Hasil untuk "Transportation and communication"

M. Jafari, Abdollah Kavousi-fard, Tao Chen et al.

With recent advances in information and communication technology (ICT), the bleeding edge concept of digital twin (DT) has enticed the attention of many researchers to revolutionize the entire modern industries. DT concept refers to a digital representation of a physical entity that is able to reflect its physical behavior by applying platforms and bidirectional interaction of data in real-time. The remarkable deployment of the internet of things in the power grid has led to reliable access to information that improves its performance and equips it with a powerful tool for real-time data management and analysis. This paper aims to trace the continuous investigation and propose practical ideas in originating and developing DT technology, according to various application domains of power systems, and also describes the proposed solutions to deal with the challenges associated with DT. Indeed, with the development of modern cities, different energy layers such as transportation systems, smart grids, and microgrids have emerged facing various issues that challenge the multi-dimensional energy management system. For example, in transportation systems, traffic is a major problem that requires real-time management, planning, and analysis. In power grids, remote data transfer within the grid and also various analyzes needing real data are just some of the current challenges in the field. These problems can be cracked by providing and analyzing a real twin framework in each section. All in all, this paper aims to survey different applications of DT in the development of the various aspects of energy management within a city including transportation systems, power grids, and microgrids. Besides, the security of DT technology based on ML is discussed. It also provides a complete view for the readers to be able to develop and deploy a DT technology for various power system applications.

Xiaokang Zhou, Wei Liang, Akira Kawai et al.

The proliferation of advanced embedded and communication technologies has facilitated the possibility of modern Intelligent Transportation System (ITS). The hierarchical nature of such large-scale and distributed systems brings obvious challenges in creating a scalable and sustainable computing environment, and hence the development and application of edge intelligence become critical. Federated learning (FL), as an emerging distributed machine learning paradigm, aims to offer secure knowledge sharing and effective learning across multiple devices. However, conventional FL may fall into trouble when facing large-scale and network-agnostic systems with fast moving devices and changing network attributes. In this study, we propose an Adaptive Segmentation enhanced Asynchronous Federated Learning (AS-AFL) model, aiming to improve the learning efficiency and reliability in sustainable ITS via a decentralized fashion. Specifically, a meta-learning based adaptive segmentation scheme is designed to automatically separate the client nodes (e.g., vehicles) into multiple edge groups according to their homogeneous attributes. An integrated aggregation mechanism is then developed to realize the horizontal FL among a group of similar client nodes via the so-called intra-group synchronous aggregation, while allowing the vertical FL across different groups via the so-called inter-group asynchronous aggregation. Experiment and evaluation results based on an open-source dataset demonstrate the outstanding learning and communication performance of our proposed model, compared with several conventional FL schemes in a distributed ITS application scenario.

D. Sarwatt, Yujia Lin, Jianguo Ding et al.

Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and their specific applications in the context of ITS. Real-world case studies, research projects, and initiatives are compiled to showcase the metaverse’s potential for ITS. It also examines the societal, economic, and technological implications of metaverse integration in ITS and highlights the associated integration challenges. Lastly, future research directions are identified to unlock the metaverse’s full potential in enhancing transportation systems.

Meenakshi Maindola, R. Al-Fatlawy, A. Badhoutiya et al.

The notion of smart transportation is an extension of the basic idea of intelligent transportation. It accumulates traffic data using high-tech means and then performs extensive statistical analysis and model creation to realize the system's methodical, real-time, and participatory qualities. The use of artificial intelligence, as well as data modeling technologies, allows the design and functioning of a transportation system to be visually viewed and anticipated. With the integration of these key innovations, an integrated smart transportation design and operations system will be progressively built, examining every aspect of traffic congestion and offering dependable analysis outcomes that will be utilized in all facets of transportation-related decision-making and citizen assistance. This study seeks to comprehend the use of Intelligent Transportation Systems (ITS) as an alternative to present traffic management practices. The topic of ITS and its components has been discussed. This paper, in this instance, offers a comprehensive review of the literature that breaks down current IoT-based smart transport systems, particularly in terms of security on the road. The present state of IoT-based smart transport solutions for better road safety is thus provided. The Internet of Things (IoT) facilitates and promotes the effective coordination of real-world transportation systems with their simulated counterparts in cyberspace.

Piera Centobelli, Roberto Cerchione, E. Esposito

Xiaocai Zhang, Hur Lim, Ke Wang et al.

In this study, a modular, data-free pipeline for multi-label intention recognition is proposed for agentic AI applications in transportation. Unlike traditional intent recognition systems that depend on large, annotated corpora and often struggle with fine-grained, multi-label discrimination, our approach eliminates the need for costly data collection while enhancing the accuracy of multi-label intention understanding. Specifically, the overall pipeline, named DMTC, consists of three steps: 1) using prompt engineering to guide large language models (LLMs) to generate diverse synthetic queries in different transport scenarios; 2) encoding each textual query with a Sentence-T5 model to obtain compact semantic embeddings; 3) training a lightweight classifier using a novel online focal-contrastive (OFC) loss that emphasizes hard samples and maximizes inter-class separability. The applicability of the proposed pipeline is demonstrated in an agentic AI application in the maritime transportation context. Extensive experiments show that DMTC achieves a Hamming loss of 5.35% and an AUC of 95.92%, outperforming state-of-the-art multi-label classifiers and recent end-to-end SOTA LLM-based baselines. Further analysis reveals that Sentence-T5 embeddings improve subset accuracy by at least 3.29% over alternative encoders, and integrating the OFC loss yields an additional 0.98% gain compared to standard contrastive objectives. In conclusion, our system seamlessly routes user queries to task-specific modules (e.g., ETA information, traffic risk evaluation, and other typical scenarios in the transportation domain), laying the groundwork for fully autonomous, intention-aware agents without costly manual labelling.

Ostonya Thomas, Muhaimin Bin Munir, Jean-Michel Tine et al.

Technological advancements have revolutionized numerous industries, including transportation. While digitalization, automation, and connectivity have enhanced safety and efficiency, they have also introduced new vulnerabilities. With 95% of data breaches attributed to human error, promoting cybersecurity awareness in transportation is increasingly critical. Despite numerous cyberattacks on transportation systems worldwide, comprehensive and centralized records of these incidents remain scarce. To address this gap and enhance cyber awareness, this paper presents a large language model (LLM) based approach to extract and organize transportation related cyber incidents from publicly available datasets. A key contribution of this work is the use of generative AI to transform unstructured, heterogeneous cyber incident data into structured formats. Incidents were sourced from the Center for Strategic & International Studies (CSIS) List of Significant Cyber Incidents, the University of Maryland Cyber Events Database (UMCED), the European Repository of Cyber Incidents (EuRepoC), the Maritime Cyber Attack Database (MCAD), and the U.S. DOT Transportation Cybersecurity and Resiliency (TraCR) Examples of Cyber Attacks in Transportation (2018 to 2022). These were classified by a fine tuned LLM into five transportation modes: aviation, maritime, rail, road, and multimodal, forming a transportation specific cyber incident database. Another key contribution of this work is the development of a Retrieval Augmented Generation question answering system, designed to enhance accessibility and practical use by enabling users to query the curated database for specific details on transportation related cyber incidents. By leveraging LLMs for both data extraction and user interaction, this study contributes a novel, accessible tool for improving cybersecurity awareness in the transportation sector.

Megh Bahadur KC, Ziqi Song

School bus transportation, the largest fleet size for public transportation in the US, plays a significant role in sustainability through transport decarbonization. Thus, effective planning of electric school bus routes and recharge schedules is vital. This study proposes a novel approach that simultaneously addresses electric school bus dynamic routing and partial charge scheduling, considering practical scenarios such as varying student demands, bus capacities, maximum ride time, stop time window, and fleet mixes. The model incorporates constraints like bell time tolerance and battery capacity and charging infrastructure candidate location, making it robust for school bus electrification. A linearized Mixed Integer Programming (MIP) model for homogeneous and heterogeneous fleets with full and partial recharging strategies is formulated. The proposed objective function for nonlinear and linear models is executed and compared for computational effectiveness. The model is tested on various sizes of school networks using modified benchmark instances, and a real-world case study demonstrates the benefits of electrified school transportation. The results show that employing heterogeneous fleets can lead to cost savings, reduced routing distance, and travel time for both the tested networks and the case study. Sensitivity analyses highlight the trade-offs between battery size and total cost. Furthermore, the benefits of partial charging and optimum riding time for school bus routes are suggested. The proposed optimization approach can achieve significant reductions in travel distance, up to 56.4% compared to the current situation and fleet size, supporting the case for school transport electrification. Potential additional investment subsidies from federal and state governments are added benefits for accelerated school bus electrification.

Luay Jum’a, Ahmed Adnan Zaid, Mohammed Othman

<i>Background</i>: This study conceptualizes supply chain ambidexterity through two capabilities, supply chain adaptability and agility. Accordingly, it investigates the impact of supply chain adaptability and agility on green product innovation (GPI) and supply chain sustainability in Jordanian manufacturing firms. It also examines the mediating role of GPI in these relationships. The study is based on dynamic capabilities theory (DCT) as the theoretical foundation. <i>Methods</i>: A quantitative research approach was employed, with data collected from 346 supply chain managers using a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used for analysis. <i>Results</i>: The findings reveal that supply chain adaptability does not directly influence sustainability but significantly enhances GPI, which positively impacts sustainability. Supply chain agility, however, directly and significantly improves both GPI and sustainability, highlighting its importance in achieving sustainable supply chain performance. Additionally, GPI mediates the relationship between supply chain ambidexterity and sustainability, reinforcing its role as a key enabler of eco-friendly supply chain management. These findings provide theoretical and managerial implications. <i>Conclusions</i>: The study extends DCT by confirming the role of GPI in linking supply chain ambidexterity to sustainability. Managers should prioritize agility, invest in sustainable products, and adopt green practices to enhance competitiveness.

Mouna Garai, Maha Sliti, Noureddine Boudriga et al.

Effective communication solutions are crucial in the dynamic transportation landscape. The rise of autonomous vehicles and sophisticated transportation systems has shaped urban mobility, underscoring the importance of safety considerations and data-driven decision making. This study examines the significance of rapid, low-latency communication in advanced intelligent transportation systems, focusing on the use of Visible Light Communication (VLC) in vehicle ad hoc networks (VANETs). This study introduces a tree-structured communication architecture utilizing hierarchical optical codewords to enhance data routing efficiency and establish a vehicle identification system. The proposed system employs dynamic attachment and reattachment protocols in conjunction with adaptive quality-of-service mechanisms to effectively mitigate variability in traffic dynamics, thus enhancing network stability and data aggregation. Simulation results contrasting the Intelligent Driver Model, Gipps, and Krauss mobility models indicate that, while more complex network trees may lead to increased delay and lower effective signal-to-noise ratios, models characterized by greater vehicular spacing generally result in reduced delay and enhanced SNR, though this improvement comes at the cost of connectivity. This document provides a detailed examination of mobility-aware performance and the incorporation of tree-structured VLC VANETs that employ hierarchical optical codewords for distinct node identification. The performance insights reveal significant improvements in scalability, latency, and throughput, which support the advancement of smart city infrastructures that are more sustainable, efficient, and secure.

Nathan Huynh, Majbah Uddin, Chu Cong Minh

With the growth of intermodal freight transportation, it is important that transportation planners and decision makers are knowledgeable about freight flow data to make informed decisions. This is particularly true with Intelligent Transportation Systems (ITS) offering new capabilities to intermodal freight transportation. Specifically, ITS enables access to multiple different data sources, but they have different formats, resolution, and time scales. Thus, knowledge of data science is essential to be successful in future ITS-enabled intermodal freight transportation system. This chapter discusses the commonly used descriptive and predictive data analytic techniques in intermodal freight transportation applications. These techniques cover the entire spectrum of univariate, bivariate, and multivariate analyses. In addition to illustrating how to apply these techniques through relatively simple examples, this chapter will also show how to apply them using the statistical software R. Additional exercises are provided for those who wish to apply the described techniques to more complex problems.

Sachin B. Chougule, Bharat S. Chaudhari, Sheetal N. Ghorpade et al.

Electric vehicles are widely adopted globally as a sustainable mode of transportation. With the increased availability of onboard computation and communication capabilities, vehicles are moving towards automated driving and intelligent transportation systems. The adaption of technologies such as IoT, edge intelligence, 5G, and blockchain in vehicle architecture has increased possibilities towards efficient and sustainable transportation systems. In this article, we present a comprehensive study and analysis of the edge computing paradigm, explaining elements of edge AI. Furthermore, we discussed the edge intelligence approach for deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. It mentions the advantages of edge intelligence and its use cases in smart electric vehicles. It also discusses challenges and opportunities and provides in-depth analysis for optimizing computation for edge intelligence. Finally, it sheds some light on the research roadmap on AI for edge and AI on edge by dividing efforts into topology, content, service segments, model adaptation, framework design, and processor acceleration, all of which stand to gain advantages from AI technologies. Investigating the incorporation of important technologies, issues, opportunities, and Roadmap in this study will be a valuable resource for the community engaged in research on edge intelligence in electric vehicles.

Bolanowska Joanna, Dębińska Ewa, Dmytryshyn Marta et al.

Proper location of medical facilities is critical to planning activities for the health security of residents at the regional level. Spatial accessibility of medical services translates into the level of social security of residents and one of the key of the sustainable development. Spatial analyses of the availability of medical services can, therefore, be used to assess the distribution of the locations of stationing Emergency Medical Teams (EMT). The conclusions made will be particularly important when adjusting the strategic documentation and operation of the system, so that the changes made will have the best possible impact on improving the safety of the population. The time it takes to reach those waiting for help plays a key role here. It is a key element in ensuring the effectiveness of emergency medical services. Constantly monitored and analyzed, it can give a complete picture of the optimality of the location of facilities. Its reasonable standards determined by provincial plans year after year should be rigorously observed. However, the key to achieving the shortest possible time to reach an incident is the location and determination of optimal routes for EMT.

Yu Guo, Ryan Wen Liu, Jiangtian Nie et al.

Visual surveillance technology is an indispensable functional component of advanced traffic management systems. It has been applied to perform traffic supervision tasks, such as object detection, tracking and recognition. However, adverse weather conditions, e.g., fog, haze and mist, pose severe challenges for video-based transportation surveillance. To eliminate the influences of adverse weather conditions, we propose a dual attention and dual frequency-guided dehazing network (termed DADFNet) for real-time visibility enhancement. It consists of a dual attention module (DAM) and a high-low frequency-guided sub-net (HLFN) to jointly consider the attention and frequency mapping to guide haze-free scene reconstruction. Extensive experiments on both synthetic and real-world images demonstrate the superiority of DADFNet over state-of-the-art methods in terms of visibility enhancement and improvement in detection accuracy. Furthermore, DADFNet only takes $6.3$ ms to process a 1,920 * 1,080 image on the 2080 Ti GPU, making it highly efficient for deployment in intelligent transportation systems.

Hyelin Nam, Jihong Park, Jinho Choi et al.

This paper proposes new framework of communication system leveraging promising generation capabilities of multi-modal generative models. Regarding nowadays smart applications, successful communication can be made by conveying the perceptual meaning, which we set as text prompt. Text serves as a suitable semantic representation of image data as it has evolved to instruct an image or generate image through multi-modal techniques, by being interpreted in a manner similar to human cognition. Utilizing text can also reduce the overload compared to transmitting the intact data itself. The transmitter converts objective image to text through multi-model generation process and the receiver reconstructs the image using reverse process. Each word in the text sentence has each syntactic role, responsible for particular piece of information the text contains. For further efficiency in communication load, the transmitter sequentially sends words in priority of carrying the most information until reaches successful communication. Therefore, our primary focus is on the promising design of a communication system based on image-to-text transformation and the proposed schemes for sequentially transmitting word tokens. Our work is expected to pave a new road of utilizing state-of-the-art generative models to real communication systems

Wei Wang, Zhuo Hao, Fufan Qu et al.

Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

Seyed Ali Haji Esmaeili, Ahmad Sobhani, Sajad Ebrahimi et al.

<i>Background</i>: Due to the growing demand for energy and environmental issues related to using fossil fuels, it is becoming tremendously important to find alternative energy sources. Bioethanol produced from switchgrass is considered as one of the best alternatives to fossil fuels. <i>Methods</i>: This study develops a two-stage supply chain modeling approach that first determines feasible locations for constructing switchgrass-based biorefineries in the state of North Dakota by using Geographic Information Systems (GIS) analysis. In the second stage, the profit of the corresponding switchgrass-based bioethanol supply chain is maximized by developing a mixed-integer linear program that aims to commercialize the bioethanol production while impacts of energy use and carbon emission costs on the supply chain decisions and siting of biorefineries are included. <i>Results</i>: The numerical results show that carbon emissions and energy consumption penalties affect optimal biorefinery selections and supply chain decisions. <i>Conclusions</i>: We conclude that there is no need to penalize both emissions and energy use simultaneously to achieve desirable environmental benefits, otherwise, the supply chain becomes non-profitable. Moreover, imposing emissions or energy consumption penalties makes the optimization model closer to supply sources while having higher land rental costs. Such policies would promote sustainable second-generation biomass production, thus decreasing reliance on fossil fuels.

Maanak Gupta, James O. Benson, Farhan Patwa et al.

Intelligent Transportation System (ITS) is a vision which offers safe, secure and smart travel experience to drivers. This futuristic plan aims to enable vehicles, roadside transportation infrastructures, pedestrian smart-phones and other devices to communicate with one another to provide safety and convenience services. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication in ITS offers ability to exchange speed, heading angle, position and other environment related conditions amongst vehicles and with surrounding smart infrastructures. In this intelligent setup, vehicles and users communicate and exchange data with random untrusted entities (like vehicles, smart traffic lights or pedestrians) whom they don’t know or have met before. The concerns of location privacy and secure communication further deter the adoption of this smarter and safe transportation. In this article, we present a secure and trusted V2V and V2I communication approach using edge infrastructures where instead of direct peer to peer communication, we introduce trusted cloudlets to authorize, check and verify the authenticity, integrity and ensure anonymity of messages exchanged in the system. Moving vehicles or road side infrastructure are dynamically connected to nearby cloudlets, where security policies can be implemented to sanitize or stop fake messages and prevent rogue vehicles to exchange messages with other vehicles. We also present a formal attribute-based model for V2V and V2I communication, called AB-ITS, along with proof of concept implementation of the proposed solution in AWS IoT platform. This cloudlet supported architecture complements direct V2V or V2I communication, and serves important use cases such as accident or ice-threat warning and other safety applications. Performance metrics of our proposed architecture are also discussed and compared with existing ITS technologies.

A. Sodhro, Sandeep Pirbhulal, Gul Hassan Sodhro et al.

Fifth generation (5G) technologies have become the center of attention in managing and monitoring high-speed transportation system effectively with the intelligent and self-adaptive sensing capabilities. Besides, the boom in portable devices has witnessed a huge breakthrough in the data driven vehicular platform. However, sensor-based Internet of Things (IoT) devices are playing the major role as edge nodes in the intelligent transportation system (ITS). Thus, due to high mobility/speed of vehicles and resource-constrained nature of edge nodes more data packets will be lost with high power drain and shorter battery life. Thus, this research significantly contributes in three ways. First, 5G-based self-adaptive green (i.e., energy efficient) algorithm is proposed. Second, a novel 5G-driven reliable algorithm is proposed. Proposed joint energy efficient and reliable approach contains four layers, i.e., application, physical, networks, and medium access control. Third, a novel joint energy efficient and reliable framework is proposed for ITS. Moreover, the energy and reliability in terms of received signal strength (RSSI) and hence packet loss ratio (PLR) optimization is performed under the constraint that all transmitted packets must utilize minimum transmission power with high reliability under particular active time slot. Experimental results reveal that the proposed approach (with Cross Layer) significantly obtains the green (55%) and reliable (41%) ITS platform unlike the Baseline (without Cross Layer) for aging society.

Jiali Zhou, Haris N. Koutsopoulos

Airborne diseases, including COVID-19, raise the question of transmission risk in public transportation systems. However, quantitative analysis of the effectiveness of transmission risk mitigation methods in public transportation is lacking. The paper develops a transmission risk modeling framework based on the Wells-Riley model using as inputs transit operating characteristics, schedule, Origin-Destination (OD) demand, and virus characteristics. The model is sensitive to various factors that operators can control, as well as external factors that may be subject of broader policy decisions (e.g. mask wearing). The model is utilized to assess transmission risk as a function of OD flows, planned operations, and factors such as mask-wearing, ventilation, and infection rates. Using actual data from the Massachusetts Bay Transportation Authority (MBTA) Red Line, the paper explores the transmission risk under different infection rate scenarios, both in magnitude and spatial characteristics. The paper assesses the combined impact from viral load related factors and passenger load factors. Increasing frequency can mitigate transmission risk, but cannot fully compensate for increases in infection rates. Imbalanced passenger distribution on different cars of a train is shown to increase the overall system-wide infection probability. Spatial infection rate patterns should also be taken into account during policymaking as it is shown to impact transmission risk. For lines with branches, demand distribution among the branches is important and headway allocation adjustment among branches to balance the load on trains to different branches can help reduce risk.