Traffic Command Research Articles

Secured intelligent transportation with privacy retention through blockchain framework

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture.

Design of Digital Image Processing System Based on Machine Vision

Digital image processing technology is a technology that has developed rapidly in recent years[1]. A series of images are processed by computers to improve the visual effect of the image in order to achieve the desired presentation effect. Digital image processing technology has been continuously developed with the innovation of computers, and its application range has expanded from the original image field to traffic command, signal processing, medical treatment and other fields closely related to people's lives. With the support of many algorithms, the speed of digital image processing technology is getting faster and faster, which brings a lot of convenience to people's lives, and which becomes an indispensable technology in life. With the help of MATLAB's GUI user interface, image processing toolbox and MATLAB's comprehensive mathematical function library, this article designs an image processing operating platform that realizes some image processing functions after repeated trials.

Research on Railway Dispatcher Fatigue Detection Method Based on Deep Learning with Multi-Feature Fusion

Traffic command and scheduling are the core monitoring aspects of railway transportation. Detecting the fatigued state of dispatchers is, therefore, of great significance to ensure the safety of railway operations. In this paper, we present a multi-feature fatigue detection method based on key points of the human face and body posture. Considering unfavorable factors such as facial occlusion and angle changes that have limited single-feature fatigue state detection methods, we developed our model based on the fusion of body postures and facial features for better accuracy. Using facial key points and eye features, we calculate the percentage of eye closure that accounts for more than 80% of the time duration, as well as blinking and yawning frequency, and we analyze fatigue behaviors, such as yawning, a bowed head (that could indicate sleep state), and lying down on a table, using a behavior recognition algorithm. We fuse five facial features and behavioral postures to comprehensively determine the fatigue state of dispatchers. The results show that on the 300 W dataset, as well as a hand-crafted dataset, the inference time of the improved facial key point detection algorithm based on the retina–face model was 100 ms and that the normalized average error (NME) was 3.58. On our own dataset, the classification accuracy based the an Bi-LSTM-SVM adaptive enhancement algorithm model reached 97%. Video data of volunteers who carried out scheduling operations in the simulation laboratory were used for our experiments, and our multi-feature fusion fatigue detection algorithm showed an accuracy rate of 96.30% and a recall rate of 96.30% in fatigue classification, both of which were higher than those of existing single-feature detection methods. Our multi-feature fatigue detection method offers a potential solution for fatigue level classification in vital areas of the industry, such as in railway transportation.

Lightweight and Scale Adaptive Efficient backbone Network for Recognition

Vehicle refinement recognition related technology research is widely used in the field of mine monitoring and management systems, road traffic command and control, etc. As researchers develop and implement the target recognition technology system based on deep learning algorithms, designing a target recognition algorithm with excellent performance is a research priority within the field of vehicle monitoring. In this paper, we propose an Efficient Net algorithm based recognition method for vehicle front-end and vehicle rear-end recognition to address the shortcomings of the current methods used for vehicle front-end and vehicle rear-end recognition, and verify the reliability of the algorithm using experiments. Algorithm systematically investigates model scaling, the backbone network makes extensive use of the MBConv structure to extract the feature maps, which cuts short the time required for model training, and the structure introduces the SE module to perform global averaging pooling operations in the channel dimension direction to enhance model performance, so that the network has the dual advantages of network model size and recognition accuracy at the same time. Based on the above findings, we improve the inverse residual module of the backbone feature extraction network EfficientNet by introducing the coordinate attention mechanism (CA) to average the spatial feature information in X-axis and Y-axis dimensions respectively, with the feature layer size and number of channels unchanged, and change the residual edge to shorten the input and output of high-dimensional channels to improve the accuracy of model feature extraction. Meanwhile, this paper introduces a depth-separable convolutional neural network and agent-normalized activation in the mobile flip-flop convolutional module to offset the two different dimensions of X-axis and Y-axis between each convolutional layer but the two main sources of non-normalization, so as to achieve the improvement of the target detection rate and accuracy.

Turning Maneuver Prediction of Connected Vehicles at Signalized Intersections: A Dictionary Learning-Based Approach

Vehicle-to-Infrastructure (V2I) communication has provided a solution for the improvement of the traffic efficiency of smart city intersections. For example, turning maneuvers prediction at signalized intersections in a connected environment helps traffic command centers time traffic lights and dynamically predict traffic flow. However, the modeling methods used in existing research on this topic have some limitations, such as poor scalability and interpretability of machine learning. Thus, this study proposes a dictionary learning-based approach to predict turning maneuvers before the intersection. The proposed dictionary model estimates the LogDet divergence-based sparse inverse covariance matrix (LDbSICM) of driving behavior samples. The graphical lasso method is used to estimate the sparse inverse covariance matrix of the driving samples to construct a dictionary library of the maneuver behavior. The LogDet divergence is used to calculate the difference between each inverse covariance matrix. A driving simulator is utilized to collect experimental data consisting of turning left (TL), turning right (TR), and going straight (GS) behaviors to establish and evaluate the proposed model. The experimental results demonstrate that the proposed dictionary learning-based turning maneuver prediction model achieves 100% prediction accuracy for TL and GS and 97.2% for TR. The proposed model has substantial advantages over existing methods. The model can predict TL, TR, and GS in a connected environment 270, 280, and 290 m, respectively, before the intersection.

Deviation Point Curriculum Learning for Trajectory Outlier Detection in Cooperative Intelligent Transport Systems

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F-measure</i> by 5.06% and 2.4% while compared with generic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Node2Vec</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Struct2Vec</i> methods.

Guest Editorial Introduction to the Special Issue on Data Science for Intelligent Transportation Systems

Intelligent transportation system (ITS) is a key enabler for future road traffic management systems. The core components of ITS include vehicles, roadside units, and traffic command centers. They generate a large amount of data flow that is made up of both mobility and service-related data. Therefore, some data science methods to handle the transportation data are very necessary for ITS. Although some attempts have been done to explore data science methods for ITS, there exist various scientific and engineering challenges including software and hardware development, computational complexity, data multi-source heterogeneity, and privacy protection. Consequently, to fully explore the benefits of ITS applications like connected and autonomous vehicles, traffic control and prediction, road safety, and accident prediction, advanced data science methodologies and applications are in great need.

A Privacy-Preserving-Based Secure Framework Using Blockchain-Enabled Deep-Learning in Cooperative Intelligent Transport System

Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs–RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems.

Skeleton-based traffic command recognition at road intersections for intelligent vehicles

Understanding traffic officer commands is a fundamental perception task for intelligent vehicles in driver assistance and autonomous driving. Previous studies have emphasized explicit traffic command gesture recognition but have not considered situations where the traffic officer is controlling the subjects in other directions, which would also influence decision-making of the ego vehicle. To fill in the gap, this article aims to research visual skeleton-based recognition of traffic commands occurring at road intersections, where both command directions and gestures should be determined. Specifically, a two-stage recognition framework for four cross-shaped directions and eight command gestures is proposed. Two kinds of handcrafted features, including upper-body geometric features and keypoint co-occurrence features, are established with estimated 2D human keypoint coordinates and heatmaps and further combined into a deep learning network. The first stage handles human body orientation classification, while the second stage addresses command gesture recognition with extra usage of the output from the first stage. Combining the recognized body orientation and command gesture, the type of traffic command can ultimately be inferred. For training and validation, a dataset termed the Chinese Traffic Command at Intersections (CTCX) is built. The proposed method gains an outperforming edit accuracy of 89.67% on the CTCX test set, demonstrating its effectiveness. This work provides a foundation in this area and is expected to inspire more research on traffic command recognition with directions in the near future.

Simple But Effective: Upper-Body Geometric Features for Traffic Command Gesture Recognition

Recognizing traffic command gestures with high accuracy and quick response at a low computational cost is a requisite for driver assistance or autonomous driving. However, it has been understudied for a long time. Existing research takes advantage of increasing development in human action recognition but pays little attention to onboard conditions. In this article, we propose a simple but effective recognition model based on human upper-body geometric features and a long short-term memory (LSTM) network. The handcrafted geometric features can easily be calculated with estimated 2-D human keypoints at a low computational cost but are discriminative and sufficient in classification. Offline and online inferences are implemented to comprehensively evaluate the proposed model. For the sake of robustness required in the automotive domain, dual voting is designed to filter the output in online inference. On the recently published Chinese traffic police gesture (CTPG) dataset, the presented approach is the best with a remarkable improvement of approximately 8% compared to previous LSTM-based methods with handcrafted spatial features and is competitive with advanced GCN-based deep learning methods. The tradeoff pattern is explored to demonstrate how accuracy and response time alter with different training and inference strategies so that a balanced setup can be manually chosen under various application scenarios. Field tests are also carried out with an experimental vehicle, and the results uncover the present gap between research and practical application to some extent, moving a step closer to real-life traffic command gesture recognition.

Novel Vote Scheme for Decision-Making Feedback Based on Blockchain in Internet of Vehicles

Obtaining timely and accurate traffic information is one of the most important problems in intelligent transportation system, which will make vehicles run smoothly, avoid road congestion, save road running time and reduce vehicle energy consumption. In the current Internet of Vehicles system, the traffic management center can learn from the feedback information of all vehicles to improve the ability of decision-making and traffic command. However, the existing feedback mechanism does not respond to the spatial-temporal characteristics of data in time, due to the lack of communication capability of the current equipment. So, it cannot meet the requirements of ultra-low delay, high reliability and high security in the Internet of Vehicles. To solve this problem, this paper proposes a blockchain-based proxy vote and revocation scheme for decision feedback in Internet of Vehicles, which allows the intelligent system to ignore the unevenness and heterogeneity in the 6G technology. In addition, blockchain technology notarizes the vote data of vehicles and outsources microservices. Secondly, we use the attributes of decision-related nodes instead of their identities to enable anonymous vote. Smart contracts can automatically expand the scalability of outsourced microservices. Finally, the security proof of the proposed scheme ensures the security and consistency of outsourced microservices. The simulation results also show that our scheme greatly improves the efficiency of voting feedback.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System (C-ITS) plays a vital role in the future road traffic management system. A vital element of C-ITS comprises vehicles, road side units, and traffic command centers, which produce a massive quantity of data comprising both mobility and service-related data. For the extraction of meaningful and related details out of the generated data, data science acts as an essential part of the upcoming C-ITS applications. At the same time, prediction of short-term traffic flow is highly essential to manage the traffic accurately. Due to the rapid increase in the amount of traffic data, deep learning (DL) models are widely employed, which uses a non-parametric approach for dealing with traffic flow forecasting. This paper focuses on the design of intelligent deep learning based short-term traffic flow prediction (IDL-STFLP) model for C-ITS that assists the people in various ways, namely optimization of signal timing by traffic signal controllers, travelers being able to adapt and alter their routes, and so on. The presented IDL-STFLP model operates on two main stages namely vehicle counting and traffic flow prediction. The IDL-STFLP model employs the Fully Convolutional Redundant Counting (FCRC) based vehicle count process. In addition, deep belief network (DBN) model is applied for the prediction of short-term traffic flow. To further improve the performance of the DBN in traffic flow prediction, it will be optimized by Quantum-behaved bat algorithm (QBA) which optimizes the tunable parameters of DBN. Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flow in real-time with a maximum performance under dissimilar environmental situations.

Data Security Protection Mechanism of Video and Image Feature Modeling Based on Domestic Crypto Algorithm

Video and image monitoring is increasingly appearing in our home, travel and other aspects. Through video and image analysis and comparison of video and image monitoring data, it can provide strong analysis support capabilities for social security prevention and control, traffic command, safety production and so on. Video and image feature modeling is a necessary prerequisite for video and image analysis and comparison. Video and image feature modeling forms video and image feature data. Video and image feature data reflects the most essential information of various elements such as people, vehicles and objects. However, video and image data is easy to be damaged, changed and leaked in the process of collection, aggregation, analysis, modeling and storage, Facing data security risks. This paper proposes a set of video and image feature modeling data security protection mechanism based on domestic algorithm to realize the whole process encryption protection of data acquisition, transmission and storage.

INŽINJERIJSKE JEDINICE U VOJSCI KRALJEVINE SHS/JUGOSLAVIJE 1918–1941.

The formation of engineer units in the Royal Yugoslav Army during the 1920s and 1930s developed in two phases. The first was its engagement within the army divisions and in the second phase was the establishment of a unique engineering command, including pioneer, pontoon, and military traffic commands. Considering their different specialized activities, planned complex training demanded certain financial resources as well as an adequately qualified command staff. The lack of engineering officers and non-commissioned officers was one of the reasons why the recruits did not have regular lectures. Also, whole units were often sent out of their garrisons, especially for the purpose of strengthening the country’s borders, which posed a serious problem, which was not solved during the entire interwar period. Another problem was a small number of recruits, which is why they had to be taken over from other military units, plus the fact that they were mostly technically inexperienced and frequently even illiterate. In addition, modern equipment was scarce, all of which made good quality training impossible.

Design of Intelligent Pedestrian and Vehicle Guidance System for Zebra Crossing Based on Millimeter Wave Radar

In order to protect the life safety of the pedestrian and strengthen the intelligent management of urban traffic, this paper’s proposes is a set of low-cost and easy-to-install intelligent transportation pedestrian and vehicle guidance system. The system can be applied to the non-crossroads zebra crossing. On the premise of ensuring the safety of crossing pedestrians, it would reduce the waiting time of vehicles when there are no pedestrians or scattered pedestrians passing by, to improve the vehicle traffic rates and avoid the traffic accidents about people and vehicles snatch the right of way, so as to effectively guide pedestrians and vehicles to pass. The system uses STM32L431RCT6 microcontroller as the main control chip, uses millimeter wave radar technology to monitor the number of pedestrians in front of the zebra crossing, intelligently controls the time of pedestrian passing, and coordinate the traffic lights on both sides of the road for countdown display by Lora, and switches the traffic status of vehicles and pedestrians, for realize the function of traffic command. At the same time, the system will automatically upload the number of pedestrian to OneNET, providing access and management interface for superior applications, so that the transportation department can master the travel rules, and provides travel data for the construction of smart city. The system also can receive data returned from OneNET, and flexibly control the time of pedestrian passing at that moment.

An efficient caching policy for content retrieval in autonomous connected vehicles

Abstract Connected vehicles will enable the smart and autonomous transportation systems in the future. Cellular Vehicle-to-Everything (C-V2X) communication will provide wireless connectivity to enable large number of connected vehicle applications. Vehicles will receive traffic and infotainment contents from the city traffic command center using C-V2X communications. In this context, infrastructure Road Side Units (RSUs) will cache urgent and popular data in their memory storage, hence providing vehicles to retrieve information from a closer vicinity at a RSU. In this paper, we present a content caching policy for the connected vehicles operator to improve the efficiency of the content retrieval in terms of download rate and delay. We propose the utility functions for the RSUs and vehicles to cache a particular content at a given RSU. Moreover, Gale-Shapley stable matching algorithm is used to efficiently allocate RSU cache to the contents. We also provide rules to update the cache slots. The proposed caching scheme is compared with random caching policy and market matching based caching policies. Results show that the proposed content caching policy improves the efficiency of the content retrieval with 60 % more data transmission with reduced downloading time and better link utilization as compared to other two scheme.

Energy-efficient Delay-aware and Profit Maximization Caching enabled, Congestion Control in Stochastic Network

We are presenting a new unified structure for dynamic distributed forwarding and congestion-controlled network caching enabled. Improved use of data transfer capacity and storage resources in Stochastic networks in aspects of energy-efficient and profit-maximization. In the investigation of stochastic networks, a framework has been developed for combined implementation of caching, forwarding and traffic command called the Markov Decision Process in Stochastic Learning (MDPSL) strategy. The MDPSL structure uses a virtual plane that manages customer request prices, as well as a real plane that processes actual interest packets and data packets. It can accomplish dynamically structured transmission and caching. It can fulfill dynamically distributed forwarding and caching. Focus on MDPSL communication and queuing systems, including wireless networks with time-varying channels, mobility, and arrival of random traffic. Using this framework, estimates of the time are optimized such as throughput, utility throughput, energy, and distortion. Explicit performance-delay tradeoffs are provided to show the expense of attaining optimality. A congestion control algorithm is intended to improve client services subject to network stability when optimally coupled with forwarding and caching algorithms

Particle Filter Vehicles Tracking by Fusing Multiple Features

Real-time and accurate vehicle tracking by Cameras and Surveillance can provide strong support for the acquisition and application of important traffic parameters, which is the basis of the traffic condition evaluation and the reasonable traffic command and dispatch. To deal with difficult problems of vehicle tracking research in a complex environments, such as occlusion, sudden illumination change, similar target interference and real-time tracking, measures are taken as follows. Firstly, the existing color local entropy particle filter tracking method is improved. The symmetry of information entropy is used to overcome the tracking failure caused by large-area occlusion. Secondly, the SIFT feature tracking method is improved to enhance real-time performance and robustness. Thirdly, two tracking methods were combined according to their characteristics, aiming at effectively improving the quasi-determination and real-time performance of vehicle tracking. Fourthly, Kalman filter was used to predict the motion state of vehicles. According to the SIFT characteristics and license plate information of vehicles, the exact position of the lost target vehicles is quickly located. It has been verified by experiments that our method has effectively improved the accuracy and real-time performance of vehicle tracking in complex situations.

Fog-Assisted Cooperative Protocol for Traffic Message Transmission in Vehicular Networks

Traffic information exchange between vehicles and city-wide traffic command center will enable various traffic management applications in future smart cities. These applications require a secure and reliable communication framework that ensures real-time data exchange. In this paper, we propose a Fog-Assisted Cooperative Protocol (FACP) that efficiently transmits uplink and downlink traffic messages with the help of fog Road Side Units (RSUs). FACP divides the road into clusters and computes cluster head vehicles to facilitate transmission between vehicles and traffic command center or fog RSUs. Using a combination of IEEE 802.11p and C-V2X wireless technologies, FACP minimizes the time required by a vehicle to retrieve traffic information. Furthermore, FACP also utilizes cooperative transmissions to improve the reliability of traffic messages. Simulations results show that FACP improves the reception rate and end-to-end delay of traffic messages.

Data analytics for Cooperative Intelligent Transport Systems

Cooperative Intelligent Transport System (C-ITS) is a key enabler of future road traffic management systems. The core component of C-ITS includes vehicles, road side units and traffic command centers. They generate a large amount of traffic that is made up of both mobility and service-related data. To extract useful and relevant information out of this data, data analytics will play a crucial role in future C-ITS applications. We present a review of how data analytics can benefit C-ITS applications. We describe the various types of data generated by C-ITS applications and potential dissemination techniques using various wireless technologies. We demonstrate how meaningful results from this data could be beneficial to C-ITS. We also demonstrate the improved reliability of C-ITS applications that can be achieved with data analytics using simulation results. Finally, we discuss future possible applications of data analytics in C-ITS.