Hasil untuk "Transportation and communication"

Taiyuan Gong, Li Zhu, Fei Yu et al.

Edge intelligence (EI) is becoming one of the research hotspots among researchers, which is believed to help empower intelligent transportation systems (ITS). ITS generates a large amount of data at the network edge by millions of devices and sensors. Data-driven artificial intelligence (AI) is at the core of ITS development. By pushing the AI frontier to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network and better use edge big data. This paper surveys Edge Intelligence in Intelligent Transportation Systems. We first introduce the challenges ITS faces and explain the motivation of using EI in ITS. We then explore the framework of using EI in ITS, including the EI-based ITS architecture, the data gathering and communication methods, the data processing and service delivery, and the performance indexes. The enabling technologies, such as AI models, the Internet of Things, and Edge Computing technologies used in EI-based ITS, are reviewed intensively. We discuss the edge intelligence applications and research fields in ITS in depth. Typical application scenarios, such as autonomous driving, vehicular edge computing, intelligent vehicular transportation system, unmanned aerial vehicle (UAV) in ITS environment, and rail transportation control and management, are explored. The general platforms of EI, the EI training and inference in ITS, as well as the benchmark datasets, are introduced. Finally, we discuss some of the challenges and future directions of using EI in ITS.

J. Bharadiya

Purpose: The purpose of the research is to investigate the role of machine learning (ML) and artificial intelligence (AI) in the growth of smart cities. It aims to understand how these technologies are being used to manage expanding metropolitan areas, boost economies, reduce energy consumption, and improve the living standards of residents. The study also aims to analyze the information flow associated with ICT in smart cities. Methodology: The methodology involves conducting a survey to identify the typical technologies used to support communication in smart cities. It also involves a systematic evaluation of current patterns in publications related to ICT in smart cities. The research utilizes ML and AI techniques to analyze and interpret the collected data. Findings: The findings of the study indicate that ML and AI play a significant role in various aspects of smart cities, particularly in the field of intelligent transportation systems. These technologies are utilized for tasks such as modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. The research also reveals the application of ML and AI in other forms of transportation like air, rail, and road travel. Recommendations: Based on the findings, the study suggests that the agent computing paradigm is a powerful technology for the development of large-scale distributed systems, particularly in the context of geographically dispersed and dynamic transport systems. The research emphasizes the interoperability, flexibility, and extendibility of agent-based traffic control and management systems. It concludes by suggesting potential future research directions to effectively integrate agent technology into traffic and transportation systems.

Ruiqi Liu, Meng Hua, Ke Guan et al.

With the emergence of communication services with stringent requirements such as autonomous driving or on- flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.

Shiying Zhang, Jun Li, Long Shi et al.

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions.

Run Liu, Anfeng Liu, Zhen-Feng Qu et al.

6G networks provide faster communication for connected vehicles. These vehicles are connected to the Internet, forming the Internet of Vehicles (IoV). Due to the development of Intelligent Transportation Systems (ITS), more and more vehicles are deployed with data-intensive applications. These applications interact heavily with IoT devices at the edge of the network, which causes IoT devices to consume a lot of limited and valuable power. Task offloading can help overcome resource constraints of IoT devices by offloading task to edge server which has sufficient computational power in ITS. Unmanned Aerial Vehicles (UAV) is a promising solution by serving as Computing-Communications Edge Server (CCES) for resource-constrained IoT devices that there is no edge server nearby that can offload task. Due to the IoT devices’ limited battery capacity and UAV energy budget, it is a challenging issue to reduce the energy for task offloading in UAV-enable edge network. In this paper, an UAV-enabled Computing-Communications Intelligent Offloading (UAV-CCIO) scheme is proposed to offload task energy-efficiently. First, some nodes with a large amount of data are selected as Task Gathering Nodes (TGNs), and TGNs collect all the tasks of the left nodes. In this way, the UAV can only fly the TGNs and so all the IoT devices’ tasks can be offloaded. The distance needed for the UAV can be greatly reduced and energy is saved. On the other hand, tasks that route to TGNs have a relatively small amount of data, while nodes with a large amount of data have already been selected as TGNs without routing, thus saving energy. Second, an optimization strategy for collection tasks is proposed to reduce UAV’s energy. The extensive experimental simulations indicate that the performance of UAV-CCIO scheme is better than the existing scheme.

M. Muslam

Vehicle-to-vehicle (V2V) communication has played a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research focused on investigating the security protocols that have been employed in vehicle-to-vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers was conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompassed key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delved into the challenges and limitations associated with implementing these protocols. The research aimed to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper proposes improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research contributes to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

Muhammad Sami Irfan, Sagar Dasgupta, Mizanur Rahman

Digital twin (DT) systems aim to create virtual replicas of physical objects that are updated in real-time with their physical counterparts and evolve alongside the physical assets throughout their lifecycle. DT technology promises to significantly enhance the capabilities of intelligent transportation systems (ITSs) through improving safety and mobility. The objectives of this review article are to: 1) conduct an in-depth review of DT technology applications in the transportation domain, i.e., safety and mobility applications; 2) develop a reference architecture for a transportation DT (TDT) system based on a hierarchical concept to guide in developing TDT systems at any scale; 3) identify research challenges for each component, i.e., the physical space, the communication gateway and the digital space of the TDT system, to realize safe and operationally efficient ITS; and 4) provide future research perspectives to develop and deploy networkwide transportation DT systems considering the scale and dynamic nature of future connected and automated transportation systems. Thus, this article focuses on providing a comprehensive understanding of the requirements, reference architecture, challenges, and future research opportunities for a TDT system, starting from individual transportation assets and building up to the entire networkwide TDT.

Babin Manandhar, Kayode Dunkel Vance, Danda B. Rawat et al.

Public transportation systems face numerous challenges like traffic congestion, inconsistent schedules, and variable passenger demand. These issues lead to delays, overcrowding, and reduced patron satisfaction. Digital twin (DT) technology is a promising innovation for improving public transportation systems by offering real-time virtual representations of physical systems. By integrating real-time data from various sources, digital twins can enable predictive analytics, optimize operations, and improve the overall performance of public transportation networks. This work explores the potential of digital twins to optimize operational efficiency, enhance passenger experiences, and support sustainable urban mobility. A comprehensive review of the existing literature was conducted by analyzing case studies, theoretical models, and practical implementations to assess the effectiveness of DTs in transit systems. While the benefits of DTs are significant, their successful implementation in bus transportation systems is impeded by several challenges like scalability limitations, interoperability issues, and technical complexities involving data integration and IT infrastructure. This paper discusses ways to overcome these challenges, which include using modular designs, microservices, blockchain for security, and standardized communication for better integration. It emphasizes the importance of collaboration in research and practice to effectively apply digital twin technology to public transit systems.

Zhiyong Zhong, Zhangyou Peng

Vehicle-to-Everything (V2X) communication is expected to play a critical role in enabling Intelligent Transportation Systems (ITS) within sixth-generation (6G) networks. Integrated Sensing and Communication (ISAC) technology is essential for enhancing spectrum efficiency and reducing resource overhead. However, this also demands a more intelligent and efficient resource allocation framework for next-generation vehicular networks. In this paper, we propose a joint resource allocation method for V2X communication and sensing, aiming to optimize both communication rate and sensing performance. We consider both communication and sensing, using the communication rate as the measure of communication and the Cramér-Rao Lower Bound (CRLB) as the measure of sensing accuracy. In addition, a reward function is designed based on the characteristics of the scenario. The power allocation is used as a continuous action space, and we employ the MultiAgent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve this optimization problem to address the challenge of dynamic resource allocation. Simulation results show that the proposed method achieves joint optimization of communication and sensing resources across various scenarios, significantly improving the overall system performance. Compared with the PPO algorithm, the proposed algorithm can improve the communication rate by 60% and achieve the trade-off between communication and sensing performance.

Shuai Yu, Yuqing Liu, Song Hu

In the rapid process of urbanization, urban agglomerations have become a key driving factor for regional development and spatial reorganization. The formation and development of urban agglomerations rely on communication between cities. However, the spatiotemporal characteristics of intercity travelers are not fully grasped throughout the entire trip chain. This study proposes a spatiotemporal analysis method for intercity travel in urban agglomerations by constructing origin-to-destination (OD) trip chains using smartphone data, with the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employed Cramer’s V and Spearman correlation coefficients for multivariate feature selection, identifying 12 key variables from an initial set of 20. Then, optimal cluster configuration was determined via silhouette analysis. Finally, the K-prototypes algorithm was applied to cluster 161,797 intercity trip chains across six transportation corridors in 2019 and 2021, facilitating a comparative spatiotemporal analysis of travel patterns. Results show the following: (1) Intercity travelers are predominantly males aged 19–35, with significantly higher weekday volumes; (2) Modal split exhibits significant spatial heterogeneity—the metro predominates in Beijing while road transport prevails elsewhere; (3) Departure hubs’ waiting times increased significantly in 2021 relative to 2019 baselines; (4) Increased metro mileage correlates positively with extended intra-city travel distances. The results substantially contribute to transportation planning, particularly in optimizing multimodal hub operations and infrastructure investment allocation.

Zelin Wang, Qixiu Cheng, Ziyuan Gu et al.

As pivotal drivers of smart cities, mega-mobility systems integrate large-scale transportation networks, communication nodes, and energy circuits into a coupled multinetwork system. Urban megasystems epitomize the grand challenge of “organized complexity”, exhibiting characteristic features such as adaptive openness, nonlinear dynamics, hierarchical organization, and emergent properties. Analytical investigations, constrained by the rigid separation of macro- and microlevel paradigms, struggle to capture the nonlinear interdependencies across levels that define mega-mobility systems. In this review, we systematically advance macro–micro integration with feedback (MMIF) as a transformative paradigm for analyzing urban mega-mobility systems, synthesizing the state-of-the-art developments in typical constituent subsystems under this unified perspective. The MMIF paradigm bridges the gap between theoretical abstraction and empirical practice, contributing to scientifically sound urban development by harmonizing emergent patterns with granular behavioral dynamics. Building upon this paradigm, we investigate the key methods and technologies empowered by artificial intelligence that enable MMIF and critically analyze the enduring challenges and prospective research directions. As urban mobility systems increasingly serve as test beds for complexity science, the MMIF paradigm using artificial intelligence promises to reshape interdisciplinary collaboration, offering a blueprint for building intelligent, adaptive, and human-centric cities.

Ngozi Idemili-Aronu, Babayemi O. Olakunde, Tara M. Friebel-Klingner et al.

PURPOSEAfrica faces a growing burden of cancer yet remains under-represented in global cancer clinical trials. This disparity limits the generation of population-specific evidence needed to improve cancer outcomes. Recruitment and retention in cancer clinical trials are particularly challenging because of various systemic and individual barriers in Nigeria. This study explores patients' perspectives on barriers and facilitators to recruitment and retention in cancer clinical trials.METHODSA convergent parallel mixed-methods design was used, which comprised a cross-sectional survey and a descriptive qualitative approach. Participants were recruited from multiple oncology centers and secondary facilities within Nigeria’s ICON-3 Practice-Based Research Network. Quantitative data were collected through interviewer-administered questionnaires, whereas qualitative data were gathered via semistructured interviews and analyzed thematically.RESULTSA total of 317 patients participated in the quantitative survey, 18 of whom participated in interviews. Barriers included limited understanding of clinical trials, logistical challenges such as transportation and visit frequency, distrust in researchers and the health care system, and lack of family support. Facilitators included effective communication, incentives, flexible research visits, and culturally tailored interventions.CONCLUSIONTo optimize cancer clinical trial participation in low-resource settings, interventions must be tailored to local contexts, addressing structural and cultural barriers. Enhanced communication, community involvement, and supportive policies can significantly improve trial participation and outcomes.

Yan He, Haibo Li, Chuanping He et al.

This study investigated the impact of foaming water consumption on the mechanical properties of foam warm mix matrix asphalt mixture (FWMAM) and foam warm mix SBS-modified asphalt mixture (FWMAM-SBS) through comparative analysis with conventional hot mix matrix asphalt mixture (HMAM) and hot mix SBS-modified asphalt mixture (HMAM-SBS). The results showed that: The tensile strength ratio (TSR) of FWMAM showed no significant difference from HMAM, whereas HMAM-SBS exhibited slightly reduced TSR compared to FWMAM-SBS. The rutting resistance of FWMAM was superior to HMAM and enhanced progressively as the water consumption increased, while the anti-rutting performance of FWMAM-SBS was less affected by the foaming water consumption. The low-temperature cracking resistance of FWMAM was generally weaker than that of HMAM, while the opposite is true for FWMAM-SBS, with lower temperatures amplifying the influence of foaming water content. The master curve distribution characteristics of the FWMAM are consistent with those of the HMAM, and the water consumption is positively correlated with the modulus of the FWMAM and has a lower effect on the FWMAM-SBS at the conventional loading frequency. For fatigue life, both FWMAM and FWMAM-SBS outperform the corresponding hot-mix mixture. The fatigue life of FWMAM gradually decreased with increasing foaming water content, while FWMAM-SBS initially increased and then decreased, peaking at 2% water content, while fatigue life of FWMAM at low strain level conditions is roughly 1.2 times higher than that of the HMAM. Based on mechanical performance and foaming efficacy, optimal water consumption thresholds are proposed: ≤1% for matrix asphalt and ≤2% for SBS-modified asphalt.

Tarik Harroud, Hasnaa Azami Idrissi, Rachid Alillouch

Like many countries in the Global North, Morocco has embraced the digital age in recent years, launching a range of programs aimed at harnessing the benefits of the smart city. Through the growing use of information and communication technologies, the goal is to enhance the management, oversight, and public image of its major urban centers.In this context, a new city was launched in 2006 as a testing ground for international standards in sustainable and smart urban development. Largely financed by international donors and designed by globally recognized experts, the Zenata Eco-city has introduced a range of digital innovations in urban service management and transportation infrastructure, positioning it as a “model” initiative in Morocco. Constructed on a strategic site near Casablanca, previously occupied by numerous shantytowns, the smart city has also had to confront significant social and economic challenges, particularly around the relocation of low-income communities. This contribution highlights the digital and technological tools deployed in Morocco’s first smart city, as well as the socio-spatial impacts they have generated. Special attention is given to how former shantytown residents have appropriated digital platforms and social media as new spaces for protest and civic engagement. Drawing on fieldwork that includes interviews with planners, administrators, and residents, along with analysis of local social media content, the study reveals how digital tools have been leveraged by local associations and communities to assert their rights and resist the exclusionary dynamics of the smart city.

A. V. Jha, B. Appasani, Mohammad S. Khan et al.

M. Naeem, Sushank Chaudhary, Yahui Meng

Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization concerns. This research looks closely at the important infrastructure parts of vehicle-to-vehicle (V2V) communication systems. It focuses on the different types of communication architectures that are out there, including decentralized mesh networks, cloud-integrated hubs, edge computing-based architectures, blockchain-enabled networks, hybrid cellular networks, ad-hoc networks, and AI-driven dynamic networks. This review aims to critically analyze and compare the key components of these architectures with their contributions and limitations. Finally, it outlines open research challenges and future technological advancements, encouraging the development of robust and interconnected V2V communication systems in ITSs.

Shiqi Sun, Cathérine Cassan, Cathy Macharis

Ali Yousaf, Sabir Muhammad, Hassan Muhammad Salman

Railway projects are incredibly costly due to their complex nature. This study performs risk analysis and management of Pakistan railways ML-1 project expansion and upgradation that will be undertaken at US$6.8 billion under the China-Pakistan Economic Corridor (CPEC). The risks were identified based on an extensive literature review and their relevance to the CPEC. We employed fuzzy set theory (integrated with the risk severity index) to obtain the most severe risks. The novelty of this study is the technique employed to analyse and rank the risk, also there are no previous studies conducted that emphasized the construction risk of railway projects in Pakistan. The study is useful for all stakeholders of the project, as it can plan for them to tackle these risks and implement ML-1 successfully.

ZHAO Rui, WEN Xiaoran, ZHENG Shilei et al.

With the rapid development of intelligent transportation systems, sidelink communication technology for cellular vehicle-to-everything (C-V2X) has become the key technology to enable vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-pedestrian communications. 3rd Generation Partnership Project (3GPP) has finished research on sidelink communication technology enhancement, aiming to meet the diverse needs of V2X services, and is actively advancing research and standardization on 6G evolution technologies such as integrated sensing and communication (ISAC), artificial intelligence and machine learning (AI/ML). The development trends of sidelink communication technology in C-V2X were firstly introduced. Then, the key evolution points of 3GPP Release 18 sidelink communication technology were discussed. Finally, considering the research on 6G technology in 3GPP Release 19, a perspective on the standard future development direction of C-V2X was provided.

Fiona S. McEwen, Hania El Khatib, Kristin Hadfield et al.

Abstract Background Refugee children are at high risk of mental health problems but face barriers to accessing mental health services, a problem exacerbated by a shortage of mental health professionals. Having trained lay counsellors deliver therapy via telephone could overcome these barriers. This is the first study to explore feasibility and acceptability of telephone-delivered therapy with refugee children in a humanitarian setting. Methods An evidence-based intervention, Common Elements Treatment Approach, was adapted for telephone-delivery (t-CETA) and delivered by lay counsellors to Syrian refugee children in informal tented settlements in the Beqaa region of Lebanon. Following delivery of t-CETA, semi-structured interviews were conducted with counsellors (N = 3) and with children who received t-CETA (N = 11, 45% female, age 8–17 years) and their caregivers (N = 11, 100% female, age 29–56 years) (N = 25 interviews). Thematic content analysis was conducted separately for interviews with counsellors and interviews with families and results were synthesized. Results Three themes emerged from interviews with counsellors and four themes from interviews with families, with substantial overlap between them. Synthesized themes were: counselling over the phone both solves and creates practical and logistical challenges; t-CETA is adapted to potential cultural blocks; the relationship between the counsellor and the child and caregiver is extremely important; the family’s attitude to mental health influences their understanding of and engagement with counselling; and t-CETA works and is needed. Counselling over the phone overcame logistical barriers, such as poor transportation, and cultural barriers, such as stigma associated with attending mental health services. It provided a more flexible and accessible service and resulted in reductions in symptoms for many children. Challenges included access to phones and poor network coverage, finding an appropriate space, and communication challenges over the phone. Conclusions Despite some challenges, telephone-delivered therapy for children shows promising evidence of feasibility and acceptability in a humanitarian context and has the potential to increase access to mental health services by hard-to-reach populations. Approaches to addressing challenges of telephone-delivered therapy are discussed. Trial Registration ClinicalTrials.gov ID: NCT03887312; registered 22nd March 2019.