Intelligent Transportation Services Research Articles

TRANSPORT SERVICE WEBSITE

Abstract: Transport is an awesome website that offers a wide range of shipping services. They specialize in transporting goods by ship, providing efficient and reliable solutions for both domestic and international shipping needs. Whether you need to transport large cargo or small packages, ShipEx has got you covered. Their website provides all the information you need about their services, including rates, tracking options, and customer reviews. It’s a convenient and trustworthy platform for all your shipping needs. Online transportation service has become a means of transportation development which able to attract people’s interest. This research explores the development and implementation of an intelligent transportation services website aimed at optimizing transportation services. Leveraging advanced algorithms, real-time data integration, and user-centric design, our platform facilitates seamless connections between various transportation modes, providing users with personalized, efficient, and sustainable goods transport options. Our findings highlight the positive impact of inclusive design on user satisfaction, accessibility, and overall usability, contributing to the broader discourse on creating equitable transportation solutions. This research explores the integration of augmented reality (AR) to improve user experience on transportation services websites. By overlaying real-time information onto the physical environment, our AR-enhanced platform provides users with intuitive navigation and enhanced situational awareness. We will ensure interactions and experience is top notch for the users. Index Term-: Management Processes, Transport Services, Strategic Functions, HTML, CSS, PHP.

Securing cooperative vehicular networks amid obstructing vehicles and mixed fading channels

Smart cities (SCs) were founded on the basis of the Internet of Vehicles paradigm, aiming to enhance the quality of life. Despite not having a unique composition, the intelligent transportation services (ITS) is a key component in most of the SCs. The ITS integrates the new communication systems along with the traditional transportation systems, forming a universal network of static and vehicular entities communicating over wireless broadcast channels. In addition to its vulnerability to co-channel interference (CCI), eavesdropping, and fading, the vehicle-to-vehicle communication channels could be subjected to shadowed fading. That is due to the probabilistic obstruction by big vehicles. Hence, degrading the signal-to-noise ratio at the receiving vehicle severely. This work evaluates the secrecy performance of a practical cooperative vehicular relaying network in terms of its secrecy outage probability (SOP). Due to the probabilistic existence of a vehicular obstacle, three different communication scenarios are considered and a novel closed-form analytical expression for the SOP is obtained over mixed Nakagami-m fading and Nakagami-N-Gamma shadowed fading channels. The system comprises legitimate source and destination operating in full-duplex mode, thus imposing CCI on both the relay and the passive eavesdropper. The result demonstrate the impacts of the secrecy data rate threshold, the strength of shadowing, the density of the obstructing vehicles, and several parameters on the SOP. High levels of shadowing strength and density of obstructing vehicles significantly degrades the secrecy performance by increasing the SOP. Accordingly, adopting the mixed fading channel model is of great importance for practical network modeling.

Joint resource allocation and security redundancy for autonomous driving based on deep reinforcement learning algorithm

AbstractAutonomous vehicles navigating urban roads require technology that combines low latency with high computing power. The limited resources of the vehicle itself compel it to offload task requirements to edge server (ES) for processing assistance. However, as the number of vehicles continues to increase, how edge servers reasonably allocate limited resources to autonomous vehicles becomes critical to the success of urban intelligent transportation services. This paper establishes an urban road scenario with multiple autonomous vehicles and an edge computing server and considers two main driving behaviour transition resource requests, namely car‐following behaviour requests and lane‐changing behaviour requests. Simultaneously, acknowledging that vehicles may encounter unforeseen traffic hazards when switching driving behaviours, a safety redundancy setting strategy is employed to allocate additional resources to the vehicle to ensure safety and model the vehicle resource allocation problem in the autonomous driving system. Double‐deep Q‐network (DDQN) is then used to solve this model and maximize the total system utility by comprehensively considering resource costs, system revenue, and autonomous vehicle safety. Finally, results from the simulation experiment indicate that the proposed dynamic resource allocation scheme, based on deep reinforcement learning for autonomous vehicles under edge computing, not only greatly improves the system's benefits and reduces processing delays compared to traditional greedy algorithms and value iteration, but also effectively ensures security.

Review of Intelligent Ship Path Planning Algorithms

Intelligent ship route planning has become an important research direction in the field of shipping in recent years. This paper first provides an overview of the basic theory of intelligent ship route planning. Secondly, various intelligent ship route planning algorithms are introduced, including methods based on A* algorithm, artificial potential field algorithm, RRT algorithm, and reinforcement learning. These algorithms analyze information such as ocean environment, predict sea conditions and traffic conditions, and consider ship dynamics and navigation safety constraints to provide efficient and safe navigation routes for ships. Finally, this paper points out the key issues and future development directions in intelligent ship route planning. The continuous innovation and application of intelligent ship route planning algorithms will provide more intelligent and efficient ship transportation services for the shipping industry, promoting the sustainable development of the shipping industry.

Secrecy performance evaluation and enhancement of vehicle-to-vehicle communications in the presence of big vehicles

Despite its significant role in providing and developing reliable and safe intelligent transportation services, the Internet of Vehicles faces several challenges and security threats, such as co-channel interference (CCI), node self-interference (SI), and eavesdropping. This paper investigates the secrecy performance of vehicular networks in terms of their secrecy outage probability (SOP) and average secrecy capacity (ASC) in the presence of an eavesdropper. The legitimate vehicles are assumed to operate in full-duplex mode. Hence, the impacts of CCI and SI are considered for practical modeling and performance evaluation. Moreover, the wireless communications among vehicles are assumed to be either subjected to Nakagami-m fading or Nakagami-N-Gamma shadowed fading due to the existence of a big vehicle and its location. Accordingly, closed-form mathematical expressions are derived for the SOP and the ASC. Furthermore, a power allocation (PA) model is proposed to improve the secrecy performance by maximizing the derived ASC. The numerical results validate the derived expressions by demonstrating the impact of the channel condition, the secrecy data rate threshold, the SI cancellation capability at the legitimate destination, the location of the eavesdropper, the location of the big vehicle, and the strength of shadowing on the SOP and the ASC. Also, it is shown that the proposed PA model can significantly enhance the secrecy performance.

Enhancing Indoor Navigation in Intelligent Transportation Systems with 3D RIF and Quantum GIS

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the “Three-Dimensional Routing Information Framework “(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF’s framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field.

Review on short-term traffic flow prediction methods under big data

The goal of traffic forecast is to predict the related traffic situation in the future according to the historical concept. The predicted angle can be divided into short-term prediction and long-term prediction. This method can be used to solve the increasingly serious urban traffic congestion problem, and researchers have proposed a deep learning model to help decision makers in the field of traffic control. It has made great contributions to improving future road capacity and optimizing intelligent transportation services. In this paper, short-term traffic forecast related documents under big data are helpful after sorting out, and the traffic flow data characteristics of intelligent transportation system and related correction methods are analysed. Secondly, it then classifies the applications involved by big data algorithm calculation according to various related principles, and summarizes the prediction accuracy, computational complexity, applicable interval and computation time of each type of algorithm in an overview. According to the relevant data, the combination forecasting model is effectively diversified, and combined with the related combination forecasting, I hope to improve the forecasting accuracy of the future development prospect.

Blockchain‐assisted twin migration for vehicular metaverses: A game theory approach

AbstractAs the fusion of automotive industry and metaverse, vehicular metaverses establish a bridge between the physical space and virtual space, providing intelligent transportation services through the integration of various technologies, such as extended reality and real‐time rendering technologies, to offer immersive metaverse services for vehicular metaverse users (VMUs). In vehicular metaverses, VMUs update vehicle twins (VTs) deployed in RoadSide Units (RSUs) to obtain metaverse services. However, due to the mobility of vehicles and the limited service coverage of RSUs, VT migration is necessary to ensure continuous immersive experiences for VMUs. This process requires RSUs to contribute resources for enabling efficient migration, which leads to a resource‐trading problem between RSUs and VMUs. Moreover, a single RSU cannot support large‐scale VT migration. To this end, we propose a blockchain‐assisted game approach framework for reliable VT migration in vehicular metaverses. Based on the subject logic model, we first calculate the reputation values of RSUs considering the freshness of interaction between RSUs and VMUs. Then, a coalition game based on the reputation values of RSUs is formulated, and RSU coalitions are formed to jointly provide bandwidth resources for reliable and large‐scale VT migration. Subsequently, the RSU coalition with the highest utility is selected. Finally, to incentivize VMUs to participate in VT migration, we propose a Stackelberg model between the selected coalition and VMUs. Numerical results demonstrate the reliability and effectiveness of the proposed schemes.

A brief survey on cooperative intelligent transportation systems and applications

Cooperative Intelligent Transport Systems (C-ITS) have emerged to provide a range of intel-ligent transport services that optimize traffic management, improve the travel experience of road users, increase road safety and security, and reduce the negative environmental impacts of transport. C-ITS, which aim to ensure that intelligent transport services function effec-tively and efficiently, communicate and exchange data with vehicles, road users, traffic con-trol centers, and road infrastructure as well. In this study, an overview of C-ITS related ac-tivities in the world and in Türkiye are discussed. C-ITS studies in literature and the objec-tives and benefits of C-ITS, its stakeholders, its relationship with ITS, its components and services are examined. After summarizing C-ITS studies in the world, policies and strategies in Europe, USA and Asian countries, and the current situation in Türkiye is provided. In addition, since there are limited studies on C-ITS in Türkiye, where ITS is still in its devel-opment stage, it is aimed to contribute to the literature by presenting recommendations for Türkiye to support the dissemination of C-ITS.

Field demonstration of advanced autonomous navigation technique for a fully unmanned surface vehicle in complex coastal traffic areas

AbstractThis study introduces an advanced autonomous navigation algorithm for unmanned surface vehicle (USV) operations in complex coastal traffic areas. To facilitate the undertaking of tests for innovative technologies, the Korean government has designated regulation‐free special zones, thereby enabling field tests for USV demonstrations without an onboard safety person on real manned‐ship navigation routes. To enable real‐world USV operation without onboard safety person, an existing autonomous navigation algorithm is extended to achieve sufficiently reliable and robust USV operation. Accordingly, berthing and unberthing algorithms are newly developed for unmanned operation, and enhanced situational awareness algorithms are applied to detect all types of obstacles, including small‐sized floating objects spread over a wide range, such as a fish farm. In particular, the Korean intelligent maritime transportation service, called e‐Navigation, is incorporated into the autonomous navigation algorithms to overcome the limitations of vehicle autonomy based on onboard sensors. To demonstrate the capability of fully unmanned operation in real marine traffic scenarios, long‐distance navigation (54 km on a single voyage) was conducted by running several missions, including navigation in complex fish farm areas, maritime surveys, collision avoidance, navigation on real traffic routes, and emergency response. The extended navigation algorithms and results of the field tests are presented and discussed in this paper.

A Lightweight Privacy-preserving Path Selection Scheme in VANETs

<p>With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure and efficient road navigation while protecting privacy is a crucial issue. In this paper, we propose a privacy-preserving path selection protocol that supports a token as the object in the oblivious transfers, which effectively reduces the communication overhead. In addition, a lightweight dual authentication and group key negotiation protocol is provided to support dynamic joining or leaving of group members. Moreover, it can guarantee the security of forward data. After experimental analysis, the proposed protocol has high security and efficiency.</p> <p> </p>

Assessment of Intelligent Transportation Services in Greater Cairo: The Case of Green Bus

Assessment of Intelligent Transportation Services in Greater Cairo: The Case of Green Bus

Legal Study of Dynamic Pricing Strategy in Companies Providing Intelligent Transportation Services with Emphasis on Snapp Application

Legal Study of Dynamic Pricing Strategy in Companies Providing Intelligent Transportation Services with Emphasis on Snapp Application

A virtual models-based CAVs platoon resilient to network and sensor attacks

The most typical topological structure of Connected and Autonomous cars (CAVs) is a platoon formation, in which vehicles move closely together, following the leader’s instructions, and offer autonomous and intelligent transport services. Nevertheless, several attacks that inject false data into networks and onboard sensors may endanger platoon stability, resulting in severe collisions over road networks in the worst case. This paper proposes a mechanism for cooperative CAV platoon recovery in the face of false data injection attacks called PReCAV. PReCAV employs virtual and physical system models to maintain the resilience of the CAV platoon despite network and sensor attacks. An evaluation by NS3 and SUMO simulations showed that PReCAV provided stability and resilience to the CAV platoon formation in a representative scenario face to false data injection attacks in the network and the sensors. For injecting false data into the network and sensors, we defined an attack model that manipulates information, such as speed and localization, using a uniform distribution to vary the falsification size over time, that is, how far does the information value vary from the correct one. During network and sensors attacks, PReCAV showed average values of 0.16 m/s2 acceleration, 21.76 m/s of speed, and 28.35 m of the distance between vehicles in the platoon when the desired speed was 22 m/s. Meanwhile, for a desired speed of 15 m/s, PReCAV maintained average values of 0.11 m/s2, 15.26 m/s, and 22.42 m in the platoon. Those results demonstrate the ability of PReCAV to maintain vehicle dynamics in the platoon in the face of attacks.

Mobility 5.0: Smart Logistics and Transportation Services in Cyber-Physical-Social Systems

The logistics and transportation industry has made significant progress in improving sustainability, efficiency, and ac- cessibility, primarily due to the continuous development of tech- nologies, including clean energy, advanced algorithms, unmanned vehicles, and privacy-enhancing techniques. Along with these ad- vancements, the Mobility 5.0 paradigm has been proposed in the ITSS Intelligent Vehicle 5.0 project meeting. This letter discusses the changes in the supply and demand of mobility resources and looks at the basic components on which the implementation of the Mobility 5.0 relies on. By integrating emerging technologies into Cyber-Physical-Social Systems (CPSS), we believe that Mobility has tremendous potential in shaping a new era of intelligent logistics and transportation services and will bring significant benefits to individuals, businesses, the government, and the entire society.

Evolution of cooperation in vehicular cloud assisted networks for ITS services: A hunt game-based approach

Modern-day vehicles that are generally equipped with advanced sensors, devices, and communication technologies and sophisticated cloud computing facilities paved the way for vehicular cloud-assisted networks. It has the potential to effectively offer a broad range of on-demand services to help intelligent transportation systems sustain current traffic loads. The system can be fully functional only when the vehicles remain available and cooperate to form such a system and provide services. However, the behavior of the nodes is highly dependent on the neighborhood environment and the needs of the owner. Hence, nodes may bail out from the system at random. Therefore, the stimulation of cooperation becomes a research challenge. In this work, we analyzed the behavior of nodes by modeling the event of intelligent transportation service provisioning in a vehicular cloud-assisted network as a cooperative hunt game to find a sufficient condition under which cooperation could be evolved. Subsequently, an incentive-based cooperation scheme is proposed that guarantees optimal incentives to the nodes depending upon their roles and the amount of participation in the network activities. To evaluate the scheme, we have created a static vehicular cloud testbed that provides Intelligent Transportation services like optimal route information to vehicles. Evaluation results show that our scheme brings about conditions where obeying network protocols and serving faithfully to the system becomes the most suitable and stable strategy for nodes. We have also analyzed various network parameters such as end-to-end delay, packet delivery ratio, normalized acceptance rate, normalized delivery rate, etc., that show the improvement in network performance as compared to other strategies.

Deep learning-based selective spectrum sensing and allocation in cognitive vehicular radio networks

The main challenge with Vehicular Ad-Hoc Networks (VANETs) for assisting Intelligent Transportation Services (ITSs) is ensuring effective data delivery under various network circumstances despite the scarcity of radio frequency spectrum channels. Meanwhile, Dynamic Spectrum Access (DSA) utilizing Cognitive Radio (CR) technology shows its potential to solve the channel shortage issue. Reliable Spectrum Sensing (SS) and Opportunistic Spectrum Allocation (OSA) are the two most important and challenging aspects of deciding the success of the deployment of CR-enabled VANETs (CR-VANETs). In this paper, we propose three relevant issues of CR-VANETs in a single framework, i.e., reliable Cooperative Spectrum Sensing (CSS), channel indexing for selective SS, and best channel allocation to the CR users. In CSS, we use the local SS decision with more critical attributes like the geographical position of sensing signal acquisition and timestamp to obtain the global CSS session using the Deep Reinforcement Learning (DRL) technique. Selective channel-based spectrum sensing is essential to minimize the sensing overload by CR users. The present work uses the time series analysis through the deep learning-based Long short-term memory (LSTM) model for indexing the primary user channels for selective SS. Finally, for the channel allocation to the CR-VANETs, we model the complex environment as a Partial Observable Markov Decision Process (POMDP) framework and solve using a value iteration-based algorithm. The simulation results show the proposed work's efficacy over the existing works.

Joint C-V2X Based Offloading and Resource Allocation in Multi-Tier Vehicular Edge Computing System

Emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be supported by a multi-tier computing system composed of vehicular edge computing (VEC) servers along the roads and micro servers on vehicles. In this work, we investigate the dual Uu/PC5 interface offloading and resource allocation strategy in Cellular Vehicle-to-Everything (C-V2X) enabled multi-tier VEC system. The successful transmission probability is characterized to obtain the normalized transmission rate of PC5 interface. We aim to minimize the system latency of task processing while satisfying the resource requirements of Uu and PC5 interfaces. Due to the non-convex and variables coupling, we decompose the original problem into two subproblems, i.e., resource allocation and offloading strategy subproblems. Specifically, we derive the closed-form expressions of packet transmit frequency of PC5 interface, transmission power of Uu interface, and CPU computation frequency in the resource allocation subproblem. Moreover, for the offloading strategy subproblem, the offloading ratio matrix is obtained by proposing the PC5 interface based greedy offloading (PC5-GO) algorithm, which concludes offloading decision and ratio. Simulation results are provided that the proposed PC5-GO algorithm can significantly improve the system performance compared with other baseline schemes by 13.7% at least.

Deep Reinforcement Learning-Based Intelligent Security Forwarding Strategy for VANET.

The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning (DRL). The key supporting elements of the model in the strategy are reasonably designed in combination with the scenario, and sufficient training of the model is carried out by deep Q networks (DQN). In the strategy, the state space is composed of the distance between candidate and destination nodes, the security attribute of candidate nodes and the type of message to be sent. The node can adaptively select the routing scheme according to the complex state space. Simulation and analysis show that the proposed strategy has the advantages of fast convergence, well generalization ability, high transmission security, and low network delay. The strategy has flexible and rich service patterns and provides flexible security for VANET message services.

Mobility Challenges in the Cities of the Future

Autonomous vehicles have become a logical consequence of implementing the Intelligent Transport Systems strategy. The article analyses the directions of road vehicles intellectualization. This is due to the implementation of new paradigms in the functioning of the Smart City transport system to solve the problems of mobility and logistics, as well as vehicle service. The article presents the concept of an intelligent passenger transportation service called “Social Mobility on Demand”, which allows passengers to choose an acceptable option using a set of options, as well as operators to optimally distribute cars for different travel types, which will reduce the load on the road network, as well as the negative impact on the environment. It is shown that in order to manage the reliability of connected vehicles, it is necessary to improve proprietary maintenance and repair systems. The concept of a branded service system in the transition to fundamentally new modes of transport is described, which will allow maintaining their working condition and increasing the safety and stability of the transport system.