Hasil untuk "Transportation and communication"

T. R. Mahesh, G. Gangadevi, Kritika Kumari Mishra et al.

Intelligent transportation systems safety and reliability rely on anomaly detection in vehicular ad hoc networks (VANETs). Robust detection methods with learning and response capabilities to different vehicle behaviors and potential security risks are required for the dynamic and complex nature of VANET communications. This research brings methodological innovation into the area through a HyperLSTM model, an extension of the standard LSTM network optimized to deal with increased complexity and flexibility, specifically engineered to cope with the complexity of VANET data. Standard LSTM models are usually poor in high-dimensional time-series data where the HyperLSTM structure is specifically engineered for detecting temporal relationships and anomalies. Two of the primary methodological contributions are a dropout technique for increasing generalizability and the application of overlapping windows for streams of real-time data. The proposed HyperLSTM model surpasses the existing methodology of machine learning and deep learning, which often struggles with accuracy varying from 80% to 95% with an accuracy of 98%. With the error metrics of 0.0649 mean squared error (MSE), 0.2547 root mean squared error (RMSE), and 0.0649 mean absolute error (MAE), the HyperLSTM model achieved significant performance values. This research introduces a dynamically adaptive HyperLSTM framework that extends conventional LSTM capabilities, specifically designed to capture the complex spatial-temporal patterns inherent in VANET communications, which have been less explored in previous studies. This study demonstrates how efficiently HyperLSTM networks leverage VANET anomaly detection, thus enhancing methodological efficiency in managing the complexity and diversity of vehicle network data. Of great significance for future generations of vehicle communication systems, the findings prompt the incorporation of HyperLSTM models into future VANET security systems, thus enhancing detection efficiency.

Wethaya Faijaidee, Sajjakaj Jomnonkwao, Pornsiri Jongkol

<i>Background:</i> The increasing global focus on sustainability has made Green Supply Chain Management (GSCM) a critical strategy for businesses to balance environmental responsibility with operational efficiency. Despite its benefits, GSCM adoption in developing countries faces significant challenges. This study addresses the gap by investigating these barriers within the Thai context, providing actionable insights for policymakers and businesses. <i>Methods:</i> A mixed-methods approach was employed, including a survey of 480 business owners, executives, and supply chain employees, and expert analysis using Interpretive Structural Modeling (ISM). The ISM technique was used to determine the relationships and hierarchy among key barriers to GSCM implementation. <i>Results:</i> The findings reveal that weak legal frameworks, insufficient supplier engagement, and a lack of social responsibility are the most significant barriers. Secondary factors, such as low consumer demand and minimal competitive pressure, exacerbate these challenges. The ISM analysis highlighted the cascading effects of foundational barriers on other dimensions of GSCM adoption. <i>Conclusions:</i> Strengthening environmental regulations, promoting supplier collaboration, and embedding sustainability in corporate culture are key to overcoming GSCM barriers to sustainably enhance Thailand’s competitiveness.

Jiawei Xue, Ruichen Tan, Jianzhu Ma et al.

Data mining in transportation networks (DMTNs) refers to using diverse types of spatio-temporal data for various transportation tasks, including pattern analysis, traffic prediction, and traffic controls. Graph neural networks (GNNs) are essential in many DMTN problems due to their capability to represent spatial correlations between entities. Between 2016 and 2024, the notable applications of GNNs in DMTNs have extended to multiple fields such as traffic prediction and operation. However, existing reviews have primarily focused on traffic prediction tasks. To fill this gap, this study provides a timely and insightful summary of GNNs in DMTNs, highlighting new progress in prediction and operation from academic and industry perspectives since 2023. First, we present and analyze various DMTN problems, followed by classical and recent GNN models. Second, we delve into key works in three areas: (1) traffic prediction, (2) traffic operation, and (3) industry involvement, such as Google Maps, Amap, and Baidu Maps. Along these directions, we discuss new research opportunities based on the significance of transportation problems and data availability. Finally, we compile resources such as data, code, and other learning materials to foster interdisciplinary communication. This review, driven by recent trends in GNNs in DMTN studies since 2023, could democratize abundant datasets and efficient GNN methods for various transportation problems including prediction and operation.

Minghao Mou, Junjie Qin

Transportation electrification introduces strong coupling between the power and transportation systems. In this paper, we generalize the classical notion of Braess' paradox to coupled power and transportation systems, and examine how the cross-system coupling induces new types of Braess' paradoxes. To this end, we model the power and transportation networks as graphs, coupled with charging points connecting to nodes in both graphs. The power system operation is characterized by the economic dispatch optimization, while the transportation system user equilibrium models travelers' route and charging choices. By analyzing simple coupled systems, we demonstrate that capacity expansion in either transportation or power system can deteriorate the performance of both systems, and uncover the fundamental mechanisms for such new Braess' paradoxes to occur. We also provide necessary and sufficient conditions of the occurrences of Braess' paradoxes for general coupled systems, leading to managerial insights for infrastructure planners. For general networks, through characterizing the generalized user equilibrium of the coupled systems, we develop efficient algorithms to detect Braess' paradoxes and novel charging pricing policies to mitigate them.

R. Tamassia

Yashar Salami, Vahid Khajehvand, Esmaeil Zeinali

Abstract The Internet of Vehicles (IoV) plays a crucial role in advancing intelligent transportation systems. However, due to limited processing power, IoV faces challenges in independently handling large volumes of data, necessitating the use of offloading as a solution. Offloading data in wireless environments raises security concerns, highlighting the need for robust data protection mechanisms. This study introduces a secure offloading (SO) scheme within the Fog-Cloud Federation for IoV. The proposed NSO-VFC scheme undergoes both informal and formal analysis using the Avispa tool, demonstrating resilience against active and passive attacks. Performance evaluations indicate that the security measures of NSO-VFC meet acceptable standards compared to similar approaches. Nonetheless, the heightened focus on security incurs higher computational and communication costs than alternative strategies. Simulation experiments using the NS3 tool involve varying numbers of IoVs (50, 70, and 100), revealing that increased IoV density correlates with enhanced packet delivery rates and throughput within the NSO-VFC scheme.

LI Yongjie, LU Jizhao, WU Chenguang et al.

【Objective】In response to the needs of Ultra High Voltage (UHV) site infrastructure control and intelligent transportation and inspection, 5^th Generation Mobile Communication Technology (5G) is applied to the construction phase of UHV converter station for the first time in China.【Methods】On the basis of realizing 5G full coverage of Henan southern converter station, 5G virtual private network of converter station is built and the Mobile Edge Computing (MEC) cloud platform is deployed. Fully considering the needs of early infrastructure and late operation, the intelligent infrastructure site is connected with the intelligent converter station in the later stage. Relying on the advantages of 5G and integrating Artificial Intelligence (AI) and other technologies, various types of data on the construction site of UHV infrastructure can be collected in real-time and unified, providing multi-functional 5G solutions for remote video monitoring of UHV infrastructure, engineering monitoring and early warning, panoramic presentation of infrastructure site, identification of safety hazards, and expert remote assistance guidance. 5G applications such as smart construction site, intelligent inspection and intelligent control are organized and 5G network adaptation test is carried out.【Results】Providing a flexible, large-bandwidth and high-speed communication methods of 5G for the business application of Henan southern converter station. It effectively enables various business requirements in the UHV converter station, verifying the carrying capacity of 5G for UHV construction and inspection services. It also improves the management efficiency, and reduces the operation and maintenance cost.【Conclusion】The successful application of 5G in the Henan southern UHV converter substation provides a typical UHV substation solution of 5G+ energy Internet, which verifies the feasibility of 5G in the power industry and also promotes the application of 5G in power scenarios.

Sisi Hu, Ciera E Kirkpatrick, Namyeon Lee et al.

BackgroundRecruiting participants for clinical trials poses challenges. Major barriers to participation include psychological factors (eg, fear and mistrust) and logistical constraints (eg, transportation, cost, and scheduling). The strategic design of clinical trial messaging can help overcome these barriers. While strategic communication can be done through various channels (eg, recruitment advertisements), health care providers on the internet have been found to be key sources for communicating clinical trial information to US adults in the social media era. ObjectiveThis study aims to examine how communication source (ie, medical doctors and peers) and message framing of TikTok videos (ie, psychological and logistical framing) influence clinical trial–related attitudes, perceptions, and sign-up behaviors under the guidance of the integrated behavioral model. MethodsThis study used a 2 (source: doctor vs peer) × 2 (framing: psychological vs logistical) between-participant factorial design web-based experiment targeting adults in the United States who had never participated in clinical trials (ie, newcomers). A Qualtrics panel was used to recruit and compensate the study respondents (n=561). Participants viewed short-form videos with doctors or peers, using psychological or logistical framing. The main outcome measures included perceived source credibility, self-efficacy, attitude toward clinical trial participation, behavioral intention, and sign-up behavior. Structural equation modeling was used to analyze the direct and indirect effects of message factors on the outcome variables. Source (doctor=1; peer=0) and framing (psychological=1; logistical=0) were dummy-coded. ResultsDoctor-featured messages led to greater perceived source credibility (β=.31, P<.001), leading to greater self-efficacy (95% CI 0.13-0.30), which in turn enhanced behavioral intention (95% CI 0.12-0.29) and clinical trial sign-up behavior (95% CI 0.02-0.04). Logistical barrier–framed messages led to greater self-efficacy (β=–.09, P=.02), resulting in higher intention to participate in clinical trials (95% CI –0.38 to –0.03) and improved sign-up behavior (95% CI –0.06 to –0.004). Logistical barrier–framed messages were also directly associated with an increased likelihood of signing up for a clinical trial (β=–.08, P=.03). The model accounted for 21% of the variance in clinical trial sign-up behavior. Attitude did not significantly affect behavioral intention in this study (β=.08, P=.14), and psychological and logistical barrier–framed messages did not significantly differ in attitudes toward clinical trial participation (β=–.04, P=.09). ConclusionsThese findings advance our understanding of how people process popular message characteristics in short-form videos and lend practical guidance for communicators. We encourage medical professionals to consider short-form video sites (eg, TikTok and Instagram Reels) as effective tools for discussing clinical trials and participation opportunities. Specifically, featuring doctors discussing efforts to reduce logistical barriers is recommended. Our measuring of actual behavior as an outcome is a rare and noteworthy contribution to this research.

Xuehan Li, Minghao Zhu, Boyang Zhang et al.

In recent years, the global surge of High-speed Railway (HSR) revolutionized ground transportation, providing secure, comfortable, and punctual services. The next-gen HSR, fueled by emerging services like video surveillance, emergency communication, and real-time scheduling, demands advanced capabilities in real-time perception, automated driving, and digitized services, which accelerate the integration and application of Artificial Intelligence (AI) in the HSR system. This paper first provides a brief overview of AI, covering its origin, evolution, and breakthrough applications. A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system: mechanical manufacturing and electrical control, communication and signal control, and transportation management. The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components, forecast of railroad maintenance, optimization of energy consumption in railroads and trains, communication security, communication dependability, channel modeling and estimation, passenger scheduling, traffic flow forecasting, high-speed railway smart platform. Finally, challenges associated with the application of AI are discussed, offering insights for future research directions.

Vineeth Nandhini, Hiremath Harshit, Gagan Bhushith et al.

Road traffic in metropolitan cities is increasing at enormous rates resulting in congestion. Vehicles rendering emergency services like ambulances, fire engines, law enforcement vehicles, etc., act as lifelines and should be looked into with the highest priorities on the road. Such emergency vehicles (EmVs / EVs) are seen stuck many times in traffic, especially during peak hours of the day. The vehicles that block the EmVs on the road are unaware of the arrival of the same. Hence this work proposes a system that uses a central server that receives the location of the EmV and shares it with the civilian vehicles around. This is achieved through pinpointed accuracy systems like the Indian Regional Navigation Satellite System (IRNSS), Global Positioning System (GPS), and Global System for Mobile Communication (GSM), etc., The objective here is to help the EmVs reach their target location earlier thus saving lives.

Shaowei Ying, Zhenlong Li, Manzhu Yu

The resurgence and rapid advancement of Generative Artificial Intelligence (GenAI) in 2023 has catalyzed transformative shifts across numerous industry sectors, including urban transportation and logistics. This study investigates the evaluation of Large Language Models (LLMs), specifically GPT-4 and Phi-3-mini, to enhance transportation planning. The study assesses the performance and spatial comprehension of these models through a transportation-informed evaluation framework that includes general geospatial skills, general transportation domain skills, and real-world transportation problem-solving. Utilizing a mixed-methods approach, the research encompasses an evaluation of the LLMs' general Geographic Information System (GIS) skills, general transportation domain knowledge as well as abilities to support human decision-making in the real-world transportation planning scenarios of congestion pricing. Results indicate that GPT-4 demonstrates superior accuracy and reliability across various GIS and transportation-specific tasks compared to Phi-3-mini, highlighting its potential as a robust tool for transportation planners. Nonetheless, Phi-3-mini exhibits competence in specific analytical scenarios, suggesting its utility in resource-constrained environments. The findings underscore the transformative potential of GenAI technologies in urban transportation planning. Future work could explore the application of newer LLMs and the impact of Retrieval-Augmented Generation (RAG) techniques, on a broader set of real-world transportation planning and operations challenges, to deepen the integration of advanced AI models in transportation management practices.

Peng Wang, Xiang Wei, Fangxu Hu et al.

Natural language processing (NLP) is a key component of intelligent transportation systems (ITS), but it faces many challenges in the transportation domain, such as domain-specific knowledge and data, and multi-modal inputs and outputs. This paper presents TransGPT, a novel (multi-modal) large language model for the transportation domain, which consists of two independent variants: TransGPT-SM for single-modal data and TransGPT-MM for multi-modal data. TransGPT-SM is finetuned on a single-modal Transportation dataset (STD) that contains textual data from various sources in the transportation domain. TransGPT-MM is finetuned on a multi-modal Transportation dataset (MTD) that we manually collected from three areas of the transportation domain: driving tests, traffic signs, and landmarks. We evaluate TransGPT on several benchmark datasets for different tasks in the transportation domain, and show that it outperforms baseline models on most tasks. We also showcase the potential applications of TransGPT for traffic analysis and modeling, such as generating synthetic traffic scenarios, explaining traffic phenomena, answering traffic-related questions, providing traffic recommendations, and generating traffic reports. This work advances the state-of-the-art of NLP in the transportation domain and provides a useful tool for ITS researchers and practitioners.

Md Anowar Hossain, J. Tanimoto

Abstract In this study, we propose an information-sharing traffic flow model by considering multiple preceding cars and system time delay effect to reproduce a more likely flow field given the dissemination of intelligent transportation systems with wireless communication. The flow field would be robustly stable and efficient if the information on each vehicle’s dynamics could be shared without time delay. However, a realistic situation inevitably entails some time delay, resulting from mechanical and control systems. The proposed model is validated by the neutral stability condition through linear stability theory, thus confirming that the proposed model substantially increases the stability of a traffic flow field compared with the conventional full velocity difference model (optimal velocity model). The modified Korteweg–de Vries equation is derived and analyzed for nonlinear analysis. A numerical simulation is also conducted to justify the proposed model.

Dajun Zhang, F. Richard Yu, Ruizhe Yang et al.

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep $Q$ -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ( $ETX$ ) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.

Ziran Wang, Xishun Liao, Xuanpeng Zhao et al.

Digital twin, an emerging representation of cyberphysical systems, has attracted increasing attentions very recently. It opens the way to real-time monitoring and synchronization of real-world activities with the virtual counterparts. In this study, we develop a digital twin paradigm using an advanced driver assistance system (ADAS) for connected vehicles. By leveraging vehicle-to-cloud (V2C) communication, on-board devices can upload the data to the server through cellular network. The server creates a virtual world based on the received data, processes them with the proposed models, and sends them back to the connected vehicles. Drivers can benefit from this V2C based ADAS, even if all computations are conducted on the cloud. The cooperative ramp merging case study is conducted, and the field implementation results show the proposed digital twin framework can benefit the transportation systems regarding mobility and environmental sustainability with acceptable communication delays and packet losses.

J. Elíasson

During the pandemic, passenger transport has decreased dramatically due to restrictions and recommendations to avoid social contacts. Hopes and expectations have been raised that experiences, habits and improved digital services developed or discovered during the pandemic can lead to a permanent decrease of travel volumes even in the long run, thereby reducing emissions, noise and congestion. This paper discusses this question, based on descriptive analyses of historical development of travel distances and travel times in Sweden, including a description of how transportation changed in Sweden during the pandemic. Obviously, it is too early to give a conclusive answer regarding long run effects, but judging from historical experiences of previous improvements in transportation and communication, it seems unlikely that increased digital experience, improved digital services or changed habits will lead to permanently reduced travel volumes. It appears more likely that improved digital services and increased digital maturity will continue to transform work, shopping and leisure, but that this will not translate into decreased physical travel to any large extent.

Ali Mohaghar, Hojjat Heydarzadeh Moghaddam, Rohollah Ghasemi

Roadside units are crucial elements of intelligent transportation systems that provide vehicle–vehicle and vehicle–equipment information communication. Due to the high cost of installation, the deployment of roadside units is the most critical. Aim of this study is developing a model to optimize of roadside units placement to achieve maximum coverage. A multi–objective mathematical model presented, based on the three main parameters. These parameters are traffic volume, incident rate and adjacency to important centers, which determine for alternative points. The maximum coverage problem is NP–hard. Consequently, conventional mathematical methods are not accurate for large scale problem. A meta–heuristic method based on the greedy algorithm was developed which conciders marking points as definitive–select or non–selectable. Result of the model were evaluated through testing of three scenarios, 200, 500 and 1000 meters coverage in District 5 of Tehran by using MATLAB and the best one, 1000 meters was chosen with 71% coverage. Observations showed the effect of various parameters such as equipment coverage radius, number of equipment and budget on the results of distribution. This algorithm makes it possible to solve the problem on a large scale by using the geolocation of the candidate points.

Jiawei Tian, SangHoon An, Azharul Islam et al.

The accelerated growth of 5G technology has facilitated substantial progress in the realm of vehicle-to-everything (V2X) communications. Consequently, achieving optimal network performance and addressing congestion-related challenges have become paramount. This research proposes a unique hybrid power and rate control management strategy for distributed congestion control (HPR-DCC) focusing on 5G-NR-V2X sidelink communications. The primary objective of this strategy is to enhance network performance while simultaneously preventing congestion. By implementing the HPR-DCC strategy, a more fine-grained and adaptive control over the transmit power and transmission rate can be achieved. This enables efficient control by dynamically adjusting transmission parameters based on the network conditions. This study outlines the system model and methodology used to develop the HPR-DCC algorithm and investigates its characteristics of stability and convergence. Simulation results indicate that the proposed method effectively controls the maximum CBR value at 64% during high congestion scenarios, which leads to a 6% performance improvement over the conventional DCC approach. Furthermore, this approach enhances the signal reception range by 20 m, while maintaining the 90% packet reception ratio (PRR). The proposed HPR-DCC contributes to optimizing the quality and reliability of 5G-NR-V2X sidelink communication and holds great promise for advancing V2X applications in intelligent transportation systems.

J. Jobe, Greg P. Griffin

Bike sharing can leverage its physical distancing advantages for responding to the COVID-19 pandemic, but system management and communication are essential to support healthy transportation. This study addresses the need to understand the range of bike share systems’ responses to the pandemic by reviewing bike share system cases in the United States and reports survey responses from bike share users in San Antonio (TX). Five out of eleven bike share systems communicated their responses to the pandemic online at the time of review. 43% of survey respondents who were unemployed due to the pandemic reported increasing use of the bike share system, whereas 36% of employed respondents decreased ridership. Most respondents were unaware of the bike share operator’s steps to control the spread of COVID-19 for users. Moderate-frequency riders (1–2 times per month) may increase bike sharing the most after Coronavirus restrictions are lifted, from 22% of respondents to 34%. Based on our findings, we suggest bike share operators should expand communication efforts about policies and actions to support community health, explore how to serve unemployed and low-income communities best, and prepare for the equitable expansion of ridership following the pandemic.

Krzysztof Gryz, Jolanta Karpowicz, Patryk Zradziński

The electromagnetic field (EMF) in electric vehicles (EVs) affects not only drivers, but also passengers (using EVs daily) and electronic devices inside. This article summarizes the measurement methods applicable in studies of complex EMF in EVs focused on the evaluation of characteristics of such exposure to EVs users and drivers, together with the results of investigations into the static magnetic field (SMF), the extremely low-frequency magnetic field (ELF) and radiofrequency (RF) EMF related to the use of the EVs in urban transportation. The investigated EMF components comply separately with limits provided by international labor law and guidelines regarding the evaluation of human short-term exposure; however other issues need attention—electromagnetic immunity of electronic devices and long-term human exposure. The strongest EMF was found in the vicinity of direct current (DC) charging installations—SMF up to 0.2 mT and ELF magnetic field up to 100 µT—and inside the EVs—up to 30 µT close to its internal electrical equipment. Exposure to RF EMF inside the EVs (up to a few V/m) was found and recognized to be emitted from outdoor radiocommunications systems, together with emissions from sources used inside vehicles, such as passenger mobile communication handsets and antennas of Wi-Fi routers.