Transportation System Applications Research Articles

Analyzing Power Systems and Power Electronics Interdependencies: A DEMATEL Method Approach

Energy research encompasses all aspects of electrical energy, focusing on innovation in energy production and delivery, alternative resources, and efficient devices. It involves studying systems and equipment for converting, providing, and utilizing energy as electricity. Power electronics have become essential to power systems, enhancing quality and efficiency and promoting the development of intelligent, efficient energy solutions. There are various types of power electronics within power systems. The architectural study of converting electrical energy from one form to another falls under power electronics. Globally, electronics recycle or recover more than 80% of the total electricity produced, averaging 3.4 billion kilowatt-hours annually. Power electronics converters, also known as power converters or switching converters, are used to process or convert electrical energy. Electricity exists as AC power and DC power, leading to the classification of distribution systems into AC and DC based on the type of power used. Power system analysis is crucial for designing electrical power systems. It involves calculations and simulations to ensure that the electrical system and its components are appropriately specified to function as intended, endure expected stress, and be protected from failures. Advantages of power electronics include high power density electricity and improved efficiency, reaching up to 99% in energy conversion. Their efficiency and reliability make switching power supplies suitable for medical devices with acoustically sensitive applications. Power system reliability addresses issues like service interruptions and power outages, often guided by specific codes relevant to consumers. Common reliability indices in the US include SAIFI, SAIDI, and CAIDI. The DEMATEL (Decision Making Trial and Evaluation Laboratory) method is applied across various industries such as non-metal mineral products, general equipment manufacturing, coal mining and washing, textiles, and food manufacturing. DEMATEL visualizes and assesses the interactions and dependent relationships between factors through a structural model, identifying critical elements. Evaluation parameters for power electronics include their application in power systems, transportation systems, energy conservation, heating and lighting control, and renewable energy integration. Power electronics in power systems are ranked highest, while energy conservation is ranked lowest.

Broadcast signcryption scheme with equality test in smart transportation system

With the generation of massive traffic information in the smart transportation system, the traffic control center efficiently utilizes broadcast communication to send multiple messages to multiple vehicles. Besides, diversified privacy disclosure and security attack issues also emerged spontaneously. To achieve secure communication between the traffic control center and vehicles in the smart transportation system, we design a broadcast signcryption scheme with equality test in the smart transportation system based on the certificateless cryptosystem and equality test. The scheme realizes message confidentiality and vehicle privacy by using the Lagrange interpolation theorem to encrypt messages and vehicle identities, while also achieving classify ciphertext by using the equality test and facilitate road traffic information management. Through numerical experiment analysis, the proposed work has higher operation efficiency and is more suitable for application in smart transportation systems.

STC-PSSA: A New Model of Traffic Flow Forecasting Based on Spatiotemporal Convolution and Probabilistic Sparse Self-Attention

Traffic flow forecasting is the foundation of the dynamic control and application of intelligent transportation systems (ITS). It is also of significant practical value in alleviating road congestion. Given the periodic and dynamic changes in traffic flow and the spatiotemporal coupling interaction of complex road networks, traffic flow forecasting is challenging and rarely yields satisfactory prediction results. To capture the dynamic spatiotemporal characteristics of traffic flow, a new model of traffic flow forecasting based on spatiotemporal convolution and probabilistic sparse self-attention (STC-PSSA) is proposed. It consists of a spatiotemporal graph convolution network (ST-GCN) module, a spatiotemporal convolution module (ST-Conv), and a probabilistic sparse attention module (PSSA). ST-GCN consists of the gated temporal convolutional network (G-TCN) and the graph convolution network (GCN), which are used to capture the temporal dependence and spatial correlation of the traffic flow, respectively. Multiple ST-GCNs are stacked to handle spatial features at various time levels. The ST-Conv captures intricate temporal dependence at the same location and dynamic spatial features at neighboring locations simultaneously. The PSSA combines dynamic spatiotemporal features and performs long-term forecasting efficiently. The experimental results demonstrate that the STC-PSSA model can accurately extract the dynamic spatiotemporal characteristics of traffic flow and outperforms the popular baseline methods in forecasting accuracy.

Next track point prediction using a flexible strategy of subgraph learning on road networks

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.

Utilizing YOLOv8 for enhanced traffic monitoring in intelligent transportation systems (ITS) applications

The increasing demand for artificial intelligence-based motor vehicle detection in Intelligent Transportation Systems (ITS) applications highlights the significance of advancements in this field. The introduction of YOLOv8, the latest iteration in the YOLO algorithm series, presents a new avenue for exploring the potential of this detection algorithm within the ITS domain. The algorithm has not been previously tested in applications such as vehicle detection, which highlights a gap in the existing literature. This presents an opportunity to explore its capabilities and contributions in traffic monitoring and vehicle detection. This study aims to address this gap by employing YOLOv8 for vehicle detection within the broader context of ITS applications. Distinguishing itself from its predecessors, YOLOv8 features a decoupled head structure and employs a C2f module instead of C3. Extensive testing was performed using datasets acquired through aerial monitoring with a drone. Special emphasis was placed on ensuring a diverse array of target objects during dataset creation, a detail frequently neglected in comparable studies. The algorithm's training not only facilitated an evaluation of its ability to generalize and process data proficiently but also provided initial insights into its potential for real-time applications. The model underwent a comprehensive series of performance tests, revealing both strengths and weaknesses and outlining its capabilities and limitations. In a comparative analysis, the study systematically compared the performance metrics of YOLOv8 with those of YOLOv5, a widely adopted model in ITS research. Precision assessments unveiled a significant disparity, with YOLOv8 exhibiting an 18% increase in precision compared to YOLOv5. Further investigation into the inference times of both algorithms highlighted the superior processing speed performance of YOLOv8. The study's findings shed light on the limitations of the detection process, attributing misclassifications to factors such as variations in vehicle shapes, lighting conditions, and relative sizes.

Cluster Optimization in VANET Using MFO Algorithm and K-Means Clustering: A Bibliometric Review

In bibliometric review reveals that VANET clustering optimization is a significant area of research, with several studies focusing on different aspects of the problem. The use of optimization algorithm, such as the MFO algorithm and K-Means clustering, has shown potential in improving network performance. The trend of VANETs employing Intelligent Transportation System (ITS) applications to optimize of clusters in enhancing network performance. The proposed route-finding approach based on clustering and intelligent optimization further emphasizes the significant of optimized clustering algorithms for maximizing VANET performance.

Exploring the Application of Blockchain Technology in Logistics and Transportation System

This paper explores the application of blockchain technology in improving the management and efficiency of logistics and transportation systems. By leveraging blockchain technology, real-time recording, traceability, data sharing, security, and trustworthiness of logistics information can be achieved, thereby enhancing the efficiency and safety of logistics operations. The paper discusses the theoretical foundations of blockchain technology and logistics and transportation systems, highlighting key concepts such as decentralization, distributed ledger, consensus mechanisms, smart contracts, operations research, supply chain management, and transportation economics. Additionally, the literature review presents an overview of existing studies on logistics and transport systems and blockchain technology, illustrating various applications and benefits, such as sustainable transport frameworks, transportation and logistics management models, smart logistics systems, and supply chain traceability platforms. In conclusion, the exploration of blockchain technology's application in logistics and transportation systems offers comprehensive support for improving logistics efficiency, reliability, and safety, promising more efficient and reliable transportation services for the logistics industry in the future.

Enabling Analysis, Modeling, and Simulation for Cooperative Automated Vehicle Applications in Transportation Systems: The Pillar Diagram

Enabling Analysis, Modeling, and Simulation for Cooperative Automated Vehicle Applications in Transportation Systems: The Pillar Diagram

Leveraging renewable energy sources for sustainable traction vehicles

In pursuit of sustainable transportation, green energy sources (GES) are taking center stage, propelling traction vehicles like tramways and trolleybuses towards a zero-emission future. Solar is the most prominent source among the available renewable sources and also for the transport system applications. To utilize photovoltaic (PV) source effectively, model predictive controller-based PV maximum power tracking algorithm is used to identify the PV parameters. Equipped with the smart energy storage system (SESS), light traction vehicles rely on the efficiency and reliability of brushless DC (BLDC) motors for smooth operation. However, BLDC motors operate at high voltages, which requires them to be connected to high voltage microgrids. Bridging the gap between ESS/SESS and the high-voltage microgrid with traditional DC-DC boost converters incurs efficiency losses and component stress. This paper tackles high-voltage needs in microgrids through innovative, efficient DC-DC boost converter designs. An innovative finite-control-set based model-predictive control (FCS MPC) controller tackles clean energy harnessing from PV and grid stability in traction applications, enabling optimized power sharing within microgrid constraints.

Development of information systems in road transportation

Nowadays, we face many problems in the field of transport. The application of intelligent transport systems should improve problems related to congestion management, road safety, access to data in the transport environment, and environmental protection. The paper presents two concepts that are key to the improvement and development of the transport sector. The development and application of automatic vehicle location since the 1960s has led to an improvement in the efficiency of transport companies’ operations and the quality of their services. This technology has led to improved efficiency of the transport service, better utilisation of resources and improved quality of service. However, the most modern concept that initiates the improvement of existing solutions and encourages the development of new solutions in the field of transport is the IoT (Internet of Things) concept. In freight transport, the application of IoT technology enables cost management, reduction of CO2 emissions and avoidance of congestion through the use of alternative routes.

Design and Application of Intelligent Transportation Systems Based on Optoelectronic Information Processing and Time-optimal Algorithms

The project employs photoelectric information acquisition technology and integrates it with an intersection waiting time optimization algorithm to achieve traffic light control at urban intersections through photoelectric acquisition, data analysis, data processing, and module control. Its objective is to efficiently and effectively address the issue of traffic congestion at intersections, providing a solution for the development of intelligent urban transportation.

Gated Fusion Adaptive Graph Neural Network for Urban Road Traffic Flow Prediction

Accurate prediction of traffic flow plays an important role in maintaining traffic order and traffic safety, which is a key task in the application of intelligent transportation systems (ITS). However, the urban road network has complex dynamic spatial correlation and nonlinear temporal correlation, and achieving accurate traffic flow prediction is a highly challenging task. Traditional methods use sensors deployed on roads to construct the spatial structure of the road network and capture spatial information by graph convolution. However, they ignore that the spatial correlation between nodes is dynamically changing, and using a fixed adjacency matrix cannot reflect the real road spatial structure. To overcome these limitations, this paper proposes a new spatial-temporal deep learning model: gated fusion adaptive graph neural network (GFAGNN). GFAGNN first extracts long-term dependencies on raw data through stacking expansion causal convolution, Then the spatial features of the dynamics are learned by adaptive graph attention network and adaptive graph convolutional network respectively, Finally the fused information is passed through a lightweight channel attention to extract temporal features. The experimental results on two public data sets show that our model can effectively capture the spatiotemporal correlation in traffic flow prediction. Compared with GWNET-conv model on METR-LA dataset, the three indexes in the 60-minute task prediction improved by 2.27%,2.06% and 2.13%, respectively.

Mechanism Design Theory in Control Engineering: A Tutorial and Overview of Applications in Communication, Power Grid, Transportation, and Security Systems

Mechanism Design Theory in Control Engineering: A Tutorial and Overview of Applications in Communication, Power Grid, Transportation, and Security Systems

Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network

Vehicular ad hoc networks (VANETs) provide a robust infrastructure for intelligent transportation system (ITS) applications. VANET communication involves vehicle-to-vehicle and vehicle-to-infrastructure connections, primarily with roadside units (RSUs). Analyzing cognitive radio (CR)-VANET studies revealed two key performance issues: high energy consumption and latency. To address these challenges, we propose a novel approach: link stability and mobility prediction-based clustered CR-VANETs, known as LMCCR-VANET. LMCCR-VANET consists of four main components: CR-VANET construction, clustering model, speed-based mobility prediction, and link-based multipath routing. Initially, we establish cluster-based CR-VANETs to analyze and mitigate spectrum scarcity and power utilization problems in VANETs. Mobility prediction evaluates vehicle speed variations and predictions. Finally, employing link stability-based multipath routing (LSMR) in conjunction with the fuzzy interference model and ad hoc on-demand multipath distance vector (AOMDV) routing protocol ensures stable and efficient routing. Experimental results showcase the superiority of LMCCR-VANET. It exhibits enhanced energy efficiency, delivery rates, reduced energy consumption, end-to-end latency, and routing overhead when compared to recent works such as SCCR-VANET, CFCR-VANET, and MMCR-VANET.

Multi-Physics Three-Dimensional Component Placement and Routing Optimization Using Geometric Projection

Abstract This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), heating, ventilation, air-conditioning, and refrigeration, and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions and are presented to exhibit the capabilities of the proposed 3D multi-physics spatial packaging optimization framework.

Intrusion detection system for vehicular ad hoc network attacks based on machine learning techniques

ABSTRACT The vehicular ad-hoc network (VANET) is a new subcategory of mobile ad-hoc networks that has excellent potential application in intelligent transportation systems. The cyberattack on vehicles can lead to privacy leaks and financial losses and could even become a national public safety issue. In this paper, a multi-level Intrusion Detection System based on Machine Learning (ML) algorithms is developed in order to detect multiple types of attacks on VANET. The proposed model combines two feature selection techniques: random forest (RF) and fast correlation-based filter (FCBF) and four tree-based algorithms: extreme gradient boosting decision tree, RF, decision tree, and ExtraTrees, for classification performance. Moreover, the model is evaluated using CICIDS2017 dataset and Python ML libraries in Jupyter Notebook such as scikit-learn and Pandas. Experiment results show that the proposed model using the stacking method achieves 99.86% attack detection accuracy, 99.85% precision with hypo-parameter (HPO), and 99.83% attack detection accuracy without using HPO. The experimental findings on the vehicular network demonstrate the feasibility of the proposed solution and the ability to implement it in real-time environments.

Ensemble learning based approach for traffic incident detection and multi-category classification

Traffic incident is one of the important causes of road congestion. Traffic incident detection plays a crucial role in the safety application of intelligent transportation systems, which provides timely information for traffic management departments and reducing losses. Despite many researches on incident detection approach, the identification of different incident categories is not enough. In addition, traffic incident detection is still a challenging task due to the problem of data imbalance and feature selection. In this study, we propose a two-stage traffic incident detection framework based on ensemble learning. In the first stage, a binary classification algorithm based on XGBoost (eXtreme Gradient Boosting) is established to detect whether there is a traffic incident, and 24 feature variables are determined by model feature selection. In the second stage, three resampling algorithms are utilized to reconstruct and balance the dataset. Through comparative analysis, SMOTE (Synthetic Minority Over-sampling Technique)-XGBoost is the best method for incident multi-category classification with precision of 87.27%, 78.52% and 92.54%, respectively. Moreover, the baseline comparison experiments are conducted to evaluate our model performance with real-word datasets. The proposed model achieves the highest average accuracy of 93.45% in the first stage and the macro-precision of 86.11% in the second stage. The results indicate that the proposed method outperforms baselines and the two-stage framework can accurately realize the incident detection and multi-category classification.

Multibeam Cylindrical Conformal Array in the Presence of Enhanced Mutual Coupling

The limitations of conventional sensors have made array antennas increasingly crucial for gathering information and communication applications in intelligent transportation and communication systems. Compact cylindrical arrays are particularly favored for their ability to achieve azimuth angle scanning. However, the substantial mutual coupling effect between the elements on curved surfaces and its implication for these arrays remain unclear, which is a key factor to consider when such arrays are used for multibeam applications. This study investigates the effect of mutual coupling in a dual-slant-polarized cylindrical array. The results showed that mutual coupling is predominantly observed among the closely located elements, and it is essential for achieving an ultra-wide bandwidth. The study also analyzes the impact of mutual coupling on the scan impedance and radiation characteristics for multibeam applications and reveals that these arrays exhibit robust multibeam capability, hence having great potential for use in sensing and communication applications.

A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA-GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy.

Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making

In the continuously evolving context of urbanization, more people flock to cities for job opportunities and an improved quality of life, resulting in undeniable pressure on transportation networks. This leads to severe daily commuting challenges for residents. To mitigate this urban traffic pressure, most cities have adopted urban dockless bicycle sharing systems (UDBSS) as an effective measure. However, making accurate decisions regarding UDBSS demand in different city locations is crucial, as incorrect choices can worsen transportation problems, causing difficulties in finding bicycles or excessive deployments leading to disorderly accumulation. To address this decision-making challenge, it is essential to consider uncertain factors like daily weather, temperature, and workdays. To tackle this effectively, we construct an adjustable multi-granularity (MG) complex intuitionistic fuzzy (CIF) information system using complex intuitionistic fuzzy sets (CIFSs). This system objectively determines classification thresholds using an evaluation-based three-way decision (TWD) method, creating adjustable MG CIF probability rough sets (PRSs). Additionally, to recognize the irrationality of decision-makers (DMs), we propose a method that combines prospect theory (PT) with regret theory (RT), providing a more comprehensive understanding of the influence of DMs' psychological factors on decision outcomes. Building upon these foundations, we present static rebalancing strategies for UDBSS based on MG PRSs and prospect-regret theory (P-RT) within the CIF information system. Finally, using UDBSS data collected from various sensors, we conduct experimental analysis to verify its feasibility and stability. In summary, this approach considers residents’ daily usage preferences, including bicycles utilization and return, with the aim of minimizing unmet resident demands and predicting usage patterns for the next day. It effectively addresses the issue of UDBSS distribution inefficiencies and holds a significant advantage in prediction, making it suitable for broader applications in transportation systems and contributing to the establishment of more advanced modern intelligent transportation systems (MITSs) in the future.