Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.
An intelligent transportation system (ITS) plays an important role in public transport management, security and other issues. Traffic flow detection is an important part of the ITS. Based on the real-time acquisition of urban road traffic flow information, an ITS provides intelligent guidance for relieving traffic jams and reducing environmental pollution. The traffic flow detection in an ITS usually adopts the cloud computing mode. The edge of the network will transmit all the captured video to the cloud computing center. However, the increasing traffic monitoring has brought great challenges to the storage, communication and processing of traditional transportation systems based on cloud computing. To address this issue, a traffic flow detection scheme based on deep learning on the edge node is proposed in this article. First, we propose a vehicle detection algorithm based on the YOLOv3 (You Only Look Once) model trained with a great volume of traffic data. We pruned the model to ensure its efficiency on the edge equipment. After that, the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm is optimized by retraining the feature extractor for multiobject vehicle tracking. Then, we propose a real-time vehicle tracking counter for vehicles that combines the vehicle detection and vehicle tracking algorithms to realize the detection of traffic flow. Finally, the vehicle detection network and multiple-object tracking network are migrated and deployed on the edge device Jetson TX2 platform, and we verify the correctness and efficiency of our framework. The test results indicate that our model can efficiently detect the traffic flow with an average processing speed of 37.9 FPS (frames per second) and an average accuracy of 92.0% on the edge device.
A smart city is an urban area that collects data using various electronic methods and sensors. Smart cities rely on Information and Communication Technologies (ICT) and aim to improve the quality of services by managing public resources and focusing on comfort, maintenance, and sustainability. The fifth generation (5G) of wireless mobile communication enables a new kind of communication network to connect everyone and everything. 5G will profoundly impact economies and societies as it will provide the necessary communication infrastructure required by various smart city applications. Intelligent Transporting System (ITS) is one of the many smart city applications that can be realized via 5G technology. The paper aims to discuss the impact and implications of 5G on ITS from various dimensions. Before this, the paper presents an overview of the technological context and the economic benefits of the 5G and how key vertical industries will be affected in a smart city, i.e., energy, healthcare, manufacturing, entertainment, and automotive and public transport. Afterward, 5G for ITS is introduced in more detail.
The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%-15% lower than the pre-pandemic level for at least one or two years.
To investigate the diversified technologies in Internet of Vehicles (IoVs) under intelligent edge computing, brain-inspired computing techniques are proposed in this study, which is a promising biologically inspired method by using brain cognition mechanism for various applications. A neuromorphic approach in a scalable and fault-tolerant framework is presented, targeting to realize the navigation function for the edge computing in IoV applications. A novel fault-tolerant address event representation approach is proposed for the spike information routing, which makes the presented model both scalable and fault-tolerant. Experimental results reveal that the proposed approaches can enhance the communication distance, the load balancing and the maximum throughput of the neuromorphic system accordingly. Based on the proposed neuromorphic model, the effects of the dopamine level are investigated. Besides, the results show that the proposed work can realize the accurate obstacle avoidance for the edge IoV computing, and the performance of the proposed network is superior to the network without the proposed scalable and fault-tolerant design. Therefore, the proposed IoV model provides an experimental basis for the improvement of the IoV system.
The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.
Emerson Philipe Sinesio, Marcele Elisa Fontana, Júlio César Ferro de Guimarães
et al.
<i>Background:</i> Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. <i>Methods:</i> Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. <i>Results:</i> The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. <i>Conclusions:</i> These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance.
Transportation and communication, Management. Industrial management
Abstract The selection and scheduling of tugboat matching schemes are key tasks in tugboat assistance operation management. With large ships requiring more tugboat assistance, a two-stage multi-criteria decision-making method is proposed. This includes a normal distribution-based multi-attribute group decision-making method with triangular fuzzy numbers to determine tugboat matching scheme reliability. A tugboat scheduling planning model for multiple berthing bases is then established, targeting the minimization of total fuel cost and total matching scheme reliability. This bi-objective problem is solved using the posteriori method, with actual data from Nansha Port validating the proposed method. Meanwhile, a priority-based encoding Memetic algorithm is designed to address the characteristics of the problem, and the solution results for 25 test cases generated from the actual data range of the Guangzhou Port are compared and analyzed using CPLEX, genetic algorithms, and simulated annealing algorithms. The results verify the feasibility of the proposed priority-based encoding Memetic algorithm. The enhanced multi-attribute group decision-making method helps decision-makers quickly select suitable matching schemes and optimize tugboat scheduling, demonstrating effective reliability evaluation and planning optimization.
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
The objective of this paper is the investigation of the parameters that influence the crash occurrence in the case of an unexpected incident. It draws inspiration from the Safety System Approach, which is built on the position that serious injury and death in transport networks cannot be ethically acceptable. To this end, the research takes into consideration parameters regarding both the road environment and the human factor. A driving simulator experiment takes place with the participation of 56 drivers, and the employment of the appropriate equipment (driving simulator, physiological parameters sensors and software, camera, synchronization software). It investigates the effect of different road and environment parameters (road category and complexity of the environment, visibility conditions), parameters of the driver (age, gender, mental workload), physiological parameters of the driver (heart rate, skin conductance, skin temperature) and the reaction time of the driver to the unexpected incidents, as well as the way of reaction (brake, maneuver, both, none). Furthermore, parameters of the vehicle are taken into consideration (speed, acceleration, headway). Α mixed logistic regression model is being developed for examining the relationship between the under study explanatory variables and the crash occurrence in unexpected incidents.
Compute nodes on modern heterogeneous supercomputing systems comprise CPUs, GPUs, and high-speed network interconnects (NICs). Parallelization is identified as a technique for effectively utilizing these systems to execute scalable simulation and deep learning workloads. The resulting inter-process communication from the distributed execution of these parallel workloads is one of the key factors contributing to its performance bottleneck. Most programming models and runtime systems enabling the communication requirements on these systems support GPU-aware communication schemes that move the GPU-attached communication buffers in the application directly from the GPU to the NIC without staging through the host memory. A CPU thread is required to orchestrate the communication operations even with support for such GPU-awareness. This survey discusses various available GPU-centric communication schemes that move the control path of the communication operations from the CPU to the GPU. This work presents the need for the new communication schemes, various GPU and NIC capabilities required to implement the schemes, and the potential use-cases addressed. Based on these discussions, challenges involved in supporting the exhibited GPU-centric communication schemes are discussed.
This study investigates the feasibility of the phase-matching measurement-device-independent quantum key distribution (PM-MDI QKD) protocol proposed by Lin and Lütkenhaus for satellite-based quantum communication. The protocol's key rate, known to exceed the repeaterless bound, is evaluated in the asymptotic limit under noisy conditions typical of satellite communications, including loss-only scenarios. The setup involves two ground-based parties connected via fiber (lossonly or noisy) and a space-based third party linked to one of these two ground-based parties through free-space communication. Simulations using the elliptic-beam approximation model the average key rate (AKR) and its probability distribution (PDR) across varying zenith angles and fiber distances. Down-link free-space communication is assessed under day and night conditions, with intensity optimization for each graphical point. Dynamic configurations of satellite and ground stations are also considered. Results indicate that AKR decays more slowly under loss-only conditions, while PDR analysis shows higher key rates produce more concentrated distributions. These findings demonstrate the potential of PM-MDI QKD protocols for achieving reliable key rates in satellitebased quantum communication.
Accurate traffic flow forecasting is crucial to improving traffic safety and alleviating road congestion for intelligent transportation network management. Recently, spatial-temporal graph-based deep learning methods have achieving significant performance improvements in traffic flow forecasting. However, they only consider spatial-temporal correlation of traffic network but ignore a mass of semantic correlation. In addition, they need to centralize data for training models, leading to privacy leakage concern. To tackle these problems, we introduce a federated learning-based intelligent traffic flow forecasting model that integrates our proposed spatial-temporal graph-based deep learning model into the devised Multilevel Federated Learning framework(MFL), named MFVSTGNN. This MFL is used to allow data collaboration among different data owners to train an efficient model without sharing their private data, while achieving the trade-off between communication overhead and computation performance. The proposed spatial-temporal graph-based deep learning model is composed of two phases. The first phase utilizes Variational Graph Autoencoder (VGAE) to dynamically generate adjacency matrix that contains both the spatial and semantic dependencies, contributing to preserving valuable information for improving prediction accuracy, and the second phase employs general spatial-temporal graph neural network to conduct prediction. We evaluate the performance of MFVSTGNN with two large-scale traffic datasets from California and Los Angeles County. The experimental results demonstrate the superior performance of MFVSTGNN in reducing communication overhead, and improving prediction accuracy, validating the effectiveness of our proposed model.
The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model’s security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system’s multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100% successful transmission probability and $0.01\sim 0.05~\lambda $ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.
Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth-generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.
The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifaceted environmental uncertainties. Thus, this article proposes a novel air transportation service management algorithm based on multi-agent deep reinforcement learning (MADRL) to address the challenges of multi-UAM cooperation. Specifically, the proposed algorithm in this article is based on communication network (CommNet) method utilizing centralized training and distributed execution (CTDE) in multiple UAMs for providing efficient air transportation services to passengers collaboratively. Furthermore, this article adopts actual vertiport maps and UAM specifications for constructing realistic air transportation networks. By evaluating the performance of the proposed algorithm in data-intensive simulations, the results show that the proposed algorithm outperforms existing approaches in terms of air transportation service quality. Furthermore, there are no inferior UAMs by utilizing parameter sharing in CommNet and a centralized critic network in CTDE. Therefore, it can be confirmed that the research results in this article can provide a promising solution for autonomous air transportation management systems in city-wide urban areas.
Unmanned aerial vehicles (UAVs) have been widely used in Intelligent Transportation Systems (ITS) due to their rapid deployment and high mobility, which are considered as a promising solution to expand the scope of communication, especially in inaccessible areas. However, there is a lack of a universal and extensible multi-hop collaboration model in the existing research on UAV-involved ITS. In this paper, we innovatively introduce a novel UAV-enabled multi-hop collaborative fog computing (FC) system model, in which several moving UAVs with unpredictable locations provide effective and efficient communication and computation services for ground user equipments (UEs). With this model, we mathematically formulate a joint user association, UAV association, task offloading, transmission power, computation resource allocation, and UAV location optimization problem, which is a mixed integer nonlinear programming (MINLP) problem and challenging to deal with. To solve the non-convex problem, we propose a novel multi-hop collaborative algorithm to derive the optimal task offloading and resource allocation decisions for each UAV. Simulation results demonstrate the superiority of the UAV-enabled multi-hop collaborative FC system and validate the effectiveness of the proposed scheme.
This purpose of the research is to explore the construction status and prediction performance of intelligent transportation systems in the road network of smart cities based on 5G (5th Generation Mobile Communication Technology) network, and further intellectualize the smart city. Aiming at the diversity and complexity of regional traffic influencing factors of road network in the construction of smart city, this research carries out resource real-time load balancing scheduling from the perspective of 5G heterogeneous network. Meanwhile, CNN (convolutional neural network) in the introduced deep learning algorithm is improved, and finally an intelligent traffic prediction model is constructed based on 5G load balancing and AlexNet network. The model is simulated and its performance is analyzed. The results show that the algorithm proposed is compared with LSTM (Long Short-Term Memory), CNN, RNN (Recurrent Neural Network), VGGNet (Visual Geometry Group Network), and BN (Bayesian network) models regarding Accuracy, Precision, Recall, and F1. It is found that the road network prediction accuracy of the algorithm proposed is 94.05%, which is at least 4.29% higher than that of the model algorithm proposed by other scholars. The analysis of network data transmission synchronization performance suggests that there are obvious performance improvements in access delay, access collision rate, reliability, and network throughput. Among them, the packet loss rate is lower than 0.1, the access collision rate is basically stable at about 0, the access time is stable at about 75ms, and the sending throughput is basically maintained at about 1, which is significantly better than the performance of other algorithms. Therefore, the intelligent transportation system can achieve better data transmission performance under the premise of ensuring high prediction performance, with prominent instantaneity, which can provide experimental basis for the intelligent development of transportation in smart cities.