Traffic Management Tasks Research Articles

Analysis and Justification of Data Structure Selection for Traffic Flow Management Tasks

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting

Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. However, these tasks are usually solved separately without ensuring coherence, which is crucial for aligning downstream decisions. Previous works mainly focus on ensuring coherence with some straightforward methods, e.g., aggregation from the forecasts of fine granularity to the coarse ones, and allocation from the coarse granularity to the fine ones. These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy. In this paper, we propose a novel granularity message-passing mechanism (GMP) that leverages temporal hierarchy information to improve forecasting performance and also utilizes an adaptive reconciliation (AR) strategy to maintain coherence without performance loss. Furthermore, we introduce an optimization module to achieve task-based targets while adhering to more real-world constraints. Experiments on real-world datasets demonstrate that our framework (GMP-AR) achieves superior performances on temporal hierarchical forecasting tasks compared to state-of-the-art methods. In addition, our framework has been successfully applied to a real-world task of payment traffic management in Alipay by integrating with the task-based optimization module.

Traffic Flow Prediction on Road using Machine Learning

The Intelligent Transportation System (ITS) plays a vital role in numerous smart city applications, particularly in improving transportation and commuting processes. A primary goal of ITS is to tackle traffic-related challenges, especially the issue of traffic congestion. The prediction system for road traffic flow has significant relevance in urban transportation and area management. Many urban centers grapple with the daunting task of effective traffic management. However, the incorporation of predictive modeling that considers environmental and weather conditions, such as rainfall and thunderstorms, has proven to be remarkably effective. In response to this challenge, we have introduced a road traffic flow prediction model specifically designed to forecast traffic conditions at hourly intervals extending up to 24 hours. Although various algorithms have been applied in previous research, there is a notable absence of accessible and user-friendly platforms dedicated to road traffic flow prediction.

MODELING CITY TRAFFIC MANAGEMENT AND IDENTIFICATION OF COMPETITIVE ROUTES USING INTELLIGENT TRANSPORTATION SYSTEMS

This article presents a conceptual approach to modelling city traffic management and identifying competitive routesusing intelligent transportation systems. It argues that modelling traffic in transport streams, considering the behaviour ofeach individual vehicle, yields the most reliable results for real-time modelling of specific intersections in city trafficmanagement. The proposed methodology for developing an algorithm to detect competitive routes allows users to choosethe optimum route. The method is based on the statistical relationship between route indicators and helps to accuratelycalculate the demand for the implementation and improvement of the transportation network with subsequent changes inits operation. Redistribution of traffic flows for the identification of competitive routes in transport corridors and the impactof random variable values on route indicators are discussed. It is proven that the probability of describing the distributionof intervals between vehicles entering the network is of utmost importance for describing processes in a transport corridor,with the exponential distribution being typically used for these purposes. The conclusion is made that the micro-levelmodel for calculating vehicle movement modes should meet the requirements necessary for solving the task of modelingcity traffic management and identifying competitive routes using ITS. Such conditions are characterized by significantfluctuations in vehicle movement modes, stops at regulated intersections and in traffic jams, lane and trajectory changes,which significantly affect the algorithm for finding a competitive route in the transport corridor.

FANET management process simulation at the deployment and operation stage

The object of the study is the process of managing the air network of air communication platforms of the FANET class (Flying Ad-Hoc Network), which is a component of the ground-air communication network, and which is performed on rotary unmanned aerial vehicles (UAVs) of the mini class, at the stage of deployment and operational management. The scientific research is aimed at the managing process formalization of aerial communication platforms of the air communication network in the implementation of two classes of management tasks – the class of traffic management tasks and the class of communication tasks. The analysis of this subject area showed that the management tasks at the stage of deployment and operational management of the air subnet are a multi-parameter optimization task and require the formation of control solutions at the OSI physical, channel and network levels, open systems interaction model. Tasks related to the adaptive management of radio coverage in zones (geographic areas of the area), including the clustering of terrestrial subscribers (communication nodes), were not considered, and relate to processes at the transport and application levels. At the same time, the article shows the mathematical apparatus of the approach to the compensation of the deviations of the trajectory of an unmanned aerial vehicle (UAV) in the conditions of a directional obstacle, which will allow the formation of control solutions for adaptive control, directional patterns at the output of the transmission path. Such compensation is carried out using methods of algorithmic exchange of probes (messages) between the mobile base station and communication platforms with a certain periodicity – solutions at the channel and network levels, as well as the use of Multi User MIMO technologies. This technology allows for information exchange with several client devices at the same time, and not sequentially, sending probes to several spacecraft on one channel, using several transmitting and receiving antennas, and the calculation of channel coefficients allows to estimate the azimuthal angle of deviation and the angle of elevation.

Improved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV intelligent traffic

As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. Drones are aerial operations and shoot small targets. So the detection accuracy of drones is less. To address the problem of low accuracy of Unmanned Aerial Vehicles (UAVs) in detecting small targets, we designed a more suitable algorithm for UAV detection and called GBS-YOLOv5. It was an improvement on the original YOLOv5 model. Firstly, in the default model, there was a problem of serious loss of small target information and insufficient utilization of shallow feature information as the depth of the feature extraction network deepened. We designed the efficient spatio-temporal interaction module to replace the residual network structure in the original network. The role of this module was to increase the network depth for feature extraction. Then, we added the spatial pyramid convolution module on top of YOLOv5. Its function was to mine small target information and act as a detection head for small size targets. Finally, to better preserve the detailed information of small targets in the shallow features, we proposed the shallow bottleneck. And the introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3% and the mAP@0.5:0.95 was 20.0%. Compared to the default YOLOv5 algorithm was boosted by 4.0% and 3.5%, respectively.

TRAPROB: An Innovative Traffic Police Robot with Arduino Intelligence

In an era of ever-increasing urbanization and traffic congestion, the development of innovative solutions for traffic management is imperative. This research paper presents the design, fabrication, and implementation of a humanoid traffic police robot, lovingly named “TRAPROB”, which is equipped with a variety of functionalities to enhance traffic control and safety. TRAPROB's hardware foundation includes an Arduino Mega microcontroller, multiple servo motors for live gestures, and an ESP32 cam for real-time monitoring. This research involves both the physical construction of the robot's iron sheet body and the complex programming required to organize its movements and traffic management functions. Robot activities range from cordial interactions like waving and shaking hands to critical traffic management tasks. TRAPROB specializes in directing the flow of vehicles, acknowledging right of way, signaling, and accurately enforcing traffic rules. It can manage intersections efficiently, ensuring a smooth and safe traffic environment. Its unique ability to seamlessly communicate with oncoming vehicles, stop traffic from different directions and give synchronized commands promotes a holistic approach to traffic control. This research paper emphasizes the practicality and simplicity of building a humanoid traffic police robot from scratch. TRAPROB represents an innovative step forward in the field of autonomous traffic management, offering a customizable, cost-effective, and adaptable solution to the challenges posed by modern urban traffic. As traffic congestion continues to increase, TRAPROB's ability to reduce congestion and increase road safety is becoming increasingly important. This paper explores the design, construction, and functionality of TRAPROB, providing information about its real-world applicability and the prospects of this DIY traffic management solution.

ИСПОЛЬЗОВАНИЕ МОДУЛЯЦИИ LORA В СРЕДСТВАХ РАДИОСВЯЗИ, НАВИГАЦИИ И НАБЛЮДЕНИЯ ДЛЯ РЕШЕНИЯ ЗАДАЧ УПРАВЛЕНИЯ ВОЗДУШНЫМ ДВИЖЕНИЕМ

The paper considers the use of LoRa technology in the data transfer system for performing aircraft position-fixing by means of the network of multilateration ground stations. The urgency of the problem is that the methods used for transferring data do not meet the requirements for safety and security of the transmitted information so such systems are not protected against unauthorized access. The information transmitted is strategically important for performing air traffic management tasks on the territory of the Russian Federation. Unauthorized access can result in intercepting and spoofing the coordinate information of aircraft location. This is possible only in exceptional circumstances which lead to reduction in flight safety and transition of flight incidents to flight accidents. The security of transmitted information can be improved by applying noise-like signals which ensure better security indexes of transmitted signals and possess higher jamming resistance. A new LoRa technology of transferring data is widespread nowadays. The technology is based on the linear frequency modulation which has been used in radar systems for several decades. The paper presents the algorithm of operating the multilateration data transfer system and its hardware-in-the-loop simulation implemented as a virtual device.

An Uncertainty Weighted Non-Cooperative Target Pose Estimation Algorithm, Based on Intersecting Vectors

Aiming at the relative pose estimation of non-cooperative targets in space traffic management tasks, a two-step pose estimation method, based on spatially intersecting straight lines, is proposed, which mainly includes three aspects: (1) Use binocular vision to reconstruct the straight space line, and based on the direction vector of the straight line and the intersection of the straight line, solve the pose of the measured target in the measurement coordinate system, and obtain the initial value of the pose estimation. (2) Analyze the uncertainty of the spatial straight-line imaging, construct the uncertainty description matrix of the line, and filter the line features, accordingly. (3) Analyze the problems existing in the current linear distance measurement, construct the spatial linear back-projection error in the parametric coordinate space, and use the linear imaging uncertainty to weigh the projection error term to establish the optimization objective function of the pose estimation. Finally, the nonlinear optimization algorithm is used to iteratively solve the above optimization problem, to obtain high-precision pose estimation results. The experimental results show that the two-step pose estimation algorithm, proposed in this paper, can effectively achieve a high-precision and robust pose estimation for non-cooperative spatial targets. When the measurement distance is 10 m, the position accuracy can reach 10 mm, and the attitude measurement accuracy can reach 1°, which meets the pose estimation accuracy requirements of space traffic management.

Discriminative-region attention and orthogonal-view generation model for vehicle re-identification

Vehicle re-identification (Re-ID) is urgently demanded to alleviate the pressure caused by the increasingly onerous task of urban traffic management. Multiple challenges hamper the applications of vision-based vehicle Re-ID methods: (1) The appearances of different vehicles of the same brand/model are often similar; However, (2) the appearances of the same vehicle differ significantly from different viewpoints. Previous methods mainly use manually annotated multi-attribute datasets to assist the network in getting detailed cues and in inferencing multi-view to improve the vehicle Re-ID performance. However, finely labeled vehicle datasets are usually unattainable in real application scenarios. Hence, we propose a Discriminative-Region Attention and Orthogonal-View Generation (DRA-OVG) model, which only requires identity (ID) labels to conquer the multiple challenges of vehicle Re-ID. The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches. Finally, the distance between vehicle appearances is presented by the discriminative region features and multi-view features together. Therefore, the significance of pairwise distance measure between vehicles is enhanced in a complete feature space. Extensive experiments substantiate the effectiveness of each proposed ingredient, and experimental results indicate that our approach achieves remarkable improvements over the state-of-the-art vehicle Re-ID methods on VehicleID and VeRi-776 datasets.

Hybrid Deep Learning Signature based Correlation Filter for Vehicle Tracking in Presence of Clutters and Occlusion

This vehicle tracking is an important task of smart traffic management. Tracking is very challenging in presence of occlusions, clutters, variation in real world lighting, scene conditions and camera vantage. Joint distribution of vehicle movement, clutter and occlusions introduces larger errors in particle tracking based approaches. This work proposes a hybrid tracker by adapting kernel and particle-based filter with aggregation signature and fusing the results of both to get the accurate estimation of target vehicle in video frames. Aggregation signature of object to be tracked is constructed using a probabilistic distribution function of lighting variation, clutters and occlusions with deep learning model in frequency domain. The work also proposed a fuzzy adaptive background modeling and subtraction algorithm to remove the backgrounds and clutters affecting the tracking performance. This hybrid tracker improves the tracking accuracy even in presence of larger disturbances in the environment. The proposed solution is able to track the objects with 3% higher precision compared to existing works even in presence of clutters.

Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.

Space governance in the new space era

This paper presents a broad, holistic overview of our current space governance system, from the best practices and self-regulation developed by spacecraft operators and relevant industry associations, to the four core treaties of the United Nations, the consensus guidelines of the IADC and UN, the majority approved international standards of ISO and CCSDS, and finally national policy and regulatory governance. Descriptions and examples are provided of the relevant documents issued by each of these categories. The “virtuous cycle” nature of this overall inter-connected governance system is discussed.A relative comparison is provided for all the primary attributes codified in each of the above space governance documents, as pertaining to spacecraft safety of flight, space traffic coordination and management. The recent AIAA Space Traffic Management (STM) Task 3, capturing of global best practices relevant to safety of flight, is described and summarized. Within this context, the evolving United States regulatory framework is discussed.

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

In VANETs (Vehicular Ad-hoc Networks), the num-ber of vehicles increased continuously, leading to significant traffic problems like traffic congestion, a feasible path, and associated events like accidents. Though, the Intelligent Transportation System (ITS) providing excellent services, such as safety appli-cations and emergency warnings. However, ITS has limitations regarding traffic management tasks, scalability, and flexibility because of the enormous number of vehicles. Therefore, extending the traditional VANET architecture is indeed a must. Thus, in the recent period, the design of the SD-VANETs (Software-Defined Networking defined VANETs) has gained significant interest and made VANET more intelligent. The SD-VANET architecture can handle the aforesaid VANET challenges. The centralized (logically) SDN architecture is programmable and also has global information about the VANET architecture. Therefore, it can effortlessly handle scalability, traffic management, and traffic congestion issues. The traffic congestion problem leads to longer trip times, decreases the vehicles’ speed, and prolong average end-to-end delay. Though, somewhere, some routes in the network are available with capacity, which can minimize the congestion problem and its characteristics. Therefore, we proposed heuristic algorithms called Congestion-Free Path (CFP) and Optimize CFP (OCFP), in SD-VANET architecture. The proposed algorithms address the traffic congestion issue and also provide a feasible path (less end-to-end delay) for a vehicle in VANET. We used the NS-3 simulator to evaluate the performance of the proposed algorithms, and for generating a real scenario of VANET traffic; we use the SUMO module. The results show that the proposed algorithms decrease road traffic congestion drastically compared to exiting approaches.

A MapReduce Approach for Traffic Matrix Estimation in SDN

A traffic matrix (TM) is a source of critical traffic throughput information for traffic engineering activities and network management tasks such as traffic prediction, capacity planning, network provisioning, and anomaly detection. However, estimating TM poses several challenges for network engineers. One of the challenges is that traffic data statistics are constantly changing, and their aggregation for real-time monitoring becomes a difficult task. This paper presents a near real-time TM estimation approach for OpenFlow (OF) networks. It makes use of Big Data techniques based on MapReduce operations to tackle the aggregation problem. The proposed method uses traffic data statistics collected from OF switches through an SDN controller as input and aggregates these data in a Big Data streaming processing environment. This paper explores the benefits of the distributed MapReduce computing model to provide an estimate of the TM for all origin-destination (OD) pairs of hosts in the network in two ways: 1) the accumulated throughput and 2) the throughput between two sequential TM estimates. This procedure enables network engineers to monitor the behavior and evolution of the throughput on each OD pair in the network and on each link in the path between each OD pair. The generated TM is persisted in a NoSQL database and can be made available for a variety of network traffic monitoring applications. The results of the simulations show the potential of the proposed MapReduce approach for TM estimation.

AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT

Abstract. Automatic vehicle recognition has an important role for many applications such as supervision, traffic management and rescue tasks. The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images. In this paper, an automatic vehicle recognition algorithm is proposed based on very high spatial resolution aerial images. In the first step of the proposed method, by generating the image pyramid, the candidate regions of the vehicles are recognized. Then, performing reverse pyramid, decision level fusion of the vehicle candidates and the land use/cover classification results of the original image resolution are performed in order to modify recognized vehicle regions. For evaluating the performance of the proposed method in this study, Ultracam aerial imagery with spatial resolution of 11 cm and 3 spectral bands have been used. Comparing the obtained vehicle recognition results from the proposed decision fusion algorithm with some manually selected vehicle regions confirm the accuracy of about %80. Moreover, the %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the obtained land use/cover classification map from decision fusion algorithm.

A new perspective on traffic control management using triangular interval type-2 fuzzy sets and interval neutrosophic sets

Controlling traffic flow on roads is an important traffic management task necessary to ensure a peaceful and safe environment for people. The number of cars on roads at any given time is always unknown. Type-2 fuzzy sets and neutrosophic sets play a vital role in dealing efficiently with such uncertainty. In this paper, a triangular interval type-2 Schweizer and Sklar weighted arithmetic (TIT2SSWA) operator and a triangular interval type-2 Schweizer and Sklar weighted geometric (TIT2SSWG) operator based on Schweizer and Sklar triangular norms have been studied, and the validity of these operators has been checked using a numerical example and extended to an interval neutrosophic environment by proposing interval neutrosophic Schweizer and Sklar weighted arithmetic (INSSWA) and interval neutrosophic Schweizer and Sklar weighted geometric (INSSWG) operators. Furthermore, their properties have been examined; some of the more important properties are examined in detail. Moreover, we proposed an improved score function for interval neutrosophic numbers (INNs) to control traffic flow that has been analyzed by identifying the junction that has more vehicles. This improved score function uses score values of triangular interval type-2 fuzzy numbers (TIT2FNs) and interval neutrosophic numbers.

How much is too much on monitoring tasks? Visual scan patterns of single air traffic controller performing multiple remote tower operations

The innovative concept of multiple remote tower operation (MRTO) is where a single air traffic controller (ATCO) provides air traffic services to two or more different airports from a geographically separated virtual Tower. Effective visual scanning by the air traffic controller is the main safety concern for human-computer interaction, as the aim of MRTO is a single controller performing air traffic management tasks originally carried out by up to four ATCOs, comprehensively supported by innovative technology. Thirty-two scenarios were recorded and analyzed using an eye tracking device to investigate the above safety concern and the effectiveness of multiple remote tower operations. The results demonstrated that ATCOs' visual scan patterns showed significant task related variation while performing different tasks and interacting with various interfaces on the controller's working position (CWP). ATCOs were supported by new display systems equipped with pan tilt zoom (PTZ) cameras allowing enhanced visual checking of airport surfaces and aircraft positions. Therefore, one ATCO could monitor and provide services for two airports simultaneously. The factors influencing visual attention include how the information is presented, the complexity of that information, and the characteristics of the operating environment. ATCO's attention distribution among display systems is the key human-computer interaction issue in single ATCO performing multiple monitoring tasks.

Methods for traffic management efficiency improvement in cities

Abstract A methodology for traffic management in cities provides for extensive use of computer technologies. Modern international experience shows that traffic management in the urban street and road network, first of all, requires a city-wide management system (intelligent transportation system, ITS). Development of a social process model is a complicated task that can be solved under rather severe restrictions. Therefore, most traffic management tasks are not formalized but solved empirically. Two basic approaches to development of network mathematical models of traffic flows, based on a set of analytical models and on microlevel simulation models, are considered by the authors. Capabilities of various software tools that allow performing modeling are reviewed. As a result, a method for forecasting network control actions affecting traffic flows, based on a piecewise-constant approximation of a traffic flow intensity function of time, is suggested. An example of forecasting control actions (cycle shifts) to ensure coordinated control on highways is given. The suggested approach to modeling of traffic flows in cities is rather simple and efficient. Therefore, it can be of practical interest and can be used when forecasting network control actions in intelligent transportation systems, including in real time and for congested sections of the street-and-road network.

Measuring Situation Awareness of Student Air Traffic Controllers with Online Probe Queries: Are We Asking the Right Questions?

ABSTRACTSituation awareness (SA) is a major factor determining operator performance in the current and future airspace systems, so it is not surprising that a considerable body of research on measuring operator SA has been amassed. A commonly used SA measurement method is a probe technique in which the operator is queried about the current state of his/her task environment, either offline (while a computer simulation is paused) or online (while a computer simulation is running.) Although sufficient evidence for the reliability and validity of both techniques is available, one factor that has not been systematically investigated is the efficacy of the questions themselves. We evaluated the types of online probe questions used for predicting the performance of student air traffic controllers in a simulated air traffic management task. These data were obtained from 84 students who completed a 16-week radar internship course in which their situation awareness and performance were assessed at the midpoint and end of the course. We subjected probe question latencies to a principle component analysis (PCA) to explore the variation between questions and grouped them based on the resulting components. The SA question components that emerged from PCA were then used to model the relations between SA factors and performance. The resulting model provides a framework for understanding factors involved in SA, a way to evaluate the validity of individual probe questions as a measure of SA, and a method for determining the efficacy of SA questions in predicting operator performance.