Hasil untuk "Transportation and communications"

B. Ai, Xiang Cheng, T. Kürner et al.

Xiuchen Xu, Zhen Wang, Zhaoyan Sheng et al.

Portable concrete barriers (PCBs) are modular structures widely used to separate traffic lanes from construction zones. However, these barriers often exhibit insufficient structural safety to effectively block intruding vehicles, leading to severe casualties. Appropriate structural design and experimental verification are required to prevent damage to the PCB structure. Existing domestic specifications lack explicit provisions regarding the structural design and load-bearing capacity of PCBs, presenting a critical bottleneck for the engineering application of temporary traffic barriers. To mitigate this limitation, a novel PCB was developed featuring a pre-embedded rectangular steel tube and I-beam joint, coupled with a single-slope cross-section (1200 mm in height), to satisfy the SB-level safety performance criteria specified in relevant standards. Distinct from conventional finite element simulation approaches, two full-scale vehicle impact tests were subsequently performed to verify the structural safety of the barrier. Experimental results demonstrate that the newly designed barrier can withstand vehicle impacts, meeting the SB protection level, with its containment, redirection, and energy absorption capabilities complying with relevant specification criteria. This study is anticipated to establish a foundational dataset for subsequent research in the field.

Hadiseh Rezaei, Rahim Taheri, Ehsan Nowroozi et al.

The rapid growth of Internet of Things (IoT) devices and the introduction of 5G networks have created new opportunities for enhancing network services, while also introducing significant security concerns. Intrusion Detection Systems (IDS) are crucial for identifying malicious activities and unauthorized access in these environments. However, current IDS solutions face challenges such as sharing sensitive data and managing large-scale networks. Federated Learning (FL) presents a promising solution by enabling models to be trained on decentralized devices without sharing private data. This paper examines how FL can enhance IDS for IoT and 5G networks, with an emphasis on privacy and security concerns. We analyze various privacy, homomorphic encryption, and security mechanisms in FL, including Differential Privacy (DP) and secure aggregation, and their potential applications in strengthening IDS solutions. Additionally, we explore how FL contributes to the development of more secure and efficient IDS systems while addressing challenges such as data heterogeneity and security risks. Finally, we identify gaps in the existing research and propose directions for future work to enhance the robustness and practicality of FL-based IDS for IoT and 5G environments.

Ainna Yue Moreno-Locubiche, Josep Vidal, Antonio Pascual-Iserte et al.

This paper proposes the joint design of reconfigurable intelligent surfaces (RIS) and zero-forcing (ZF) precoding for the downlink (DL) multiuser multiple-input single-output (MU-MISO) setup in millimeter-wave (mmWave) bands, where ZF is particularly attractive due to its ability to suppress inter-user interference by exploiting the large antenna arrays and sparse directional channels characteristic of mmWave systems. This ensures efficient spatial multiplexing with manageable complexity, making ZF a practical and in modern 5G/6G deployments. However, a careful design is necessary to overcome potential rank deficiency in the channel matrix. For the MU-MISO case, rank deficiency may arise if users exhibit significantly different channel gains or if, being in far-field, they are aligned with the position of the transmitter. On the other hand, the deployment of a RIS introduces artificial scattering which can shape the radio environment to address those situations. We explore the joint design under perfect channel knowledge, assess the impact of imperfect channel estimation on the bit error rate (BER) and propose a robust design of pilot transmissions that equalizes multiuser interference across users in the presence of channel errors in the precoder design. This evaluation shows the advantages of optimized RIS-aided ZF MU-MISO communication for the DL of wireless systems.

Lu Gao, Pan Lu, Fengxiang Qiao et al.

The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%-15% lower than the pre-pandemic level for at least one or two years.

Xiangjun Cheng, Liuqing Yang, X. Shen

Michail A. Makridis, Shaimaa K. El-Baklish, Anastasios Kouvelas et al.

Rushan Wang, Yanan Xin, Yatao Zhang et al.

Marcel Moran

Starting in 2017, a new public demonstration called "People Protected Bike Lanes" (PPBL) took place in San Francisco, in which participants stand on the street in a line between cyclists and car traffic to demonstrate the inadequacy of existing bicycle infrastructure. Between 2017-2023, there were at least 55 PPBL demonstrations in 25 different cities in 11 countries. At more than half of the streets in which PPBL were held, protected bike lanes were later installed. PPBL's straightforward arrangement, legibility to news media, and participants' use of online social networks have likely contributed to its considerable global spread, and influence on bike-lane upgrades.

MAO Quan, ZHENG Junqiu, MA Hui et al.

Asphalt pavement is prone to fatigue cracking during long-term service, which can lead to secondary diseases and weaken the structural durability. For in-depth revelation of the fatigue damage behavior of asphalt mixtures, in this paper, a semi-circular bending (SCB) mesoscopic model was established based on the discrete element method. The fatigue fracture behavior of three typical asphalt mixtures in Jiangsu Province’s expressways, including SMA-13, SUP-20, and SUP-25, was simulated under different stress ratio conditions. The fatigue damage mechanism and evolution law of asphalt mixtures were analyzed from the aspects of fatigue life, crack propagation, and residual strength. The results show that all three mixtures exhibit typical three-stage fatigue damage characteristics, and the increase of stress ratio will significantly shorten the fatigue life and accelerate the crack development. SMA-13 shows excellent fatigue resistance due to its dense skeleton and modified asphalt mortar, followed by SUP-20, while SUP-25 performs the worst due to insufficient skeleton constraint and low mortar toughness. Meanwhile, the crack propagation paths and residual strength attenuation laws of different mixtures are significantly different, further revealing the key role of skeleton structure and asphalt mortar performance in the fatigue damage mechanism.

Yufeng Diao, Yichi Zhang, Changyang She et al.

Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates task-oriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution.

Francesco Nardo, Jan Tepper, Ricardo Barrios et al.

Optical pin beams (OPBs) are a promising candidate for realizing turbulence-resilient long-distance free-space optical communication links spanning hundreds of kilometers. In this work, we introduce a unified theoretical model to describe the propagation of OPBs and present comprehensive simulation results based on many realizations and link-budget analyses for constant turbulence strengths. For reference, we compare the performance of the OPBs to weakly diverging and focusing Gaussian beams. For a 100km long air-to-air link, 10km above sea level, our simulation results show that OPBs offer an improved link budget of up to 8.6dB and enhanced beam wander statistics of up to 3dB compared to the considered Gaussian beams. Additionally, we identified a quadratic relationship between the transmitter aperture diameter and the maximum achievable distances, which is crucial in deciding the suitability of OPBs for a given application scenario.

Minze Chen, Tianqi Mao, Yang Zhao et al.

The RYDberg Atomic Receiver (RYDAR) has been demonstrated to surmount the limitation on both the sensitivity and operating bandwidth of the classical electronic counterpart, which can theoretically detect indiscernible electric signals below -174 dBm/Hz with optical measurement through Rydberg-state atoms. Such miracle has established a new quantum-based paradigm for communications and sensing, which motivates a revolution of the transceiver design philosophies to fully unleash the potential of RYDAR towards next-generation networks. Against this background, this article provides a thorough investigation of Rydberg atomic communications and sensing from theory to hardware implementations. Specifically, we highlight the great opportunities from the hybridization between the RYDAR and the cutting-edge integrated sensing and communication (ISAC), followed by essential preliminaries of the quantum-based receiver. Then we propose a theoretical framework for broadband ISAC based on RYDAR, demonstrated by the proof-of-concept experiments. Afterwards, the enabling technologies for the ISAC framework are explored ranging from channel characterization, waveform design to array-based receiver configurations, where the open problems are also summarized. Finally, the future applications of RYDAR-based ISAC are envisioned, indicating its significant potential for both civilian and military purposes.

Fatih Gulec, Hamdan Awan, Nigel Wallbridge et al.

Smart agriculture applications, integrating technologies like the Internet of Things and machine learning/artificial intelligence (ML/AI) into agriculture, hold promise to address modern challenges of rising food demand, environmental pollution, and water scarcity. Alongside the concept of the phytobiome, which defines the area including the plant, its environment, and associated organisms, and the recent emergence of molecular communication (MC), there exists an important opportunity to advance agricultural science and practice using communication theory. In this article, we motivate to use the communication engineering perspective for developing a holistic understanding of the phytobiome communication and bridge the gap between the phytobiome communication and smart agriculture. Firstly, an overview of phytobiome communication via molecular and electrophysiological signals is presented and a multi-scale framework modeling the phytobiome as a communication network is conceptualized. Then, how this framework is used to model electrophysiological signals is demonstrated with plant experiments. Furthermore, possible smart agriculture applications, such as smart irrigation and targeted delivery of agrochemicals, through engineering the phytobiome communication are proposed. These applications merge ML/AI methods with the Internet of Bio-Nano-Things enabled by MC and pave the way towards more efficient, sustainable, and eco-friendly agricultural production. Finally, the implementation challenges, open research issues, and industrial outlook for these applications are discussed.

Guanpeng Dong, Frank Witlox, Yiming Bie

Shahnaz N. Fuady, Paul C. Pfaffenbichler, Yusak O. Susilo

Xiaohe Li, Yuquan Du, Yanyu Chen et al.

Hongsheng Qi

The safety of autonomous vehicles (AVs) is a critical consideration for their widespread adoption. Responsibility sensitive safety (RSS) is proposed to serve as a model checking tool for AV safety. However, RSS alone cannot guarantee safety when they are mixed with human-driven vehicles (HDVs). These HDVs may disregard safety rules, creating dilemmas for AVs where they must choose between crashing into their leader or crashing into their follower. This manuscript defines this dilemma regarding the longitudinal driving and extends it to platooning scenarios with an arbitrary number of vehicles, referred to as polylemma. In polylemma, a violation of safety rules by one vehicle inevitably results in at least one crash between neighboring vehicles. To avoid the polylemma scenario, the manuscript proposes a human error-tolerant (HET) driving strategy, wherein AVs maintain an additional gap and prepare for moderate deceleration to account for potential errors by human drivers. The manuscript derives the risk reduction and capacity variation resulting from the implementation of this strategy at a given market penetration rate (MPR) using real world trajectory data. The analysis indicates that a 50% MPR would reduce risks due to human error by 80%, with a decrease in capacity which vary different for background traffic flow speed.

Ria Bhargava, Ruolin Zhang, Eleftheria Kontou

Electric vehicles (EVs) have a growing market share in the US and several policies promote their ownership and use. EV drivers face significantly longer charging times at public charging stations, compared to internal combustion engine vehicle drivers. When charging stations are colocated with other land uses, such as shopping and recreational ones, EV drivers can plan their trips and turn their charging down time to a productive or enjoyable experience. This study examines the variety of businesses and premises located near public charging stations in Chicago, Illinois, and explores their pricing levels, which may influence the accessibility of charging stations for EV users from diverse socio-economic backgrounds. Our findings reveal that the businesses closest to charging stations predominantly include food establishments, shops, and tourism-related venues, with the majority falling into affordable pricing categories. These results suggest that public charging stations in Chicago are situated in areas with amenities that cater to a wide range of income levels, promoting equitable access to EV infrastructure. On the other hand, chargers are overwhelmingly placed in majority white neighborhoods. We highlight the importance of considering both amenities proximity and affordability in the assessment of public charging infrastructure to support a more inclusive and equitable transition to electric vehicles.

Mohammad Shehab, Mustafa Kishk, Maurilio Matracia et al.

Civilian communication during disasters such as earthquakes, floods, and military conflicts is crucial for saving lives. Nevertheless, several challenges exist during these circumstances such as the destruction of cellular communication and electricity infrastructure, lack of line of sight (LoS), and difficulty of localization under the rubble. In this article, we discuss key enablers that can boost communication during disasters, namely, satellite and aerial platforms, redundancy, silencing, and sustainable networks aided with wireless energy transfer (WET). The article also highlights how these solutions can be implemented in order to solve the failure of communication during disasters. Finally, it sheds light on unresolved challenges, as well as future research directions.