Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data trans-mission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.
In light of rising sustainability demands in commercial transport, cargo bikes and light electric vehicles (LEV) offer promising alternatives to conventional fleets. However, despite increasing vehicle diversity and supportive policies, adoption remains limited due to organizational inertia, sunk costs, and resistance to change. Short-term pilot projects, while useful for testing feasibility, often fail to capture the evolving and context-specific dynamics of fleet integration. To address this gap, we introduce and evaluate a novel long-term trial framework implemented across multiple German regions over a 12-month period. The framework embeds vehicle trials into daily operations and combines tailored onboarding, sustained engagement, and structured reflection phases. This article makes three core contributions. First, it advances both academic and practical understanding by introducing a reliable and transferable framework for long-term fleet transformation that functions as an organizational learning device. Second, it uncovers context-specific and time-sensitive challenges of integrating LEVs and cargo bikes, as shown through five stylized cases across logistics, manufacturing, services, and craft sectors. Third, it conceptualizes three learning-based pathways of organizational commitment to fleet transformation: fleet expansion, vehicle substitution, and purchase prevention, through which long-term trials can support learning-based fleet reconfiguration, including normative learning reflected in the strategic reframing of alternative vehicles. The findings highlight the value of long-term trials in supporting adaptive and real-world transitions that extend beyond the scope of short-term studies. By fostering deeper organizational learning and aligning vehicle solutions with operational realities, the framework establishes critical conditions for sustained adoption and contributes to the broader transition toward more sustainable transport practices.
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation—which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications—will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings.
Autonomous Vehicle has been transforming intelligent transportation systems. As telecommunication technology improves, autonomous vehicles are getting connected to each other and to infrastructures, forming Connected and Autonomous Vehicles (CAVs). CAVs will help humans achieve safe, efficient, and autonomous transportation systems. However, CAVs will face significant security challenges because many of their components are vulnerable to attacks, and a successful attack on a CAV may have significant impacts on other CAVs and infrastructures due to their communications. In this paper, we conduct a survey on 184 papers from 2000 to 2020 to understand state-of-the-art CAV attacks and defense techniques. This survey first presents a comprehensive overview of security attacks and their corresponding countermeasures on CAVs. We then discuss the details of attack models based on the targeted CAV components of attacks, access requirements, and attack motives. Finally, we identify some current research challenges and trends from the perspectives of both academic research and industrial development. Based on our studies of academic literature and industrial publications, we have not found any strong connection between academic research and industry's implementation on CAV-related security issues. While efforts from CAV manufacturers to secure CAVs have been reported, there is no evidence to show that CAVs on the market have the ability to defend against some novel attack models that the research community has recently found. This survey may give researchers and engineers a better understanding of the current status and trend of CAV security for CAV future improvement.
D. Caprio, A. Ebrahimnejad, Hamidreza Alrezaamiri
et al.
Abstract The shortest path (SP) problem constitutes one of the most prominent topics in graph theory and has practical applications in many research areas such as transportation, network communications, emergency services, and fire stations services, to name just a few. In most real-world applications, the arc weights of the corresponding SP problems are represented by fuzzy numbers. The current paper presents a fuzzy-based Ant Colony Optimization (ACO) algorithm for solving shortest path problems with different types of fuzzy weights. The weights of the fuzzy paths involving different kinds of fuzzy arcs are approximated using the α -cut method. In addition, a signed distance function is used to compare the fuzzy weights of paths. The proposed algorithm is implemented on three increasingly complex numerical examples and the results obtained compared with those derived from a genetic algorithm (GA), a particle swarm optimization (PSO) algorithm and an artificial bee colony (ABC) algorithm. The results confirm that the fuzzy-based enhanced ACO algorithm could converge in about 50% less time than the alternative metaheuristic algorithms.
Deema Almaskati, Apurva Pamidimukkala, Sharareh Kermanshachi
et al.
Fully autonomous vehicles (FAVs) are expected to reshape transportation systems and benefit society in several ways, but their advantages do not guarantee that the public will embrace them, a factor that is critical to their widespread deployment. This local pilot case study in Arlington, Texas (N = 295) aimed to supplement the existing literature on the adoption of autonomous vehicles (AVs) by investigating how the public’s perceptions shift as the level of autonomy increases. A survey questionnaire was developed to examine how demographic and attitudinal factors influence automation preference rankings and non-parametric statistical tests were used to evaluate the responses. The findings indicated significant variations in ranking preferences, with an overall preference for manual vehicles, followed by partially autonomous vehicles and FAVs. Among different demographic groups, age, gender, race, and prior exposure to AVs were found to be significant for ranking manual vehicles and FAVs. Spearman’s rank correlation analysis revealed a mirrored and opposite trend across attitudinal factors for rankings of manual vehicles and FAVs, but preferences for partial automation were not strongly influenced by them. This study highlights the importance of public receptivity to FAVs and may benefit policymakers by providing insight into the factors that affect the perceptions of prospective users, thereby facilitating their integration onto existing roadways.
Transportation and communications, Transportation engineering
Javier Palomares, Estela Carmona-Cejudo, Cristina Cervello-Pastor
et al.
In modern novel collaborative multi-Automated Guided Vehicle (AGV) systems, vehicles are responsible for executing both mission-critical process-related operations and purely computational tasks, such as collision avoidance. This work investigates the problem of joint inter-AGV task placement and intra-AGV computational resource allocation in MEC-enabled multi-AGV environments. To address this challenge, a two-step strategy is proposed to maximize the number of scheduled and completed tasks across multiple AGVs while ensuring fair and efficient resource use within each AGV. The problem of inter-AGV task placement is solved by dynamically applying a catalog of deep reinforcement learning (DRL) models for varying numbers of AGVs. Training time for these models is reduced threefold by using datasets from existing optimization solvers. Transfer learning further reduces training times by up to 51%. Second, a multi-agent deep reinforcement learning (MADRL)-based collaborative protocol for dynamic intra- AGV resource allocation (MACP-DRA) is proposed, allowing AGVs to adjust computational resources dynamically. It incorporates a minimum guaranteed share strategy to ensure fair resource distribution while optimizing performance under dynamic workloads. Compared to existing MADRL approaches, MACP-DRA enhances conflict resolution efficiency while maintaining low computational cost. Evaluation results demonstrate that the proposed inter-AGV scheduling strategy approaches optimal performance while achieving a superior trade-off between decision time and task completion rates. Compared to a multi-agent DRL baseline, the proposed MACP-DRA models reduced resource conflicts by 54.9%, task processing delays by 35.7%, and resource underutilization by 9.93%, while maintaining minimal computational and energy consumption overhead.
Telecommunication, Transportation and communications
Jilly Ayuningtias, Marimin Marimin, Agus Buono
et al.
<i>Background:</i> The competitiveness of Indonesia’s Muslim fashion industry requires evaluation through both internal efficiency and external strategic factors, yet existing approaches often assess these dimensions separately. <i>Methods:</i> This study develops a Weighted Efficiency Competitive Score (WECS) that integrates Data Envelopment Analysis (DEA) to measure operational efficiency and Porter’s Five Forces to capture market pressures. The weights of α and β were calibrated through sensitivity analysis under the constraint α + β = 1, with values ranging from α = 0.3 to 0.7 and β = 0.7 to 0.3, using data from 23 Muslim fashion businesses in Jakarta. <i>Results:</i> The analysis identified α = 0.6 and β = 0.4 as the most stable configuration, and only 30% of firms achieved both high efficiency and strong market positioning. Strategic leaders such as JT. Co and PM. Co demonstrated that digital transformation, disciplined cost structures, and strong supply chain partnerships foster sustainable competitiveness. <i>Conclusions:</i> The WECS framework offers a replicable method to quantitatively integrate micro and macro determinants of competitiveness, contributes to the literature by bridging efficiency and strategy evaluation, and provides practical guidance for managers and policymakers to enhance decision support systems in strengthening the Muslim fashion industry’s global positioning.
Transportation and communication, Management. Industrial management
Twitter has established itself as a valuable social media platform for urban research over the last 10 years, by providing free and accessible data. However, recent shift towards the monetization of its data, raises questions on its future use. To investigate this, bibliometric analysis and topic modeling techniques are used to explore trends in urban research publications and wider consequences of reduced data access. The results illustrate a period of ‘hype’ towards Twitter data, followed by decline in recent years. Application areas and topics are also identified, highlighting the distinct ways in which social media data contributes to urban research.
Transportation and communications, Urban groups. The city. Urban sociology
Md. Noor-A.-Rahim, Zilong Liu, Haeyoung Lee
et al.
Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.
Internet of Vehicles (IoV) is an intelligent application of Internet of Things (IoT) in smart transportation that takes intelligent commitments to the passengers to improve traffic safety and efficiency, and generate a more enjoyable driving and riding environment. Fog cloud-based IoV is another variant of mobile cloud computing where vehicular cloud and Internet can co-operate in more effective way in IoV. However, more increasing dependence on wireless communication, control, and computing technology makes IoV more dangerous to prospective attacks. For secure communication among vehicles, road-side units, fog and cloud servers, we design a secure authenticated key management protocol in fog computing-based IoV deployment, called AKM-IoV. In the designed AKM-IoV, after mutual authentication between communicating entities in IoV they establish session keys for secure communications. AKM-IoV is tested for its security analysis using the formal security analysis under the widely accepted real-or-random (ROR) model, informal, and formal security verification using the broadly accepted automated validation of Internet security protocols and applications (AVISPAs) tool. The practical demonstration of AKM-IoV is shown using the NS2 simulation. In addition, a detailed comparative study is conducted to show the efficiency and functionality and security features supported by AKM-IoV as compared to other existing recent protocols.
Brandi S. Morris, P. Chrysochou, J. D. Christensen
et al.
Climate change is an issue which elicits low engagement, even among concerned segments of the public. While research suggests that the presentation of factual information (e.g., scientific consensus) can be persuasive to some audiences, there is also empirical evidence indicating that it may also increase resistance in others. In this research, we investigate whether climate change narratives structured as stories are better than informational narratives at promoting pro-environmental behavior in diverse audiences. We propose that narratives structured as stories facilitate experiential processing, heightening affective engagement and emotional arousal, which serve as an impetus for action-taking. Across three studies, we manipulate the structure of climate change communications to investigate how this influences narrative transportation, measures of autonomic reactivity indicative of emotional arousal, and pro-environmental behavior. We find that stories are more effective than informational narratives at promoting pro-environmental behavior (studies 1 and 3) and self-reported narrative transportation (study 2), particularly those with negatively valenced endings (study 3). The results of study 3 indicate that embedding information in story structure influences cardiac activity, and subsequently, pro-environmental behavior. These findings connect works from the fields of psychology, neuroscience, narratology, and climate change communication, advancing our understanding of how narrative structure influences engagement with climate change through emotional arousal, which likely incites pro-environmental behavior as the brain’s way of optimizing bodily budgets.
Abstract Advances in communications, smart transportation systems, and computer systems have recently opened up vast possibilities of intelligent solutions for traffic safety, convenience, and effectiveness. Artificial Intelligence (AI) is currently being used in various application domains because of its strong potential to help enhance conventional data-driven methods. In the area of Vehicular Ad hoc NETworks (VANETs) data is frequently collected from various sources. This data is used for various purposes which include routing, broadening the awareness of the driver, predicting mobility to avoid hazardous situations, thereby improving passenger comfort, safety, and quality of road experience. We present a comprehensive review of AI techniques that are currently being explored by various research efforts in the area of VANETs. We discuss the strengths and weaknesses of these proposed AI-based proposed approaches for the VANET environment. Finally, we identify future VANET research opportunities that can leverage the full potential of AI.
Mahmood A. Al-Shareeda, Murtaja Ali Saare, S. Manickam
The use of unmanned aerial vehicles (UAVs) will be crucial in the next generation of wireless communications infrastructure. When compared to traditional ground-based solutions, it is expected that their use in a variety of communication-based applications will increase coverage and spectrum efficiency. In this paper, we provide a detailed review of all relevant research works as follows. This paper presents types of UAVs (e.g., wireless coverage, military, agriculture, medical applications, environment, and climate, and delivery and transportation), characteristics of UAVs (e.g., node density, altering system topology, node mobility, radio broadcasting mode, frequency band, localization, and power consumption and network lifetime), the application of UAVs (e.g., Multi-UAV cooperation, UAV-to-VANET collaborations, and UAV-to-ground tasks). Additionally, this paper reviews the routing protocols of UAVs (e.g., topology-based, position-based, heterogeneous, delay-tolerant networks (DTNs), swarm-Based, and cluster-based) and simulation tools (e.g., OMNeT++, AVENS, MATLAB, NS3, SUMO, and OPNET). The design and development of any new methods for UAVs may use this work as a guide and reference.