Hasil untuk "Transportation and communication"

Jingwen Wang, Ivan Topilin, Anastasia Feofilova et al.

The advancement of intelligent road transport represents a promising direction in the evolution of transportation systems, aimed at improving road safety and reducing traffic accidents. The integration of artificial intelligence, sensors, and machine vision systems enables autonomous vehicles (AVs) to rapidly adapt to changes in the road environment, minimizing human error and significantly reducing collision risks. These technologies provide continuous and highly precise control, including adaptive acceleration, braking, and maneuvering, thereby enhancing overall road safety. Connected vehicles utilizing C-V2X (Cellular Vehicle-to-Everything) communication primarily feature real-time operation, safety, and stability. However, communication flaws, such as signal fading, time delays, packet loss, and malicious network attacks, can affect vehicle-to-vehicle interactions in cooperative intelligent transport systems (C-ITSs). This study explores how C-V2X technology, compared to traditional DSRC, improves communication latency and enhances vehicle communication efficiency. Using SUMO simulations, various traffic scenarios were modeled with different autonomous vehicle penetration rates and communication technologies, focusing on traffic conflict rates, travel time, and communication performance. The results demonstrated that C-V2X reduced latency by over 99% compared to DSRC, facilitating faster communication between vehicles and contributing to a 38% reduction in traffic conflicts at 60% AV penetration. Traffic flow and safety improved with increased AV penetration, particularly in congested conditions. While C-V2X offers substantial benefits, challenges such as data packet loss, communication delays, and security vulnerabilities must be addressed to fully realize its potential. Future advancements in 5G and subsequent wireless communication technologies are expected to further reduce latency and enhance the effectiveness of C-ITSs. This study underscores the potential of C-V2X to enhance collision avoidance, alleviate congestion, and improve traffic management, while also contributing to the development of more reliable and efficient transportation systems. The continued refinement of simulation models and collaboration among stakeholders will be crucial to addressing the challenges in CAV integration and realizing the full benefits of connected transportation systems in smart cities.

Félix Díaz, Rafael Liza, Nhell Cerna

<i>Background:</i> The increasing complexity of global supply chains has intensified the demand for transparency, traceability, security, and sustainability in logistics and operations. Blockchain technology enables decentralized, immutable frameworks that improve data integrity, automate transactions via smart contracts, and integrate seamlessly with the IoT and AI. <i>Methods:</i> This bibliometric review analyzes 559 peer-reviewed publications retrieved from Scopus and Web of Science using a PRISMA-guided protocol. Data were processed with Bibliometrix and Biblioshiny to examine scientific production, contributing institutions, author countries, collaboration patterns, thematic clusters, and keyword evolution. <i>Results:</i> The analysis reveals a 400% increase in publications after 2020, with China, India, and the USA leading in output but with limited international collaboration. Keyword co-occurrence and thematic mapping reveal dominant topics, including smart contracts, food supply chain traceability, and sustainability, as well as emerging themes such as decentralization, privacy, and the circular economy. <i>Conclusions:</i> The field is marked by interdisciplinary growth, yet it remains thematically and geographically fragmented. This review maps the intellectual structure of blockchain-enabled sustainable supply chains, offering insights for policymakers, developers, and industry leaders and outlining future research avenues centered on global cooperation, platform efficiency, and ethical and regulatory dimensions.

Anil Kumar, Indrajit Pal, Djoen San Santoso et al.

Abstract Bangladesh aims to become a high-income country by 2041, requiring investment in critical infrastructure sectors. Disruptions in one sector can affect others, so prioritizing actions for key sectors is essential when resources are limited. Since no country has endless resources, the current strategy is to focus on developing infrastructure in order of importance. This means that the most critical infrastructure is given priority when allocating resources. The aim of this study was to identify the critical infrastructure sectors and their interdependencies in Bangladesh. While the science of critical infrastructure protection and resilience is well-developed in high-income and developed economies, this research sheds light on identifying critical infrastructure in developing nations like Bangladesh. To identify the critical infrastructure sectors, a comprehensive literature survey was conducted, which was verified and validated by country experts. Policymakers, practitioners, and researchers were consulted through key informant interviews (KII). Interpretive structural modeling (ISM) was applied to determine the interdependencies among identified sectors. Furthermore, cross-impact matrix multiplication applied to classification (MICMAC) analysis was applied to categorize the identified sectors based on driving power and dependence of sectors. The study found that 14 sectors—energy, information and communication technology (ICT), media and culture, law enforcement, transportation, among others—need extra protection measures. It also identified infrastructures with driving power and dependencies in the country’s context. Additionally, this article offers recommendations for improving policy and institutional actions to enhance the resilience of critical infrastructure in the country.

Pattaraporn Khuwuthyakorn, Abdullah Lakhan, Arnab Majumdar et al.

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions.

Hongyang Liu, Min Zhang, Wei Wu et al.

The carbon emissions from tunnel construction can cause a significant impact to today's deteriorating environmental conditions, with a substantial portion arising from construction machinery. However, current methods lack accuracy in quantifying these emissions in real-world projects. This paper proposed a new calculation model for timely result output of construction machines used in drilling-and-blast tunnelling which considered on-site influencing factors. In particular, the proposed model classified different machineries into different groups based on the tunnel construction processes and machinery's function, incorporating internal combustion engines, electric motors, and operational resistance. The reliability and applicability of the model is verified with real tunnel construction data. Through control variable comparisons, the impacts of slope, acceleration, and complex road conditions on mechanical carbon emissions are analyzed. Based on construction time and operational processes, the advantages of introducing new battery-based energy machinery compared to traditional fossil fuel machinery are highlighted, confirming the various benefits of new energy machinery. Besides, this study provides a method for calibrating the carbon emissions of tunnel construction machinery. This study helps understanding the environmental impact of construction and aids subsequent research on machinery selection.

Thuvva Anjali, Rajeev Goyal, G. N. Balaji

Abstract Vehicular Ad-Hoc Networks (VANETs) play a vital role in the advancement of Intelligent Transportation Systems (ITS), offering secure and efficient communication between vehicles to support smart and traffic-independent road systems. With the integration of Internet of Things (IoT) technologies, vehicles have become capable of exchanging critical information over the internet, enabling real-time decision-making. However, this rapid digitization has also introduced significant security and privacy vulnerabilities, leading to increased susceptibility to malicious attacks that may cause catastrophic consequences. To address these security concerns, this study aims to develop a real-time, lightweight, and efficient deep learning model for detecting intrusions in VANET environments. The proposed framework, named X-GEVON, combines Gated Recurrent Units (GRU) for temporal feature extraction with an Enhanced Energy Valley Optimization (EVO) algorithm for hyperparameter tuning, ensuring high detection performance with minimal computational overhead. Furthermore, Explainable Artificial Intelligence (XAI) techniques, particularly the Local Interpretable Model-Agnostic Explanations (LIME), are integrated to provide transparent and interpretable classification results. The model is trained and tested on approximately 400,000 real-time data traces, including both normal and attack scenarios, generated using simulation tools such as SUMO, OMNET +  + , and Python 3.19. Experimental results demonstrate that the proposed method significantly outperforms existing deep learning techniques, achieving a detection accuracy of 99.6%, a precision of 99.2%, and a recall of 99.4%. Additionally, the use of LIME enhances the interpretability of the model by explaining its prediction logic, making it more reliable for real-world applications. In conclusion, the X-GEVON framework offers a powerful, accurate, and explainable solution for intrusion detection in VANETs, bridging the gap between high-performance security models and transparent, interpretable AI systems.

Xiao Liang, Chuan-Zhi Thomas Xie, Hui-Fang Song et al.

Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud–vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario. To address this, we proposed the Three-Stage Cellular Automata (TSCA) model based on empirical observations, dividing the vehicle journey over roundabouts into three stages: entrance, following, and exit. Furthermore, four optimization strategies were developed based on empirical observations and simulation results, using the traffic efficiency, delay time, and dangerous interaction frequency as key evaluation indicators. Numerical tests reveal that dangerous interactions and delays primarily occurred when the roundabout Road Occupancy Rate (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula>) ranged from 0.12 to 0.24, during which times the vehicle speed also decreased rapidly. Among the strategies, the Path Selection Based on Road Occupancy Rate Recognition Strategy (Simulation 4) demonstrated the best overall performance, increasing the traffic efficiency by 15.65% while reducing the delay time, dangerous interactions, and frequency by 6.50%, 28.32%, and 38.03%, respectively. Additionally, the Entrance Facility Optimization Strategy (Simulation 1) reduced the delay time by 6.90%. While space-based optimization strategies had a more moderate overall impact, they significantly improved the local traffic efficiency at the roundabout by approximately 25.04%. Our findings hold significant practical value, particularly with the support of onboard sensors, which can effectively detect non-compliance and provide real-time warnings to guide drivers in adhering to the prescribed traffic management strategies.

Marino Lupi, Daniele Conte, Stefano Benenati et al.

<i>Background</i>: Telematization is essential for improving port efficiency by reducing dwell times and simplifying document management. Currently, only a few ports use informatic document management tools like the Port Community System (PCS), and customs documents are produced and shared in paper format. This results in long port dwell times. <i>Methods</i>: A platform was developed to allow sharing of documents among port actors. The platform shares export documents of each given shipment between export and import port actors; moreover, it serves as a document management platform for ports lacking PCS. In addition, the platform helps in reorganizing the shipment in case of disruptions. <i>Results</i>: The platform has global validity as it allows users to share documents among all port actors worldwide. The platform is formed by the following menus: “Path”, which provides the intermodal freight path; “Shipment”, which allows one to create or change shipment data; “Send notify” to send notifies in case of disruptions; “PMIS and PCS”, which redirects to these platforms of ports involved in the project; and “Documents”, which allows one to upload and share customs documents at the global level. <i>Conclusions</i>: The app contributes to speeding up port operations by reducing dwell times, assists in managing shipment disruptions, and enhances intermodality in freight transportation.

Yosef Daryanto, Djoko Setyanto

<i>Background</i>: The latest global agreement on net-zero emissions encourages new studies on production inventory optimization that promote carbon emissions reduction without harming a company’s profit performance, particularly because certain carbon-pricing regulations bind manufacturing companies. <i>Methods</i>: This study aims to develop a production inventory model that considers direct and indirect emissions in three emission scopes. It incorporates emissions from production, material handling, transportation, and waste disposal for further treatment under a carbon cap-and-trade regulation. With the help of Maple software, a convex total cost function was solved. <i>Results</i>: The results show that the optimum production quantity depends on the values of demand, setup cost, holding cost, fixed cost per delivery, fixed cost for waste disposal, and other parameters related to carbon prices. This study also found that the total cost was highly dependent on the values of the carbon cap, carbon price, and delivery distance. Meanwhile, changes in the delivery distance and fuel emissions standard significantly impacted total emissions. <i>Conclusions</i>: The proposed model can guide manufacturing companies in setting the optimum production quantity per cycle. Moreover, they must carefully manage the delivery and setting of the carbon cap and carbon price from the government.

Md Altab Hossin, Jie Du, Lei Mu et al.

Big data analytics (BDA) enhances knowledge and decision-making. Despite its importance, the connection between technical progress and political change is neglected in the administrative process. Most studies focus on e-government, e-governance, and how technology can improve existing operations of the bureaucracy. However, this article aims to explore the potential of BDA for public policy systems and provide a linkage for the transformation toward digital and smart governance using preferred reported items for systematic review and meta-analysis (PRISMA) approach to reveal the relevant documents and narrative review approach to interpret the application of BDA at each step of the public policy system. In addition, this study identifies several common public policy-related big data sources and techniques that could be used at the various stages of the public policy process. This study argues that BDA has the potential to be used for policy formulation in the four main phases—planning, design, service delivery, and evaluation. Most studies confirm its potential in the policy process for taxation, health, education, transportation, law, economy, and social system. This study reveals that it is also suitable for public policy execution stages, such as public supervision, public regulation, service delivery, and policy feedback. Previous studies have indicated that the application of BDA can transform traditional or manual governance systems into digital and smart governance. We contend that the policy cycle should be seen as a dynamic and iterative process characterized by continuous evolution. Though each step of transformation has its unique challenges in handling BDA and maintaining the Information and Communication Technology (ICT) infrastructure, it can ensure an accurate, prompt, and context-oriented public policy system. These insights provide a novel outlook on effectively managing the interplay between innovation and traditional approaches in the realm of public policy development.

Tejal Rathod, Nilesh Kumar Jadav, Sudeep Tanwar et al.

The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT’s sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.

Rushikesh A. Patil, Abhishek D. Patange, Sujit S. Pardeshi

<i>Background</i>: International transportation has grown substantially, causing total logistics costs (TLCs) to rise. Companies are increasingly striving for their reduction. The most crucial factor affecting TLCs is the transportation mode, and its appropriate selection has become vital for firms. Maritime transport is the most preferred mode for international shipments, while air transport is also increasingly preferred due to the rise in underweight and high-frequency shipments, the expectation of reduced delivery times, and inventory costs. However, a thorough comparative analysis is necessary for the selection. <i>Methods:</i> This paper proposes an intelligent approach based on TLCs. Non-linear optimization is adopted for regular replenishment, while maching-learning classifiers are employed to establish a decision boundary for the chargeable weight of shipments. <i>Conclusions:</i> The study assists in decision making and also establishes a country-wide threshold, highlighting the importance of a country-based logistics strategy. The paper successfully establishes the trends and relations between logistics parameters, which assists the logistics decision making. Research identifies the gaps in the existing literature and bridges them by addressing the required concerns.

Santhosh Kumar Sripathi Venkata Naga, Rajkumar Yesuraj, Selvi Munuswamy et al.

Data transmission in intelligent transportation systems is being challenged by a variety of factors, such as open wireless communication channels, that pose problems related to security, anonymity, and privacy. To achieve secure data transmission, several authentication schemes are proposed by various researchers. The most predominant schemes are based on identity-based and public-key cryptography techniques. Due to limitations such as key escrow in identity-based cryptography and certificate management in public-key cryptography, certificate-less authentication schemes arrived to counter these challenges. This paper presents a comprehensive survey on the classification of various types of certificate-less authentication schemes and their features. The schemes are classified based on their type of authentication, the techniques used, the attacks they address, and their security requirements. This survey highlights the performance comparison of various authentication schemes and presents the gaps in them, thereby providing insights for the realization of intelligent transportation systems.

Vuyo S. Pana, Oluwaseyi P. Babalola, Vipin Balyan

The fifth generation (5G) technology improves the user experience and creates new possibilities for a variety of applications, including transportation, device-to-device communication, agriculture, and manufacturing. These new use-cases significantly increase the number of users, volume of traffic, throughput, and latency. Hence, there is a need to modify the radio access network (RAN) for the 5G system. This study investigates the various RAN architectures such as cloud-RAN (CRAN), heterogeneous cloud-RAN (HCRAN), and fog-RAN (FRAN). The architectures are examined in a variety of contexts, including system efficiency, spectrum and energy efficiency, fronthaul capacity, latency, resource sharing and allocation, and so on. Also, current issues with these architectures are highlighted, as well as some existing remedies.

João M. Lopes, Sofia Gomes, Lassana Mané

<i>Background</i>: The constraints imposed by the pandemic COVID-19 increased the risks of the disruption of supply chains, bringing new challenges to companies. These effects were felt more intensely in less-developed countries, which are highly dependent on imports of products and raw materials. This study aims to assess the impact of supply chain resilience in a less-developed country (Guinea-Bissau) using complex adaptive system theory. <i>Methods</i>: We used a qualitative methodology through multiple case studies. Semi-structured interviews were conducted with four companies. The semi-structured script contains questions about supply chain disruptions, vulnerabilities and resilience. <i>Results</i>: The main results show that the companies in Guinea-Bissau, due to their dependence on the outside world and the absence of formal, larger and more diversified supply chains, suffered serious consequences with the disruption imposed by the pandemic. It was also concluded that the more resilient the supply chain, the fewer the impacts of crisis events and that the resilience of companies at this level depends on their obtaining competitive advantages over their competitors. <i>Conclusions</i>: The main practical implications of this study are the need to formalize the supply chain, diversify the supply of services and products of companies dependent on the exterior, adopt metrics that allow for the early detection of situations of supply chain disruption, effectively manage stocks and promote proactive crisis resolution strategies. Studies on the impact of resilience on supply chains in crises are scarce, especially on companies located in underdeveloped countries.

Yedilbayev Bauyrzhan, Brener Arnold, Shokanova Akmaral et al.

The work deals with the mathematical modeling of traffic phenomena. The submitted model is based on a prospective analogy of some described phenomena with particle sedimentation. Both the qualitative analysis of the model and the numerical experiment is carried out. Qualitative results of the research have been compared with the known data of supervision of the traffic on city highways. As a result, the main control parameters which can use for optimal traffic management are highlighted and justified.

Sony Sumaryo, Kalamullah Ramli, Abdul Halim et al.

Intelligent Transportation System (ITS) is the synergy of information technology, real-time control, and communication networks. The system is expected to perform more complex traffic arrangements, in particular, traffic management of Emergency Vehicles (EV) such as fire trucks, ambulances, and so forth. Implementation of traffic management using only Traffic Signal Pre-emption does not give enough space for an EV to cross an intersection safely, especially on streets where there is only one lane. This paper proposes a model of accelerated emptying of traffic in front of EVs. Accelerated emptying model uses historical approach, based on current characteristics of traffic. For example, if the normal vehicle speed is equal to the EV speed before accelerated emptying, the system indicator will be 0%, thereby indicating no need for accelerated emptying. Similarly, a negative system indicator result means an accelerated emptying process is not necessary. However, if the system indicator is close to 100%, this result indicates accelerated emptying is necessary.

Sawsan Abdul Rahman, Azzam Mourad, May El Barachi

With the widespread use of smartphones and the continuous increase of their capabilities, a new sensing paradigm has emerged: mobile crowdsensing. The concept of crowdsensing implies the reliance on the crowd to perform sensing tasks and collect data about a phenomena of interest. Due to the benefits it offers in terms of time and cost savings in terms of sensors' deployment and maintenance, the concept of mobile crowdsensing is now being adopted in the area of intelligent transportation. In this context, drivers or pedestrians equipped with sensor enabled smartphones collaborate to collect information about roads and traffic. However, the current solutions proposed for the use of crowdsensing for the collection of traffic related data adopt an opportunistic continuous sensing approach, which entails high resource consumption on the server and mobile device side, a high communication overhead, while offering little control of the users over the sensing activity. In this paper, we address these limitations by proposing an infrastructure-assisted on-demand crowdsensing approach for the real time detection and prediction of traffic conditions in an area of interest. Our approach combines the strengths of mobile crowdsensing, with the support of the mobile infrastructure, a multi-criteria algorithm for the participants' selection, and a deductive rule-based model for traffic condition estimation. The proposed solution was validated through a combination of prototyping and simulated traffic traces, and the results show a significant reduction in terms of resources' consumption and network overhead, while reaching high accuracy for the traffic condition estimation.

Hendrik E. Niemeijer

In this paper, I would like to discuss two extraordinary tales of two rather ordinary individuals in the service of the Dutch East India Company (henceforth: VOC), the first a Dutchman, Jacob Janssen de Roy, and the second a German, Georg Naporra (1731-1793). It is important to understand that all cross-cultural contacts between the peoples in the archipelago and westerners depended on seaborne trade and the vessels which plied the maritime routes. This was the only means of transportation and communication. As a consequence, cross-cultural contacts took place mainly in the port cities and coastal trading outposts. This can be clearly seen in the cases of our two ordinary Europeans: Jacob de Roy and Georg Naporra.

Xuerong Cui, Juan Li, Chunlei Wu et al.

Vehicle positioning technology has drawn more and more attention in vehicular wireless networks to reduce transportation time and traffic accidents. Nowadays, global navigation satellite systems (GNSS) are widely used in land vehicle positioning, but most of them are lack precision and reliability in situations where their signals are blocked. Positioning systems base-on short range wireless communication are another effective way that can be used in vehicle positioning or vehicle ranging. IEEE 802.11p is a new real-time short range wireless communication standard for vehicles, so a new method is proposed to estimate the time delay or ranges between vehicles based on the IEEE 802.11p standard which includes three main steps: cross-correlation between the received signal and the short preamble, summing up the correlated results in groups, and finding the maximum peak using a dynamic threshold based on the skewness analysis. With the range between each vehicle or road-side infrastructure, the position of neighboring vehicles can be estimated correctly. Simulation results were presented in the International Telecommunications Union (ITU) vehicular multipath channel, which show that the proposed method provides better precision than some well-known timing estimation techniques, especially in low signal to noise ratio (SNR) environments.