Intelligent Transportation Systems Applications Research Articles

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.

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system.

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

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.

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.

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.

Multi-Vehicle Multi-Camera Tracking With Graph-Based Tracklet Features

Multi-target multi-camera tracking (MTMCT) is an important application in intelligent transportation systems (ITS). The conventional works follow the tracking-by-detection scheme and use the information of the object image separately while matching the object from different cameras. As a result, the association information from the object image is lost. To utilize this information, we propose an efficient MTMCT application that builds features in the form of a graph and customizes graph similarity to match the vehicle objects from different cameras. We present algorithms for both the online scenario, where only the past images are used to match a vehicle object, and the offline scenario, where a given vehicle object is tracked with past and future images. For offline scenarios, our method achieves an IDF1-score of 0.8166 on the Cityflow dataset, which contains the actual scenes of the city from multiple street cameras. For online scenarios, our method achieves an IDF1-score of 0.75 with an FPS of 14. Our codes and datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/elituan/GraphBasedTracklet_MTMCT</uri> .

A Nonlocal Similarity Learning-Based Tensor Completion Model With Its Application in Intelligent Transportation System

A Nonlocal Similarity Learning-Based Tensor Completion Model With Its Application in Intelligent Transportation System

Lipschitz Continuity and Data Poisoning Attacks — Applications in Intelligent Transportation Systems

Lipschitz Continuity and Data Poisoning Attacks — Applications in Intelligent Transportation Systems

Privacy-Preserving Distributed Transfer Learning and its Application in Intelligent Transportation

With the rapid development of intelligent transportation systems (ITS), more and more intelligent applications for ITS have received widespread attention, such as the vehicle detection, inference of typical routes, and traffic forecasting. In these applications, deep learning is widely used as a key artificial intelligence technology. However, most ITS providers fail to collect enough labeled traffic data for model training. As a complement to deep learning, transfer learning is an effective way to solve the scarcity of labeled data, which can transfer knowledge from labeled datasets to unlabeled datasets, thus improving the accuracy of prediction and classification. Nevertheless, when the labeled dataset and the unlabeled dataset are held by different entities, it is still unrealistic for two mutually distrustful entities to cooperate in transfer learning regarding data security and privacy preservation. Although some existing works provide privacy-preserving transfer learning methods, such methods fail to apply to traffic data with high sample dimensions due to their high computational cost and round complexity. To address this problem, we design an efficient privacy-preserving distributed transfer learning protocol, which is appropriate for traffic data. Compared to existing works, our protocol addresses the privacy-preserving problem of transfer learning for traffic data with high sample dimensions. In addition, our protocol has fewer interaction rounds and can be proved in the semi-honest model. Finally, we validate the effectiveness, efficiency and security of the proposed protocol via experiments. Furthermore, we show the application of the proposed protocol in intelligent transportation systems.

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

The Internet-of-Things (IoT) constructs a vast, intricate, and perpetually evolving ecosystem exerting profound societal implications. This labyrinthine nature often culminates in errors that directly impact human lives. A significant domain where this complexity materializes is Intelligent Transportation Systems (ITS). Present tools and methodologies inadequately accommodate the complex task of testing and validation, underscoring the urgency for comprehensive review and enhancement. This study aims to present a broad analysis of existing simulators utilized for ITS simulations. It delves into the role and effectiveness of such simulation tools, highlighting their limitations and proposing research directions. This paper scrutinizes both commercial and research-oriented IoT simulators for ITS, evaluating their features and simulation environment tools. We have detailed various ITS scenarios simulated within these frameworks, intending to gauge their readiness for real-world ITS applications and to elaborate on the challenges involved in ITS infrastructure implementation. The findings suggest that despite numerous simulators aiding the evolution of solutions for IoT challenges in recent years, their utility in actual ITS implementations remain uncertain. Consequently, we explore public cloud platforms offering IoT simulation capabilities, focusing particularly on the capabilities provided by the Amazon Web Services (AWS) IoT simulation for this study. Our research outlines the pressing challenges in this field, while proposing potential solutions and flagging opportunities for further research. This study paves the way towards improving the reliability and accuracy of IoT simulators in the context of ITS, which has immense potential to enhance the quality of human life.

Analysis and simulation of vehicle following behavior with consideration of multiple time delays

With the application of intelligent transportation system, drivers can receive more traffic status information in the process of driving, but they may have multiple time delays when they are presented with different information. As a result, to further research the influence of multiple time delays on vehicle following behavior, this study comes up with a modified car following model, which takes into account multiple time delays in sensing both distance and velocity information, based on the backward looking and velocity difference model. The linear stability theory is used to explore the influence of multiple time delays on the stability of the extended model. Different nonlinear equations are derived using nonlinear theory, which include Burgers, Korteweg–de Vries, and Modified Korteweg–de Vries equations and are used to depict triangular wave, soliton wave and kink–antikink wave corresponding to stable, metastable and unstable regions respectively. In the end, numerical simulation is used to further study the improved model to depict the dynamic evolution of traffic stream. The results display that the improvement of time delay in sensing distance can induce traffic congestion, conversely, the time delay in sensing velocity offsets the negative effect of time delay in sensing distance. The theoretical results and numerical simulation results are consistent with each other.

Efficient Visual Tracking Based on Fuzzy Inference for Intelligent Transportation Systems

Remote monitoring is an important application of intelligent transportation systems (ITSs). The combination of monitoring equipment and tracking algorithms can be used to automatically track moving targets. The tracking algorithm based on the Siamese network is both accurate and efficient, and its development potential is better than that of other algorithms. Its output is a detection map that reflects the probability that any position in the search area is the center of the target’s bounding box, and the maximum value of the detection map is the center of the target’s bounding box predicted by the algorithm. Owing to partial occlusion, target deformation, out-of-view, and background clutter, local maxima in the detection map may also be the center of the target’s bounding box. A tracker’s ability to make accurate judgments is currently limited. Furthermore, previous trackers extracted only the target features in the initial frame as the matching template. Although this matching template is highly reliable, it cannot effectively combine the target features available in the subsequent frames. Therefore, in this study, fuzzy inference is introduced into the tracking process to analyze the reliability of the detection map. When this map is reliable, the target feature of the search area is transformed into a substitute template; otherwise, multiple substitute templates are selected from the template pool for parallel matching as per the set rules. The optimal result is selected from multiple detection results, based on the priority of the detection results when the initial frame is used as the matching template. Experimental results on multiple datasets show that the proposed algorithm is superior to other similar algorithms in terms of multiple assessment metrics and can improve the robustness of remote monitoring tasks in ITSs.

Performance of Public Transport Appraisal using Machine Learning

Public passenger transport holds immense significance in the overall transportation system. Forecasting the movement of public transport has emerged as a crucial problem in transport planning due to its practical implications. Recently, there has been a lot of significant attention in Intelligent Transportation Systems (ITS), introducing various advancements and innovative applications to develop conditions for public transit that are safer, more effective, and fun. To fully leverage the potential of ITS applications and deal with road situations proactively, it becomes crucial to have a reliable method for predicting traffic flow. This opens up opportunities for ITS applications to anticipate and address potential challenges in advance. Enhancing the efficient functioning of Public Transport (PT) networks is a primary objective for urban area authorities, and the proliferation of location and communication devices has led to an abundance of operational data. Applying appropriate Machine Learning (ML) methods can help identify patterns in the data to improve the Schedule Plan. This research focuses on heterogeneous information that influences the prediction value, aiming to predict the required transport demand for specific routes and the arrival time of public transport. Utilizing DBSCAN clustering with SARIMA Algorithm, real-time passenger demand forecasting is extensively promoted to enhance dynamic bus scheduling and management. Furthermore, this paper compares the accuracy of the proposed Prophet Model with traditional time series models like ARIMA and SARIMA. The aim is to provide precise and robust passenger demand predictions, enabling more effective planning and management of PT services.

IODRNN - Incremental output decomposition for a valid traffic flow prediction with GNSS data

Traffic flow prediction, a crucial application of intelligent transportation systems (ITS), has become an increasingly prevalent research topic. However, existing models that achieved high prediction accuracy on selected metrics may suffer from time delay in prediction curves which has been rarely explored. These models may produce seemingly accurate but invalid predictions by merely tracking and replicating previous true values. To address this anomaly, we propose a highly interpretable prediction mechanism, the Incremental Output Decomposition Recurrent Neural Network (IODRNN). We also introduce a new metric called Shift Divergence Difference (SDD) to assess the degree of latency of the overall sequence and evaluate the effectiveness of IODRNN in reducing the delay phenomenon. Our experimental results using real-world GNSS traffic data show that IODRNN has the smallest degree of latency and improves MAE and RMSE by 16.8% and 17.4% on average, respectively, over most contrast models. Our study presents an effective approach to evaluate prediction latency, ensuring validity and robustness in traffic prediction.

Self-supervised generative adversarial learning with conditional cyclical constraints towards missing traffic data imputation

Accurate traffic data imputation aims to fill in missing traffic values with observations as much as possible, which has long been a challenging task that affects its exploitation and application in intelligent transportation system. However, most available generative models lack extensive and effective conditional constraints during their training phase. Hence, the repair accuracy and scalability of these models are poor when dealing with complex missing cases. To fill this research gap, we propose a novel framework called self-supervised generative adversarial learning with conditional cyclical constraints (i.e., C3S2-GAL) to address the traffic data imputation problem. C3S2-GAL employs two self-supervised sparse autoencoders (i.e., S2-SAE) that share the same network structure as it's generator and discriminator to perform the fine-grained mapping and precise output of the input partial observations. With the help of multiple loss constraints, S2-SAE learns the shallow hidden relationships between observations and then generates the initial complete data. Moreover, we further enhance the repair capability of S2-SAE by calculating and integrating the pseudo-labels loss of implicit category information within the pre- and post-repair traffic data. Also, we introduce a cyclical consistency constraint to provide reliable supervision for observed and unobserved traffic values and adopt an iterative learning strategy to ensure that C3S2-GAL yields high-quality results. Finally, extensive experiments are conducted on the publicly available Guangzhou and Seattle-Loop traffic speed datasets to evaluate C3S2-GAL under different missing scenarios and missing rates. The comparison results, convergence analysis, sensitivity analysis and ablation study confirm the superiority of C3S2-GAL over existing state-of-the-art baseline models.