Because of communication delays in mixed traffic scenarios, autonomous vehicles may receive outdated information about surrounding vehicles, leading to inaccurate trajectory prediction and compromised safety. To address this issue, this study proposes a spatio-temporal delay-aware long short-term memory (SDA-LSTM) model. Using the Next Generation Simulation dataset, this research analyzes the interactions between spatial distribution and motion parameters in multi-vehicle cooperative driving scenarios. It constructs a spatial relationship delay adaptive module to dynamically weight the contributions of heterogeneous neighboring vehicle data to prediction outcomes. By innovatively integrating a delay-aware temporal encoding mechanism in the model framework, it effectively characterizes the differences in time-delay features of historical data. Eventually, an SDA-LSTM with spatio-temporal delay perception capability is established. Experiment results demonstrate that SDA-LSTM surpasses LSTM in prediction accuracy, with an average root mean square error reduction of approximately 52% across all prediction steps. It also exhibits robust capability to capture vehicle motion intentions across both straight-driving and lane-changing scenarios. The prediction errors remain within acceptable ranges for aggressive, normal, and conservative driving styles, with the smallest prediction error observed in normal driving style vehicles. Furthermore, through delay-aware weight heatmaps analysis, the study verifies its adaptive historical data weighting and irrelevant information pruning capabilities.
Abstract Vehicular communication enables a variety of safety, infotainment, mobility, and environmental applications. Vehicular communication is one of the leading research areas because of its specific applications and characteristics and has attracted great interest from academia, industries, and governments. Our paper is a comprehensive survey of vehicular communication that covers the state of the art and future research directions. The article is a new contribution in the similar category of tutorials/surveys of the vehicular communication domain with the latest details. State of the art presents the architecture, applications, emerging radio access technologies, standardization, and project activities. We review the protocol stacks of the intelligent transportation system (ITS) in the USA, Japan, and Europe with their latest standards. In this paper, we present the emerging radio access technologies such as visible light communication, mmWave, Cellular-V2X, and 5G for connected and autonomous vehicles and their associated challenges. The new research directions in the emerging areas of this domain, such as seamless connectivity, edge, fog, software-defined and named data network, and security are also present. We believe that our work will help the researchers, developers and government agencies to become familiar with the latest features of the domain.
Muhammad Arif, Guojun Wang, Md Zakirul Alam Bhuiyan
et al.
Abstract Over the past few decades, the intelligent transportation system (ITS) have emerged with new technologies and becomes the data-driven ITS, because the substantial amount of data is assembled from the multiple sources. Vehicular Ad hoc networks (VANETs), are a particular case of ad hoc networks that are used in the smart ITS. VANETs have become one of the most, encouraging, promising, and fastest-growing subsets of the mobile ad hoc networks (MANETs). They are comprised of smart vehicles and roadside units (RSUs) and on-board units (OBUs) which communicate through unreliable wireless media. Other than lacking infrastructure, delivering entities move with different increasing speeds. Thus, this delays establishing reliable end-to-end communication paths and having efficient data transfer. In this manner, VANETs have diverse system concerns and security difficulties in getting the accessibility of ubiquitous availability, secure communication, interchanges, and reputation management system. Which influence the trust in collaboration and arrangement between the portable system. By their fluctuation in nature, they are genuinely defenseless against assaults, which may result in life-jeopardizing circumstances. In this survey, we provide an extensive overview of the ITS and the evolution of ITS to VANETs. We provide the details of VANETs, discussed the privacy and security attacks in VANETs with their applications and challenges. We address the effectiveness of VANETs and cloud computing with architecture and related privacy and security issues. We also examined the communication protocols for each network layer with the relevant attacks occurred at each layer. We also discussed the potential benefits of the different proposed techniques related to VANETs, application, and challenges in details. In the end, we provide a conclusion with some open and emerging issues in VANETs.
The hot in-plant recycling technology for asphalt pavement, as a solid waste recycling method, can significantly reduce environmental pollution and lower construction costs. However, the variability of Reclaimed Asphalt Pavement (RAP) materials greatly affects the quality of hot in-plant recycled pavement. Effectively controlling the variability of RAP materials has become key to ensuring the long-term durability of recycled pavements. To address this issue, this study proposes an intelligent framework for controlling RAP variability, introducing a method for RAP classification and storage based on the original performance of the pavement, as well as a method for calculating the maximum RAP incorporation rate based on RAP variability. First, road sections are classified and milled according to their original performance to control RAP variability. Then, the maximum RAP content in the hot in-plant recycled asphalt mixture is controlled according to the RAP variability. This proposed framework has been applied to highway maintenance projects in Jiangsu Province, China. After the implementation of the classification and storage method, the coefficient of variation in the passing rate of each sieve in the recycled asphalt mixtures was reduced, and the coefficient of variation in the oil-stone ratio decreased by 1.6 %, demonstrating a significant reduction in variability. Additionally, the carbon emissions of recycled asphalt pavement containing 32 % RAP were evaluated, showing an 8.59 % reduction in carbon emissions compared to the actual pavement constructed with 20 % RAP content.
Alaa Fouad Momena, Kamal Hossain Gazi, Sankar Prasad Mondal
<i>Background:</i> The supply chain refers to the full process of creating and providing a good or service, starting with the raw materials and ending with the final customer. It requires cooperation and coordination between many parties, including the suppliers, manufacturers, distributors, retailers, and customers. <i>Methods:</i> In the medicinal supply chain (MSC), the critical nature of these processes becomes more complicated. It requires strict regulation, quality control, and traceability to ensure patient safety and compliance with regulatory standards. This study is conducted to suggest a smooth channel to deal with the challenges and adaptability of the MSC. Different MSC challenges are considered as criteria which deal with various adaptation plans. Multi-criteria decision-making (MCDM) methodologies are taken as optimization tools and probabilistic linguistic term sets (PLTSs) are considered for express uncertainty. <i>Results:</i> The subscript degree function (SDF) and deviation degree function (DDF) are introduced to evaluate the crisp value of the PLTSs. An MSC model is constructed to optimize the sustainable medicinal supply chain and overcome various barriers to MSC problems. <i>Conclusions:</i> Additionally, sensitivity analysis and comparative analysis were conducted to check the robustness and flexibility of the system. Finally, the conclusion section determines the optimal weighted criteria for the MSC problem and identifies the best possible solutions for MSC using PLTS-based MCDM methodologies.
Transportation and communication, Management. Industrial management
Antonio Santos da Silva, Kevin Herman Muraro Gularte, Giovanni Almeida Santos
et al.
Autonomous vehicles (AVs) are transforming transportation by improving safety, efficiency, and intelligence through integrated sensing, computing, and communication technologies. However, their growing reliance on Vehicle-to-Everything (V2X) communication exposes them to cybersecurity vulnerabilities, particularly at the physical layer. Among these, jamming attacks represent a critical threat by disrupting wireless channels and compromising message delivery, severely impacting vehicle coordination and safety. This work investigates the robustness of New Radio (NR)-V2X-enabled vehicular systems under jamming conditions through a dual-methodology approach. First, two Cooperative Intelligent Transport System (C-ITS) scenarios standardized by 3GPP—Do Not Pass Warning (DNPW) and Intersection Movement Assist (IMA)—are implemented in the OMNeT++ simulation environment using Simu5G, Veins, and SUMO. The simulations incorporate four types of jamming strategies and evaluate their impact on key metrics such as packet loss, signal quality, inter-vehicle spacing, and collision risk. Second, a complementary laboratory experiment is conducted using AnaPico vector signal generators (a Keysight Technologies brand) and an Anritsu multi-channel spectrum receiver, replicating controlled wireless conditions to validate the degradation effects observed in the simulation. The findings reveal that jamming severely undermines communication reliability in NR-V2X systems, both in simulation and in practice. These findings highlight the urgent need for resilient NR-V2X protocols and countermeasures to ensure the integrity of cooperative autonomous systems in adversarial environments.
Sajib Tripura, Qing-Chang Lu, Dhonita Tripura
et al.
In the rapidly changing world of Intelligent Transportation Systems (ITS), achieving fast, reliable, and energy-efficient communication in vehicle fog computing (VFC) networks is crucial for safety–critical applications. Current VFC approaches are not apt for safety–critical applications as they are based on static heuristics, QoS focus design which neglects trust, energy and reliability; slow convergence and does not support fairness and responsiveness. Moreover, they do not adaptively prioritize concurrent emergencies, which motivates the development of mobility and criticality-aware adaptive approaches. This study proposes a novel reinforcement learning framework named Q-APERF based on tabular Q-learning agent improved by the Augmented Priority-Entropy Reward Function (APERF). Our approach dynamically adjusts multiple QoS metrics, including latency, reliability, trustworthiness, and energy consumption, while prioritizing overlapping emergencies such as ambulances, crash alerts, and road hazards exponentially. The agent achieves adaptive QoS weighting and discrete vehicular state, and therefore, the message forwarding performance can be enhanced in a highly dynamic environment (i.e., the IoV). Extensive simulations show that it outperforms some of the existing state-of-the-art approaches. The Q-APERF achieves 95.5% of message prioritization accuracy, 75.4% of transmission efficacy in packet loss situation, and 83% of energy efficiency and 80% faster response to emergency events, which illustrates its dynamic resilience adaptability balance QoS and energy consumption perspective. Moreover, we introduce a novel metric, Survival-Weighted Data Integrity (SWDI), to evaluate incentive mechanisms that promote the sustained participation of vehicles and encourage them to share their resources. This holistic view will enable safer and more fault-tolerant smart transportation systems through offering a secure, scalable, and context-aware vehicular communication solution.
This study explores the innovation model and the role of public policy in tourism development within Pamekasan Regency, utilizing the Analytical Network Process (ANP) approach. It primarily seeks to identify and rank key factors that affect the effectiveness of tourism policy and offer strategic recommendations for improving its development. The research adopts a mixed-method approach that encompasses qualitative insights, including expert interviews and extensive literature reviews, as well as quantitative data collected through a structured questionnaire based on ANP administered to experts, managers, and public decision makers in the tourism sector. Results indicate that, among others, human capital is the most important component of tourism development, followed by institutional/bureaucratic improvements, then accessibility. This emphasis on human capital is in line with endogenous growth theory, which underlines the role played by people in driving economic prosperity. On the other hand, institutional improvement promotes efficient policies for promoting tourism, while accessibility, although ranked lower, still remains crucial for enhancing visitors’ experiences. The recommendations for strategy consist of rigorous training for personnel in the tourism sector, the easing of license and permit procedures, and the development and enhancement of transportation and communication networks. The aim of these strategies is to promote a balanced and sustainable growth within the tourism industry. The study’s rigorous empiricism and theoretical framework offer strong support for the suggested strategies and make theoretical and application-level contributions to tourism management. In this research, by connecting the research results to the endogenous growth theory and supporting the results with a literature review, useful information and strategic solutions for international policymakers and stakeholders in Pamekasan Regency are provided.
The classical pathway of mass production followed a linear model with trashed products and wasted remaining materials at the final stage of their life cycle. Smart approaches of manufacturing and product life cycle management aim for Circular Economy (CE) models to implement sustainable business models to overcome imbalances between resource supply and demand of goods. Non-Fungible Token (NFT) solutions together with smart contracts seem to have the potential to realise such new sustainable business models in the context of CE. The study demonstrates how NFT technology can become an integral part of smart product life cycle management for batteries of e-cars. The research highlights how circular business models can be developed and implemented in the e-car sector around the life cycle management of batteries as well as how NFT technology can contribute to sustainable conceptualisation for battery recycling.
Moh. Khalid Hasan, Md. Osman Ali, Md. Habibur Rahman
et al.
Recently, substantial development is observed in the area of Internet of Vehicles owing to the application of wireless communication technologies. Majority of these technologies are based on radio frequency (RF); however, RF spectra are overly congested and regulated, and hence, insufficient to support massive data traffic in the future. In recent times, optical camera communication (OCC) that uses a light-emitting diode (LED) as a transmitter and a camera as a receiver has been deemed an excellent solution for future intelligent transportation systems. As a communication medium, OCC mostly uses visible light, the spectrum of which is vast, completely free, and unregulated. The current outdoor environment is heavily crammed with LED infrastructures, and most vehicles have built-in cameras, rendering OCC immensely promising. OCC is highly secured, supports mobility, and can achieve an excellent bit-error rate. However, the data rate obtained using OCC is not as high as that obtained using other RF-based systems; therefore, its reliability in fast-changing channels is still under research. This review article discusses the applications of the OCC system in vehicle-to-vehicle and vehicle-to-infrastructure (or vice versa) networks; to the best of our knowledge, this is the first extensive review dedicated to the above topic. Herein, we provide a general overview of OCC standardization in IEEE and ISO in recent years. Then, we explain the general principles of OCC, including channel characteristics, region of interest signaling, and modulation schemes particularly considered in vehicular communications. Additionally, we present a comprehensive overview of the effects of mobility, noise, and interference in OCC. Finally, the challenges and future opportunities in OCC are outlined.
Autonomous vehicles (AVs), as one of the cores in future intelligent transportation systems (ITSs), can facilitate reliable and safe traffic operations and services. The ability to automatically perform effective AV motion planning and deploy efficient perception systems is vital for advancing the quality of core transportation services. However, existing research studies have only considered the applications of either of these approaches, which neglect their necessary interactions in real-world AV motion planning systems. To address this problem, we design an AV motion planning strategy based on motion prediction and V2V communication. Specifically, we propose the perception system and V2V communication module to provide real-time traffic and vehicular information to the participated AVs. Then, we formulate the AV lane-change motion planning problem through the scope of model predictive control based problem, as well as proposing the method on learning optimal motion planning by means of a novel deep learning technique. We conduct extensive case studies to evaluate the performance of the proposed system model. Our experimental results demonstrate the effectiveness of the proposed system model under various traffic conditions. In addition, the robustness of the perception system is guaranteed by utilizing the Car Learning to Act (CARLA) system with available V2V communication.
In this day and age, one of our most challenging communication topics is climate change and the degradation of nature (Fraenkel, 2020). The old model of science communication - whereby scientists in lab coats communicate their facts and truth through mass communication channels - is coming to an end (Climate Outreach, 2017). The science of science communication is a developing body (Climate Outreach, 2017; Corner & Clarke, 2016). There is an urgent need to understand how narratives can contribute to communicating about environmental science more effectively, by aligning with the needs and values of different audiences (Climate Outreach, 2017). Using narratives in environmental communication has become increasingly common (Smith et al., 2014). Yet, many scientists are uncertain about how to communicate and translate their research into compelling stories (Martinez-Conde et al., 2019). This study aims to give scientists and environmental organisations insight into the mechanisms of narrative persuasive storytelling and how an audience processes stories. It concludes that stories are uniquely suited for changing emotionally held opinions and beliefs, and can help individuals to understand complex and abstract scientific subjects. Furthermore, this study concludes that identification is a vital element in narrative persuasion.
In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain‐based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state‐of‐the‐art techniques.
The fare payment system for public transportation has developed from coins, paper tickets, magnetic strips, and pre-paid cards to new technologies such as Radio-Frequency Identification (RFID) and Near Field Communication (NFC). Most fare payment systems are a fusion of NFC and mobile payment. As the latest fare payment system, the studies on gate-free technology without tag action are in progress. The gate-free system has several advantages, such as improving user convenience, eliminating congestion, and preventing the spread of infectious diseases. We introduce a gate-free system using Bluetooth Low Energy (BLE) technology as the next-generation fare payment system for public transportation. We propose a smart block structure for a gate-free system and analyze the antenna for this structure. A directional antenna is required for location detection of multiple mobile terminals in the gate-free zone, and an antenna to satisfy the requirements is analyzed. A stacked air-gap patch array that satisfies BLE performance and has high directivity has been proposed within the limited smart block size. To compensate for the low bandwidth of the patch antenna, an air gap is added between the patch radiator and the ground, and the antenna gain is increased by applying a stacked patch on the patch radiator. Based on the proposed antenna and smart block, a gate-free system testbed is installed, and the testing results are presented.