Intelligent Transportation Systems Applications Research Articles

Management of Positioning Functions in Cellular Networks for Time-Sensitive Transportation Applications

Device positioning has generally been recognized as an enabling technology for numerous vehicular applications in intelligent transportation systems (ITS). The downlink time difference of arrival (DL-TDOA) technique in cellular networks requires range information of geographically diverse base stations (BSs) to be measured by user equipment (UE) through the positioning reference signal (PRS). However, inter-cell interference from surrounding BSs can be particularly serious under poor network planning or dense deployments. This may lead to a relatively longer measurement time to locate the UE, causing an unacceptable location update rate to time-sensitive applications. In this case, PRS muting of certain wireless resources has been envisioned as a promising solution to increase the detectability of a weak BS. In this paper, to reduce UE measurement latency while ensuring high location accuracy, we propose a muting strategy managed by positioning functions that utilizes a combination of optimized pseudo-random sequences (CO-PRS) for multiple BSs to coordinate the muting of PRS resources. The original sequence is first truncated according to the muting period, and a modified greedy selection is performed to form a set of control sequences as the muting configurations (MC) with balance and concurrency constraints. Moreover, efficient information exchange can be achieved with the seeds used for regenerating the MC. Extensive simulations demonstrate that the proposed scheme outperforms the conventional random and ideal muting benchmarks in terms of measurement latency by about 30%, especially when dealing with severe near-far problems in cellular networks.

A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system’s efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications’ inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle’s proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge Computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle’s computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the scheduling overhead, improves the utilization of system resources, and minimizes the data transmission delay caused by vehicle motion.

Toward a Generic Vehicular Cloud Network Architecture: A Case of Virtual Vehicle as a Service

In recent years, cloud computing has gained more and more popularity. The motivation towards implementing cloud computing in vehicular networks is due to the availability of communication, storage, and computing resources represented by communication, vehicles, roadside units (RSUs), and central servers. These resources can be utilized and provided to vehicles, drivers on the road, travellers, and customers on the internet. Intelligent Transportation System (ITS) applications can utilize vehicular cloud computing to provide efficient real-time services, as well as to improve transportation safety, mobility, and comfort levels for drivers. In this paper, all possible vehicular cloud models are presented. Each vehicular cloud model offers different services. Integrating all vehicular cloud models into one integrated system will provide all services and serve internet users, passengers, and vehicles. Therefore, a generic vehicular cloud model is proposed. After that, a new service called Virtual Vehicle is proposed in vehicular cloud computing. The virtual vehicle is a virtual machine that migrates from one physical vehicle to another. It provides the same services as the physical vehicle according to the consumer's requirements.

Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks

Short-term traffic flow prediction is a significant and challenging research topic as it is closely related to the application of intelligent transportation systems. Due to the variable and random characteristics of the transportation system, raw traffic flow data often contain noise, and predicting the raw data directly may reduce the accuracy and effectiveness of the prediction models. Therefore, a hybrid method is established in this research which combines denoising schemes and deep learning models to improve the prediction accuracy. The time series denoising schemes include two parts: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and wavelet packet decomposition (WPD). Firstly, the raw traffic flow data are decomposed by CEEMDAN to obtain intrinsic mode functions (IMFs) and a residual. Then the IMFs are divided into anti-persistent and persistent components through the Hurst Exponent index. The anti-persistent components are re-decomposed by the WPD algorithm, and persistent components are aggregated into one component. Finally, these components and residual are forecasted by the deep echo state network (DeepESN) model. In the experiment, to investigate the prediction performance of the proposed CEEMDAN-WPD123456–7a11-DeepESN model, the LSTM, CEEMDAN-LSTM, CEEMDAN-WPD-LSTM, DeepESN, CEEMDAN-DeepESN, CEEMDAN-WPD1-DeepESN, CEEMDAN-WPD123456-DeepESN and CEEMDAN-WPD1a6–7a11-DeepESN models are considered to be comparison models. The experimental results demonstrate that the proposed model has superior performance on both efficiency and accuracy.

City-scale Vehicle Trajectory Data from Traffic Camera Videos

Vehicle trajectory data underpins various applications in intelligent transportation systems, such as traffic surveillance, traffic prediction, and traffic control. Traditional vehicle trajectory datasets, recorded by GPS devices or single cameras, are often biased towards specific vehicles (e.g., taxis) or incomplete (typically < 1 km), limiting their reliability for downstream applications. With the widespread deployment of traffic cameras across the city road network, we have the opportunity to capture all vehicles passing by. By collecting city-scale traffic camera video data, we apply a trajectory recovery framework that identifies vehicles across all cameras and reconstructs their paths in between. Leveraging this approach, we are the first to release a comprehensive vehicle trajectory dataset that covers almost full-amount of city vehicle trajectories, with approximately 5 million trajectories recovered from over 3000 traffic cameras in two metropolises. To assess the quality and quantity of this dataset, we evaluate the recovery methods, visualize specific cases, and compare the results with external road speed and flow statistics. The results demonstrate the consistency and reliability of the released trajectories. This dataset holds great promise for research in areas such as unveiling traffic dynamics, traffic network resilience assessment, and traffic network planning.

Design of auto obstacle avoidance system based on machine learning under the background of intelligent transportation

AbstractWith the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane‐changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane‐changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.

Adaptive and Simultaneous Trajectory Prediction for Heterogeneous Agents via Transferable Hierarchical Transformer Network

Simultaneously and accurately predicting trajectories of multiple heterogeneous agents is crucial for intelligent transportation systems (ITS) applications, e.g., connected and autonomous vehicles. Existing model-based and data-driven methods can achieve good prediction accuracy, but most of them neglect the domain shift issue and prevalent imperfect data problems, i.e., few-shot learning and zero-shot learning issues. To address these issues, we propose a multi-source transfer learning (TL) framework, transferable hierarchical Siamese Transformer network (T-HSTN), for trajectory prediction of multiple heterogeneous agents, e.g., vehicles, bicycles, and pedestrians, at urban unsignalized intersections under small data conditions. Specifically, by extending the self-attention mechanism and exploring feature representations of traffic scenes, a Transformer-based network that hierarchically extracts temporal/spatial features and map features is introduced as the basic prediction model. Moreover, a TL framework with adaptive learning and feature alignment modules is built to explore the feature representations of unfixed traffic scenes and align both statistical and deep features to learn domain-invariant knowledge. More challenging trajectory prediction experiments are designed, corresponding to newly-built or badly-instrumented intersections under real-world scenarios. Experimental results verify the proposed method’s high accuracy, transferability, and generability. Our work fills the gap in solutions and benchmarks for TL tasks in trajectory prediction for heterogeneous agents. The conducted TL experiments provide a more practical setting of considering imperfect data problems in trajectory prediction.

E-detector: Asynchronous Spatio-temporal for Event-based Object Detection in Intelligent Transportation System

In intelligent transportation systems, various sensors, including radar and conventional frame cameras, are used to improve system robustness in various challenging scenarios. An event camera is a novel bio-inspired sensor that has attracted the interest of several researchers. It provides a form of neuromorphic vision to capture motion information asynchronously at high speeds. Thus, it possesses advantages for intelligent transportation systems that conventional frame cameras cannot match, such as high temporal resolution, high dynamic range, as well as sparse and minimal motion blur. Therefore, this study proposes an E-detector based on event cameras that asynchronously detect moving objects. The main innovation of our framework is that the spatiotemporal domain of the event camera can be adjusted according to different velocities and scenarios. It overcomes the inherent challenges that traditional cameras face when detecting moving objects in complex environments, such as high speed, complex lighting, and motion blur. Moreover, our approach adopts filter models and transfer learning to improve the performance of event-based object detection. Experiments have shown that our method can detect high-speed moving objects better than conventional cameras using state-of-the-art detection algorithms. Thus, our proposed approach is extremely competitive and extensible, as it can be extended to other scenarios concerning high-speed moving objects. The study findings are expected to unlock the potential of event cameras in intelligent transportation system applications.

Multi-stage deep residual collaboration learning framework for complex spatial–temporal traffic data imputation

Performing accurate and efficient traffic data repair has become an essential task before proceeding with other applications of intelligent transportation systems. However, existing repair models often impose assumptions on the underlying data generation process or fail to sufficiently capture the complex spatial–temporal dependencies between several nodes at different intervals. To address these issues, we propose a novel multi-stage deep residual collaboration learning framework (named Multi-DRCF) to tackle the complex task of repairing missing traffic data. In particular, a spatiotemporal fusion network (i.e., ST-imputator) incorporating the graph-based convolutions with recurrent structures is introduced for modeling the spatial topology and dynamic temporal coherences of the traffic graph network in turn. Moreover, to further achieve both higher accuracy and efficiency, we exploit a stackable bi-directional residual optimization (i.e., Bi-RO) structure to enhance the role of ST-imputator in the utilization of the spatiotemporal properties of the residuals. Indeed, this combination of ST-imputator and Bi-RO structure can serve as an iterable unit for Multi-DRCF, and the whole computation process is highly modular and flexible in operation. We evaluate Multi-DRCF using two real-world large-scale traffic speed datasets, i.e., Seattle-Loop and METR-LA. Experimental results demonstrate that Multi-DRCF can effectively extract the spatial and temporal properties from traffic data, resulting in an excellent repair performance with an acceptable computational cost. Compared with the state-of-the-art baseline models, Multi-DRCF performs more competitively for three types of missing patterns, and also provides steadier repair results with missing rates ranging from 10% to 90%. Finally, the ablation experiments and visualization analysis also present well insights for better understanding the superiority of the Multi-DRCF for complex spatial–temporal traffic data imputation.

REGULATORY AND LEGAL ENSURING THE SUSTAINABLE DEVELOPMENT OF TRANSPORT AND LOGISTICS SYSTEMS

Topicality. The scientific article is devoted to the research analysis of the peculiarities of legal regulation and the construction of the architecture of the intellectualization of transport systems. Transport communications unite companies around the world and are a necessary prerequisite for the integrity of the country and its integration into the world economy through foreign economic activity. In this regard, the transport system is directly related to the economy and the social sphere. The transport policy of any state is characterized by national priorities at the micro and macro levels. A competitive transport system must have the following characteristics: be economically, ecologically and socially sustainable; to be able to recover effectively after natural disasters; have a developed and reliable IT infrastructure.The use of information systems in traffic management is associated with the rapid development of modern cities, an increase in the number of rolling stocks on the streets, new routes and other features. As a result, the amount of information needed for analysis and making operational decisions has increased significantly. The information component will become an integral part of city life. And for more effective use of all the possibilities of intelligent transport systems (ITS), legal regulation is necessary, which includes data protection, standardization, rules for the application of ITS achievements and their further development.Aim and tasks. The purpose of the research is to study the legal foundations of the concept of transport and logistics systems sustainable development.Materials and Methods. The research methodology is based on the analysis of legislative and legal methods of ensuring transport and logistics flows, economic and legal analysis using the general legal framework and special legal acts that regulate the activity of transport and logistics systems. The problem of intellectualization of environmental protection activities is considered both through structural changes in the international economy, through modern processes of development of national transport and logistics systems, as well as through the study of the complex network structure of global logistics chains, where both approaches complement each other.Research results. The rapid development of modern cities, the increase of vehicles on the streets, the development of new routes increases the amount of information necessary for analysis and making operational decisions. This, in turn, stimulates the active use of intelligent information technologies. And the creation of legal incentives for the development of the intellectualization of the transport and logistics system is one of the factors of strengthening the economy and improving the welfare of the population.Conclusion. The conducted research made it possible to conduct an analysis of legal regulation and stimulation of the development of intelligent transport systems (ITS). In particular, at the international and regional levels. The main necessary elements of the comprehensive application of intelligent transport systems are standardization at the international level, in order to be able to create a global system for more efficient use of transport and achieving global goals in the safety of movement and reducing the impact of transport on the environment.

ITS-PRO-FLOW: A NEW ENHANCED SHORT-TERM TRAFFIC FLOW PREDICTION FOR INTELLIGENT TRANSPORTATION SYSTEMS

Short-term traffic flow prediction plays a significant role in various applications of intelligent transportation systems (ITS), such as road traffic control and route guidance. This requires the development of intelligent prediction approaches for accurate and timely traffic flow information. To handle this issue, this paper emphasizes the potential of a new idea to propose a high-quality and intelligent prediction of short-term traffic flow in ITS. The proposed model, referred to as ITS-Pro-Flow, takes the benefits of the well-known Profile-Energy (Pro-Energy) as a landmark solution, relying on past observations and current conditions to forecast future short-term traffic flow volume. ITS-Pro-Flow has an effective prediction mechanism due to its unique enhancements over Pro-Energy. The distinctive feature of ITS-Pro-Flow is that it dynamically adjusts the contributions of past predictions and current observations for a particular prediction, which is equally performed in Pro-Energy. We prove the performance of ITS-Pro-Flow through extensive simulations with 2 datasets, in comparison to Pro-Energy and IPro-Energy. Performance results clearly indicate that ITS-Pro-Flow provides more accurate predictions than other schemes.

ENABLING INTELLIGENT TRANSPORTATION SYSTEM (ITS) APPLICATION FOR SMART PARKING MONITORING SYSTEM (SPMS) AT URBAN ENVIRONMENT

Smart City conceptual development is as an approach to solve urbanization issues and improve the quality of life. Among others, the needs of Smart City are focused to enhanced the users of smart mobility. Taking action to reduce and control pollution levels is a major priority because Urban mobility accounts for 40% of all CO2 emissions of road transport and up to 70% of other pollutants from transportation record. The study was performed by reviewing and highlighting existing published studies on Intelligent Transportation System (ITS) application for smart parking monitoring system and performing gap analysis. This paper reviewed recent research towards smart parking monitoring system highlighting its implementation stage, factors and impacts.

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.

Terahertz Communication with MIMO-OFDM in FANETs for 6G

Background: Unmanned aerial vehicles (UAVs) are envisioned in several intelligent transport system (ITS) application fields due to their autonomous operation, mobility, and communication/processing capabilities. Methods: In order to increase performance, dependability, and customer satisfaction, wireless communications are being used for essential and noncritical services in a variety of transportation systems. Terahertz (THz) communication aims to raise the wireless communication frequency band to the terahertz level and is one of the technologies that shows the most promise in addressing needs like the exponential increase in the number of devices, the necessity for higher data rates, and the need for larger channel capacity. For meeting the demands of 5G and 6G users, the hybrid combination of multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) appears to be an excellent technology. Therefore, it is essential to investigate the performance of THz communication with MIMO-OFDM in flying ad hoc networks (FANETs) for 6G. FANETs are multi-UAV systems arranged in an ad hoc way. Results: In this paper, the performance of the THz communication with MIMO-OFDM in FANETs for 6G is evaluated. The Markov chain model-based analytical study is used to derive how parameters and performance measures relate to one another. Conclusion: The equations for path loss, outage probability, bit error rate, and throughput are attained. Moreover, numerical results are shown to justify the outcomes of the analytical studies.

Retracted: Analysis of Intelligent Transportation System Application Based on Internet of Things and Big Data Technology under the Background of Information Society

Retracted: Analysis of Intelligent Transportation System Application Based on Internet of Things and Big Data Technology under the Background of Information Society

A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7.

Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and traffic safety. However, due to the complexity and variability of traffic environments and the small size of traffic signs, detecting small traffic signs in real-world scenes remains a challenging problem. In order to improve the recognition of road traffic signs, this paper proposes a small object detection algorithm for traffic signs based on the improved YOLOv7. First, the small target detection layer in the neck region was added to augment the detection capability for small traffic sign targets. Simultaneously, the integration of self-attention and convolutional mix modules (ACmix) was applied to the newly added small target detection layer, enabling the capture of additional feature information through the convolutional and self-attention channels within ACmix. Furthermore, the feature extraction capability of the convolution modules was enhanced by replacing the regular convolution modules in the neck layer with omni-dimensional dynamic convolution (ODConv). To further enhance the accuracy of small target detection, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to mitigate the sensitivity to minor positional deviations of small objects. The experimental results on the challenging public dataset TT100K demonstrate that the SANO-YOLOv7 algorithm achieved an 88.7% mAP@0.5, outperforming the baseline model YOLOv7 by 5.3%.

Trajectory-Based Spatiotemporal Multi-Task Multi-Graph Network for Traffic State Prediction

As a critical application in intelligent transportation systems, traffic state prediction still faces various challenges, such as unsatisfactory capability of utilizing multi-source data and modeling spatiotemporal network relevancies. Therefore, we propose a trajectory-based multi-task multi-graph convolutional network (Tr-MTMGN), a novel spatiotemporal deep learning framework for traffic state prediction on a citywide scale. This method firstly mines the underlying information from vehicle trajectories and designs a multi-graph convolution block to investigate spatial correlations. Sequentially, the multi-head self-attention layer is integrated into the multi-task learning framework to capture the temporal dependencies of the traffic state. The proposed model was evaluated on field data collected in Zhangzhou, China, and demonstrated superior performance when compared with existing state-of-the-art baselines.

An RFID Tag Movement Trajectory Tracking Method Based on Multiple RF Characteristics for Electronic Vehicle Identification ITS Applications.

Intelligent transportation systems (ITS) urgently need to realize vehicle identification, dynamic monitoring, and traffic flow monitoring under high-speed motion conditions. Vehicle tracking based on radio frequency identification (RFID) and electronic vehicle identification (EVI) can obtain continuous observation data for a long period of time, and the acquisition accuracy is relatively high, which is conducive to the discovery of rules. The data can provide key information for urban traffic decision-making research. In this paper, an RFID tag motion trajectory tracking method based on RF multiple features for ITS is proposed to analyze the movement trajectory of vehicles at important checkpoints. The method analyzes the accurate relationship between the RSSI, phase differences, and driving distances of the tag. It utilizes the information weight method to obtain the weights of multiple RF characteristics at different distances. Then, it calculates the center point of the common area where the vehicle may move under multi-antenna conditions, confirming the actual position of the vehicle. The experimental results show that the average positioning error of moving RFID tags based on dual-frequency signal phase differences and RSSI is less than 17 cm. This method can provide real-time, high-precision vehicle positioning and trajectory tracking solutions for ITS application scenarios such as parking guidance, unmanned vehicle route monitoring, and vehicle lane change detection.

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domain-specific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNN-based frameworks.

ITS Based on Deep Graph Convolutional Fraud Detection Network Blockchain-Enabled Fog-Cloud

The advancement in transport applications increases at the everyday progress in technologies. Therefore, intelligent transport systems (ITS) gain a lot of progress at the different vehicle levels and in the vehicular area network. However, privacy and security at the network level are critical issues for ITS applications in the existing mechanism. In this paper, the study devises the cost-efficient and secure Serverless Blockchain Enable Task Scheduling (SBETS) ITS system and algorithm framework. The main goal is to reduce processing and security blockchian costs for ITS applications in the system. The processing cost minimizes based on the new proposed function-based price model and secures the data by a suggested deep graph convolutional neural network scheme in the network. The simulation results show that SBETS outperformed all existing ITS systems and minimized processing costs by 10% and fraud detection issues by 50% for transport applications.