Hasil untuk "Transportation and communication"

Shrief Rizkalla, Adrian Kliks, Nila Bagheri et al.

This white paper aims to comprehensively analyze and consolidate the state of the art in communication technologies supporting modern and future Information and Communication Technology (ICT). Its primary objective is to establish a common understanding of how communication solutions enable automation, safety, and efficiency across multiple transport domains, including railways, road vehicles, aircraft, and unmanned aerial vehicles. The document seeks to identify key communication requirements and technological enablers necessary for interoperable and reliable ITS operation. It also assesses the limitations of current systems and proposes pathways for integrating emerging technologies such as 5G, Sixth Generation (6G), and Artificial Intelligence (AI)-driven network control. The white paper also intends to support harmonization between different transport modes through a unified framework for communication modeling, testing, and standardization. It highlights the importance of accurate channel modeling and empirical validation to design efficient, robust, and scalable systems. Another objective is to explore the use of reconfigurable intelligent surfaces, integrated sensing and communication, and digital twin concepts within ITS. The document emphasizes the role of spectrum management and standardization efforts in ensuring interoperability among diverse communication systems. Finally, the paper seeks to stimulate collaboration among academia, industry, and standardization bodies to advance the design of resilient and adaptive communication infrastructures for future transportation systems.

Subul Malik, Marina Plesons, Monica Faraldo et al.

Abstract Background People who inject drugs (PWID) are at increased risk for viral hepatitis, yet hepatitis A virus (HAV) and hepatitis B virus (HBV) screening and immunization rates remain low. Although offering HAV and HBV services at syringe services programs (SSPs) is effective, few U.S. SSPs currently offer them. Limited qualitative research exists on the advantages and optimization of these services at SSPs. This study explored PWID and SSP staff perspectives regarding barriers to HAV and HBV prevention and care services in traditional healthcare, facilitators for SSP-based provision, and opportunities to improve service delivery. Methods This study was conducted at an SSP in Miami, Florida serving over 2500 PWID annually. Quantitative data on vaccine administration from August 2023 to May 2025 were abstracted from the SSP database. Prior to implementation, in May 2022, we conducted in-depth interviews with 15 PWID and 11 SSP staff. Transcripts were analyzed using codebook thematic analysis in Dedoose. Results From August 2023 to May 2025, the SSP administered 114 HAV and 176 HBV vaccine doses. Qualitative interviews from May 2022 revealed several key findings. Barriers included limited knowledge, stigma and discrimination, resource and transportation challenges, navigation difficulties, and limited prioritization. Facilitators for SSP-based services included the benefits of co-located, on-demand care, and non-stigmatizing and supportive environment. Opportunities for improvement included offering incentives, expanding outreach, and increasing communication. Conclusion PWID face significant barriers to HAV and HBV services in traditional healthcare, including stigma, logistical challenges, and limited awareness of viral hepatitis. Integrating these services into SSPs enhanced accessibility and uptake by leveraging trust, convenience, and harm reduction principles.

Dazhe Wang, Xiaolei Yang

Enhancing the subjective well-being of poor households is crucial for the world’s sustainable development. Using a comprehensive household-level dataset from the China Household Finance Survey (CHFS) spanning 2011 to 2019, this study employed a multi-period difference-in-differences (DID) approach to systematically identify the causal effect and underlying mechanisms of the Targeted Poverty Alleviation (TPA) program on the subjective well-being of poverty-stricken households. Then it explored the heterogeneous effects of different assistance measures on their subjective well-being. We found that the TPA program significantly improves the subjective well-being of rural poor households after a series of robustness checks. The analysis indicated that the TPA program improves the happiness of poor households by reducing their relative poverty and promoting their labor participation to eliminate poverty. We found that providing basic public services, means of agricultural production, and communication infrastructure all enhance the positive impact of TPA on happiness, while the housing relocation program, transportation infrastructure investment, and agricultural technical support do not. The conclusions of this study have important policy implications for ensuring equitable access to basic public services, consolidating the effective link between poverty alleviation achievements and rural revitalization in the post-poverty era, thereby promoting the common prosperity of rural households.

Mingqian Liu, Zhenju Zhang, Yunfei Chen et al.

Air transportation communication jamming recognition model based on deep learning (DL) can quickly and accurately identify and classify communication jamming, to improve the safety and reliability of air traffic. However, due to the vulnerability of deep learning, the jamming recognition model can be easily attacked by the attacker’s carefully designed adversarial examples. Although some defense methods have been proposed, they have strong pertinence to attacks. Thus, new attack methods are needed to improve the defense performance of the model. In this work, we improve the existing attack methods and propose a double level attack method. By constructing the dynamic iterative step size and analyzing the class characteristics of the signals, this method can use the adversarial losses of feature layer and decision layer to generate adversarial examples with stronger attack performance. In order to improve the robustness of the recognition model, we use adversarial examples to train the model, and transfer the knowledge learned from the model to the jamming recognition models in other wireless communication environments by transfer learning. Simulation results show that the proposed attack and defense methods have good performance.

Zhiqing Wei, Jinzhu Jia, Yangyang Niu et al.

Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The performance of radar sensing in ISAC systems is closely related to the characteristics of radar sensing and communication channels. Therefore, ISAC channel modeling serves as a fundamental cornerstone for evaluating and optimizing ISAC systems. This article provides a comprehensive survey on the ISAC channel modeling methods. Furthermore, the methods of target radar cross section (RCS) modeling and clutter RCS modeling are summarized. Finally, we discuss the future research trends related to ISAC channel modeling in various scenarios.

Tong Nie, Jian Sun, Wei Ma

Modern transportation systems face pressing challenges due to increasing demand, dynamic environments, and heterogeneous information integration. The rapid evolution of Large Language Models (LLMs) offers transformative potential to address these challenges. Extensive knowledge and high-level capabilities derived from pretraining evolve the default role of LLMs as text generators to become versatile, knowledge-driven task solvers for intelligent transportation systems. This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation into four synergetic dimensions: information processors, knowledge encoders, component generators, and decision facilitators. Through a unified taxonomy, we systematically elucidate how LLMs bridge fragmented data pipelines, enhance predictive analytics, simulate human-like reasoning, and enable closed-loop interactions across sensing, learning, modeling, and managing tasks in transportation systems. For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization, highlighting how emergent capabilities of LLMs such as in-context learning and step-by-step reasoning can enhance the operation and management of transportation systems. We further curate practical guidance, including available resources and computational guidelines, to support real-world deployment. By identifying challenges in existing LLM-based solutions, this survey charts a roadmap for advancing LLM-driven transportation research, positioning LLMs as central actors in the next generation of cyber-physical-social mobility ecosystems. Online resources can be found in the project page: https://github.com/tongnie/awesome-llm4tr.

Yihao Wang, Lingxiao Li, Yifan Tang et al.

Intelligent transportation systems (ITS) use advanced technologies such as artificial intelligence to significantly improve traffic flow management efficiency, and promote the intelligent development of the transportation industry. However, if the data in ITS is attacked, such as tampering or forgery, it will endanger public safety and cause social losses. Therefore, this paper proposes a watermarking that can verify the integrity of copyright in response to the needs of ITS, termed ITSmark. ITSmark focuses on functions such as extracting watermarks, verifying permission, and tracing tampered locations. The scheme uses the copyright information to build the multi-bit space and divides this space into multiple segments. These segments will be assigned to tokens. Thus, the next token is determined by its segment which contains the copyright. In this way, the obtained data contains the custom watermark. To ensure the authorization, key parameters are encrypted during copyright embedding to obtain cipher data. Only by possessing the correct cipher data and private key, can the user entirely extract the watermark. Experiments show that ITSmark surpasses baseline performances in data quality, extraction accuracy, and unforgeability. It also shows unique capabilities of permission verification and tampered location tracing, which ensures the security of extraction and the reliability of copyright verification. Furthermore, ITSmark can also customize the watermark embedding position and proportion according to user needs, making embedding more flexible.

Ostonya Thomas, M Sabbir Salek, Jean-Michel Tine et al.

The transportation industry is experiencing vast digitalization as a plethora of technologies are being implemented to improve efficiency, functionality, and safety. Although technological advancements bring many benefits to transportation, integrating cyberspace across transportation sectors has introduced new and deliberate cyber threats. In the past, public agencies assumed digital infrastructure was secured since its vulnerabilities were unknown to adversaries. However, with the expansion of cyberspace, this assumption has become invalid. With the rapid advancement of wireless technologies, transportation systems are increasingly interconnected with both transportation and non-transportation networks in an internet-of-things ecosystem, expanding cyberspace in transportation and increasing threats and vulnerabilities. This study investigates some prominent reasons for the increase in cyber vulnerabilities in transportation. In addition, this study presents various collaborative strategies among stakeholders that could help improve cybersecurity in the transportation industry. These strategies address programmatic and policy aspects and suggest avenues for technological research and development. The latter highlights opportunities for future research to enhance the cybersecurity of transportation systems and infrastructure by leveraging hybrid approaches and emerging technologies.

José Manuel Rúa-Estévez, Alicia Meleiro-Estévez, Pablo Fondo-Ferreiro et al.

This work presents a simulator designed for the validation, evaluation, and demonstration of flying adhoc networks (FANETs) using 5G vehicle-to-everything (V2X) communications and the named-data networking (NDN) paradigm. The simulator integrates the ns-3 network simulator and the Zenoh NDN protocol, enabling realistic testing of applications that involve the multi-hop communication among multiple unmanned aerial vehicles (UAVs).

Martín Schottlender, Maximilian Schäfer, Ricardo A. Veiga

Molecular Communications (MC) is an emerging research paradigm that utilizes molecules to transmit information, with promising applications in biomedicine such as targeted drug delivery or tumor detection. It is also envisioned as a key enabler of the Internet of BioNanoThings (IoBNT). In this paper, we propose algorithms based on Recurrent Neural Networks (RNN) for the estimation of communication channel parameters in MC systems. We focus on a simple branched topology, simulating the molecule movement with a macroscopic MC simulator. The Deep Learning architectures proposed for distance estimation demonstrate strong performance within these branched environments, highlighting their potential for future MC applications.

Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ahmed Ali Ahmed et al.

Abstract Vehicular Ad-Hoc Networks (VANETs) have facilitated the massive exchange of real-time traffic and weather conditions, which have helped prevent collisions, reduce accidents, and road congestions. This can effectively enhance driving safety and efficiency in technology-driven transportation systems. However, the transmission of massive and sensitive information across public wireless communication channels exposes the transmitted data to a myriad of privacy as well as security threats. Although past researches has developed many vehicular ad-hoc networks security preservation schemes, several of them are inefficient or susceptible to attacks. This work, introduces an approach that leverages reverse fuzzy extraction, bilinear pairing, and Physically Unclonable Function (PUF) to design an efficient and anonymity-preserving authentication scheme. We conduct an elaborate formal security analysis to demonstrate that the derived session key is secure. The semantic security analyses also demonstrate its resilience against typical VANET attacks such as impersonations, denial of service, and de-synchronization, instilling confidence in its effectiveness. Moreover, our approach incurs the lowest computational overheads at relatively low communication costs. Specifically, our protocol attains a 66.696% reduction in computation costs, and a 70% increment in the supported security functionalities.

Mateusz Zajac, Tomislav Rožić, Justyna Swieboda-Kutera et al.

<i>Background</i>: The increasing complexity of global supply chains and environmental expectations has highlighted the strategic importance of digital transformation in the transport, forwarding, and logistics (TFL) sector. Despite a growing portfolio of available tools, adoption rates—particularly among small and medium-sized enterprises (SMEs) in Central and Eastern Europe—remain low. This study investigates the barriers and motivations related to the implementation of digital planning tools supporting intermodal transport planning. <i>Methods</i>: A structured online survey was conducted among 80 Polish TFL enterprises, targeting decision-makers responsible for operational and digital strategies. The questionnaire included 17 closed and semi-open questions grouped into three thematic sections: tool usage, implementation barriers, and digital readiness. <i>Results</i>: The findings indicate that only 20% of respondents use dedicated route planning tools, and merely 10% report satisfaction with their performance. Key barriers include lack of awareness, organizational inertia, and the prioritization of other initiatives, with financial cost cited less frequently. While environmental sustainability is declared as a priority by most enterprises, digital support for emission tracking is limited. The results highlight the need for targeted education, integration support, and differentiated platform functionalities for SMEs and larger firms. <i>Conclusions</i>: This study offers evidence-based recommendations for developers, policymakers, and logistics managers aiming to accelerate digital adoption in the intermodal logistics landscape.

H. A. Elhamy, A. B. Eltawil

<i>Background</i>: Defining the optimal fleet portfolio is a crucial process in airline planning. The published efforts in literature provide ways to anticipate the disruption effects on the passenger demand; however, the proposed solution in this paper provides visibility on the impact of sustainable disruption and the way an airline can resist it. <i>Methods</i>: This paper proposes a two-stage methodology to find the best portfolio for airline operational requirements under the impact of disruption. The first stage considers optimization for normal airline operations under a specific fleet portfolio using an Integer Linear Programming (ILP) model. The second stage of the analysis is a mapping for the scenario-based methodology to find a way out for an airline subjected to some given disruption in operations. <i>Results</i>: The result of the two-stage analysis shall define the best fleet portfolio to withstand sustained disruptions by mapping the results in a disruption funnel and showing the impact of the supply and demand gap on the airline’s sustainable profitability. <i>Conclusions</i>: This paper provides a novel, practical way of evaluating strategic decisions to choose the best fleet portfolio and make airlines rely on the mapping of the disruption funnel to modify their network while increasing supply chain resilience.

Tan Yigitcanlar, Adam T. Downie, Shane Mathews et al.

Azizulrahman Shafiqurrahman, V. Khadkikar, A. Rathore

The concept of energy transfer between two electric vehicles and communication between them is a promising one for the future of the electrified transportation sector. In response to the growing research and interest in vehicle-to-vehicle (V2V) technology, this article provides an in-depth review of the actual energy transfer between two vehicles and their communication aspects. The literature is addressed to analyze power electronics topologies for successful V2V power transfer and compare V2V charging optimization techniques. Communication protocols and standards relevant to V2V technology are also discussed with a focus on their potential applications for improving transportation safety and efficiency. Furthermore, challenges faced by existing V2V power transfer solutions and the commercial products available for implementing V2V charging are described. In contrast to other literature surveys, this article provides a comprehensive overview of V2V power transfer and communication technologies with implications for the future of sustainable electrified transportation. The study and discussion of over 300 papers on the topic are encompassed in this article.

Y. Rajkumar, Sripathi Venkata Naga Santhosh Kumar

Mingan Luan, Bo Wang, Zheng Chang et al.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Dushyantha A Basnayaka

This paper, based on recent research, articulates the opportunities and challenges posed by an emerging area of study known as ``mediumband wireless communication'', which refers to digital radio-frequency (RF) wireless communication through mediumband channels. This class of channels that falls in the transitional region between the narrowband and broadband channels, in many ways, is unique and shows significant potential. For instance, the effect of a highly unfavourable non-line-of-sight (NLoS) propagation environment can be transformed into a significantly favourable condition without making any intervention on the original propagation environment, but by simply communicating in the mediumband. The more unfavourable a propagation environment for wireless communication, the higher the potential gain by communicating in the mediumband. In this paper, using lay language as much as possible, we elaborate the unique properties of mediumband channels and implications of communicating in the mediumband for wider wireless communication along with some future research directions.

Qionghua Liao, Guilong Li, Jiajie Yu et al.

With the proliferation of electric vehicles (EVs), the transportation network and power grid become increasingly interdependent and coupled via charging stations. The concomitant growth in charging demand has posed challenges for both networks, highlighting the importance of charging coordination. Existing literature largely overlooks the interactions between power grid security and traffic efficiency. In view of this, we study the en-route charging station (CS) recommendation problem for EVs in dynamically coupled transportation-power systems. The system-level objective is to maximize the overall traffic efficiency while ensuring the safety of the power grid. This problem is for the first time formulated as a constrained Markov decision process (CMDP), and an online prediction-assisted safe reinforcement learning (OP-SRL) method is proposed to learn the optimal and secure policy by extending the PPO method. To be specific, we mainly address two challenges. First, the constrained optimization problem is converted into an equivalent unconstrained optimization problem by applying the Lagrangian method. Second, to account for the uncertain long-time delay between performing CS recommendation and commencing charging, we put forward an online sequence-to-sequence (Seq2Seq) predictor for state augmentation to guide the agent in making forward-thinking decisions. Finally, we conduct comprehensive experimental studies based on the Nguyen-Dupuis network and a large-scale real-world road network, coupled with IEEE 33-bus and IEEE 69-bus distribution systems, respectively. Results demonstrate that the proposed method outperforms baselines in terms of road network efficiency, power grid safety, and EV user satisfaction. The case study on the real-world network also illustrates the applicability in the practical context.

Jonathan A. Soares, Kayol S. Mayer, Dalton S. Arantes

In the ever-evolving field of digital communication systems, complex-valued neural networks (CVNNs) have become a cornerstone, delivering exceptional performance in tasks like equalization, channel estimation, beamforming, and decoding. Among the myriad of CVNN architectures, the phase-transmittance radial basis function neural network (PT-RBF) stands out, especially when operating in noisy environments such as 5G MIMO systems. Despite its capabilities, achieving convergence in multi-layered, multi-input, and multi-output PT-RBFs remains a daunting challenge. Addressing this gap, this paper presents a novel Deep PT-RBF parameter initialization technique. Through rigorous simulations conforming to 3GPP TS 38 standards, our method not only outperforms conventional initialization strategies like random, $K$-means, and constellation-based methods but is also the only approach to achieve successful convergence in deep PT-RBF architectures. These findings pave the way to more robust and efficient neural network deployments in complex digital communication systems.