garbage dumping, illegal car parking, haphazard hawker encroachments in Motijheel Ward-08. Our findings identify targeted areas needing improvement to enhance overall pedestrian-friendliness of the CBDs. The methodology followed in this study could be applied to evaluate the walkability of CBDs worldwide.

Qing Yang, Song Fu, Honggang Wang et al.

Connected and autonomous vehicles is a disruptive technology that has the potential to transform the current transportation system by reducing traffic accidents and enhancing driving safety. One major challenge of building such a system is how to realize effective and efficient cooperative perception among vehicles, which enables them to share local (raw or processed) perception data with each other or roadside infrastructures through wireless communications. As machine learning techniques become prevalent in autonomous vehicles, particularly in their perception subsystem, we articulate the possibility to design a machine-learning-enabled cooperative perception system for connected autonomous vehicles. Not only are the research challenges in designing cooperative perception presented, but we also focus on how to reduce communication and data processing latency in order to meet the stringent time requirements posed by autonomous driving applications. The article outlines the research challenges and opportunities in designing cooperative perception for autonomous vehicles, leveraging the recent research outcomes from machine learning, feature map quantification, millimeter-wave communications, and vehicular edge computing.

Zhenchang Xia, Jia Wu, Libing Wu et al.

Vehicular ad hoc networks (VANETs) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.

Chenzhao Zhai, Samantha Jamson, Zahara Batool et al.

Driving anger among Chinese drivers is common leading to aggressive and risky driving behaviours and potentially increasing involvement in road collisions. This study adopted an online survey to explore the relationship between personality, self-consciousness and driving anger expression. 559 participants completed a questionnaire consisting of the Driving Anger Scale (14-item DAS), the short version of the Driving Anger Expression Inventory (15 item DAX), the Brief HEXACO Inventory (BHI), and the Self-Consciousness Scale (SCS). A Confirmatory Factor Analysis yielded a reliable and valid three-factor structure of the Chinese 15 item DAX, labelled as “Adaptive Expression”, “Verbal Expression” and “Physical and Vehicle Expression”. Physical and Vehicle expression of anger was reported more by males and by experienced drivers compared to females and novice drivers. Traffic offenders showed more inclination towards exhibiting verbal anger expression than non-traffic offenders. In terms of dispositional traits, Humility-Honesty had a negative effect on both verbal expression and physical and vehicle expression. However, private self-consciousness was related to an increase in verbal expression and physical and vehicle expression. Importantly, Humility-Honesty and private self-consciousness moderated the relationship between trait driving anger and non-adaptive anger expressions in opposite ways. The findings could provide some support for the development of strategies to mitigate driving anger in China.

Ricardo Pereira, Neri dos Santos

<i>Background</i>: The Industry 5.0 emerges as a new paradigm for the industry by considering sustainability, human-centered approaches, organizational resilience, and interaction between humans and machines as its core values. This new trend for the future of the industry is referred to as neoindustrialization. Due to being a topic in development, there is still no precise consensus on its definition, which prompted the current study to comprehensively investigate and analyze the existing literature on Industry 5.0. <i>Methods</i>: The method employed was a scoping review, examining publications from various databases and academic journals, including those specific to the Brazilian context. <i>Results</i>: The results indicate a transition towards an industry that meets societal demands and respects planetary boundaries, aspects that were overlooked by Industry 4.0. <i>Conclusions</i>: In this new scenario, the industry reassumes its leadership by combining technology with new strategies and organizational models. Furthermore, it undergoes organizational changes to align its structure, operations, human resources, and new practices, aiming to meet the demands of society and all stakeholders involved. To achieve this, it is necessary to create an environment conducive to innovation and entrepreneurship, promoting the development of qualified human capital, investments in research and development, and strengthening partnerships between the public and private sectors. A successful neoindustrialization policy will generate high-quality jobs and foster economic growth. Industry 5.0 is the paradigm that will prevail in the 21st century. It is not a matter of speculation; it is an inseparable and inevitable reality. Otherwise, the industry will be relegated to a secondary role in the process of digital and social transformation.

Amira Saker, Amr Eltawil, Islam Ali

<i>Background</i>: The growth of e-commerce necessitates efficient logistics management to address rising last-mile delivery challenges. To overcome some of the last-mile delivery costs, parcel lockers as a delivery option, can be an alternative solution. This study presents the Capacitated Vehicle Routing Problem with Delivery Options (CVRPDO), which includes locker delivery. <i>Methods</i>: this problem is solved with An Adaptive Large Neighborhood Search (ALNS). The solution suggests some specific destroy and repair operators and integrates them with various selection schemes. The proposed method results are compared with the exact solution of the MIP model of the problem for validation. <i>Results</i>: Objective function values improved by 25%, 30%, 7%, 5%, and 6% for 1000, 800, 600, 400, and 200 customers, respectively, when using a 120-s ALNS runtime compared to the MIP model with a 3-h runtime. <i>Conclusions</i>: the CVRPDO problem involves creating a set of routes for ve-hicles that visit each customer at their delivery location or deliver their parcels to one of the lockers. These routes should respect the capacity of each vehicle and locker while minimizing the total routing costs and the number of utilized vehicles. The problem is resolved by ALNS algorithm, which outperformed the MIP model.

Hongzhi Guo, Jiajia Liu, Ju Ren et al.

Recently, traditional transportation systems have been gradually evolving to ITS, inspired by both artificial intelligence and wireless communications technologies. The vehicles get smarter and connected, and a variety of intelligent applications have emerged. Meanwhile, the shortage of vehicles' computing capacity makes it insufficient to support a growing number of applications due to their compute- intensive nature. This contradiction restricts the development of ICVs and ITS. Under this background, vehicular edge computing networks (VECNs), which integrate MEC and vehicular networks, have been proposed as a promising network paradigm. By deploying MEC servers at the edge of the network, ICVs' computational burden can be greatly eased via MEC offloading. However, existing task offloading schemes had insufficient consideration of fast-moving ICVs and frequent handover with the rapid changes in communications, computing resources, and so on. Toward this end, we design an intelligent task offloading scheme based on deep Q learning, to cope with such a rapidly changing scene, where software-defined network is introduced to achieve information collection and centralized management of the ICVs and the network. Extensive numerical results and analysis demonstrate that our scheme not only has good adaptability, but also can achieve high performance compared to traditional offloading schemes.

Dong Zheng, Chunming Jing, Rui-Fang Guo et al.

The vehicular ad-hoc networks (VANETs) is one of the most promising application in the communications of smart vehicles and the smart transportation systems. However, authentication and privacy of users are still two vital issues in VANETs. It is crucial to prevent internal vehicles from broadcasting the forged messages while preserving the privacy of vehicles against the tracking attack. Moreover, in the traditional mode, the transactional data storage provides no distributed and decentralized security, so that the third party initiates the dishonest behaviors possibly. In this paper, based on blockchain technique, we propose a traceable and decentralized the Internet of Vehicle system framework for communication among smart vehicles by employing of a secure access authentication scheme between vehicles and RoadSide Units (RSUs). On the one hand, this scheme allows that vehicles employ pseudonyms for Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications anonymously in the non-fully trusted environment. On the other hand, the transparency of vehicles in authentication and announcement is preformed efficiently by the blockchain technology. In addition, the transaction information is tamper-resistant that provides the distributed and decentralized property for the different cloud servers. With the help of Certificate Authority (CA) and the RoadSide Units (RSUs), our proposal achieves the conditional privacy to trace the real identity of the malicious vehicle in the anonymous announcements as well. Finally, through the theoretical analysis and simulations, our scheme is able to construct a secure and decentralized system framework of VANETs with accountability and privacy preservation.

Peng Han, Xiaoxia Yang

The reliable and accurate detection of bridges plays an important role in imaging-driven transportation surveillance. It is capable of timely providing the traffic information, leading to safer and more convenient transportation. However, the visual quality of observed images is often inevitably reduced owing to the adverse weather conditions, e.g., haze and low lightness. It is still difficult to adopt the existing powerful deep learning methods to reliably and accurately detect the bridges under different imaging conditions. To achieve satisfactory bridge detection results, we first propose to exploit the data augmentation strategy and physical imaging method to generate the natural-looking experimental dataset, which contains latent high-quality images and their hazy and low-light versions. We then investigate how to further promote the deep learning-based bridge detection methods through the manually generated dataset. It is obvious that the generalization abilities of these deep neural networks are significantly improved using this data augmentation strategy. In this work, we constructed an original dataset consisting of 3500 images of size 900×600, collected under normal imaging condition. Extensive detection experiments will be performed based on the augmented dataset. Experimental results have demonstrated that our automatic bridge detection framework could generate more reliable and accurate results compared with existing detection methods.

Y. Akbar, U. Akram, M.A. Aun et al.