Traffic Management System Research Articles

IMPLEMENTATION OF PEDESTRIAN SAFETY SYSTEMS IN THE URBAN INFRASTRUCTURE OF KHARKIV

The article focuses on pedestrian safety issues within the urban infrastructure of Kharkiv City in Ukraine and the implementation of modern protection systems on roads. It examines approaches to organising pedestrian zones and safety islands, with particular emphasis on the RS PSS system, designed to enhance pedestrian safety on roads with high traffic intensity. Significant attention goes to the growing number of road traffic accidents (RTAs) at pedestrian crossings and main city highways. From 2018 to 2024, an analysis of accident hotspots took place based on statistical data. This analysis led to the creation of a map of RTAs, which helped identify the most dangerous areas for pedestrians and determine the priority locations for safety measures. One of the proposed solutions discussed is using bollards – protective posts that prevent vehicles from entering pedestrian areas. The article analyses the effectiveness of bollards in combination with other safety measures, such as safety islands and traffic management systems. In addition to bollards, landscape solutions that protect pedestrians and blend into the urban environment are also mentioned. The main focus is on the RS PSS system, which consists of pedestrian slabs and safety islands manufactured using specialised technology. The system has been tested in various locations throughout the city and has demonstrated a significant reduction in RTAs after its implementation. The article illustrates the process of installing the system using the example of the reconstruction of a safety island on Liudviha Svobody Avenue. The use of modern equipment and technology ensured the rapid and efficient completion of the project. According to official data, the number of RTAs in this area decreased from 21 to 4 after completing the project. In conclusion, the implementation of new safety systems has had a positive impact on reducing accidents and improving pedestrian protection. There is also a noted need for further development and advancement of such systems and the integration of advanced technologies to enhance urban infrastructure. Keywords: pedestrian safety, pedestrian safety systems, safety islands, bollard, refuge.

Survey of Recent Results in Privacy-Preserving Mechanisms for Multi-Agent Systems

Privacy-preserving communication in cooperative control is essential for effective operations of various systems where sensitive information needs to be protected. This includes systems such as smart grids, traffic management systems, autonomous vehicle networks, healthcare systems, financial networks, and social networks. Recent privacy-preserving cooperative control literature is categorized and discussed in this paper. Advantages and disadvantages of differential privacy and encryption-based privacy-preserving protocols are described. The objective of this work is to examine and analyze existing research and knowledge related to the preservation of privacy in the context of cooperative control. This paper aims to identify and present a range of approaches, techniques, and methodologies that have been proposed or employed to address privacy concerns in multi-agent systems. It seeks to explore the current challenges, limitations, and gaps in the existing literature. It also aims to consolidate the findings from various studies to provide an overview of privacy-preserving cooperative control in multi-agent systems. The goal is to assist in the development of novel privacy-preserving mechanisms for cooperative control.

Traffic Prediction as a Multidimensional Random Process in a Three-Dimensional High-Density Internet of Things Network

Relevance. The relevance of the topic considered in the article lies in the active transformation of communication networks and the formation of a three-dimensional high-density communication network, which changes the structure of data traffic, therefore, for this type of network, a traffic model is considered as a multidimensional random process. The main purpose of the study is to improve the efficiency of network traffic forecasting by developing a method, the distinctive feature of which is traffic forecasting as a multidimensional random process, taking into account the mutual dependence of individual flows produced by network nodes. Methods. The paper considers an algorithm for training an artificial neural network (ANN) based on the method of reducing the root of the mean square error RMSE, and also proposes forecasting methods using LSTM-type ANNs and adapting model parameters to changing network operating conditions. The use of LSTM-type ANN for forecasting a multivariate random process describing traffic in a three-dimensional high-density network can yield better results than forecasting individual traffic flows as independent random processes due to the consideration of mutual influences between different traffic flows.The results. Building the corresponding model, collecting statistics (obtaining a training sample), training the ANN and performing the forecast require the use of computing resources. Thus, the forecasting efficiency can be defined as a decrease in the forecasting error while maintaining the volume of resources used or a decrease in the volume of resources while maintaining the forecasting error. In the course of solving the scientific problem, criteria were identified for selecting the value of a unit interval (lag), which, together with the forecasting interval, significantly affects the final scenario.The theoretical significance The scientific novelty of the work lies in the assessment of the change in the error in forecasting the traffic of a three-dimensional high-density communication network as a multivariate random process, compared to presenting the forecast of the same traffic as a set of independent random processes.Significance (theoretical). The efficiency of traffic forecasting as a multidimensional random process in a three-dimensional high-density communication network increases with increasing dimensionality. Thus, such traffic in forecasting problems should be considered as a multidimensional random process, the dimensionality of which is equal to the number of network nodes producing traffic.Significance (practical). The results obtained in the work can be used in the future to optimize the functioning of the traffic management system.

TariqAmn's Innovative Road Safety Paradigm through Smart Technology

In response to escalating road safety challenges within Algeria, marked by rising traffic volumes and urban development, the "TariqAmn Algeria" initiative emerges as a groundbreaking approach in traffic management. This study delineates the development and deployment of this innovative system, which leverages intelligent traffic signs, LiDAR (Light Detection and Ranging, using laser light to measure distances) radar, and proactive brake control technologies, embedded within a robust traffic management framework. The system's dynamic adaptation to real-time traffic conditions and its enforcement of speed compliance are central to its design. Rigorous evaluations through SUMO (Simulation of Urban Mobility, a traffic simulation software) simulations and field tests in high-risk urban settings affirm its efficacy, demonstrating a 60% reduction in traffic incidents and a 50% reduction in speeding violations. By marrying local cultural insights with cutting-edge technology, "TariqAmn Algeria" not only enhances immediate road safety but also establishes a new paradigm in traffic management systems. The findings illuminate the significant scalability potential of the system, promising broader applications in enhancing road safety and adherence to traffic regulations across various regions, such as densely populated urban areas, high-traffic corridors, and accident-prone black spots. This initiative underscores the critical role of Intelligent Transportation Systems (ITS) in modern road safety strategies and sets a precedent for future technological advancements in the field.

Effect of Layout Discretization on the Performance of Zone Control-Based Multi-AGV Traffic Management Systems

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts.

AI-Enabled Traffic Light Control System: An Efficient Model to Manage the Traffic at Intersections using Computer Vision

Traffic congestion is a significant issue with studies indicating it costs cities billions annually and averages 54 hours of wasted time per traveler each year. This situation necessitates the implementation of efficient traffic management systems, especially at intersections. In response to this challenge, our work introduces an artificial intelligence-based system designed to analyze and predict traffic flow using machine learning algorithms and deep learning methods in conjunction with traffic cameras. The model comprises two main components: real-time data collection and predictive modeling. It employs object detection to identify and classify vehicles and adjusts traffic signal timings based on the necessary passage time and predetermined constraints. Additionally, data accumulated during operation facilitates the development of a predictive model for traffic flow over time, allowing for proactive traffic management. Evaluations are done to showcase the accuracy of the model and corresponding simulation and physical implementation further approved the applicability of our approach. Finally, this work aims to enhance urban transportation efficiently, reduce commuting stress, and improve the quality of life for city residents

Driven traffic flow prediction in smart cities using hunter-prey optimization with hybrid deep learning models

Accurate and timely flow prediction is the most significant element for intelligent traffic management systems. However, developing a robust and potential prediction method is a challenge because of the nonlinear characteristics and inherent randomness of the traffic flow in smart cities. Deep learning can analyze historical traffic data and predict future traffic patterns in traffic flow prediction. This can be done by training deep neural networks on large datasets, such as traffic speed and volume data, to learn the underlying relationships between various factors influencing traffic flow. The resulting models can then be used to predict future traffic conditions, helping optimize traffic management, reduce congestion, and improve safety. This study introduces a Hunter Prey Optimization with Hybrid Deep Learning-Driven Traffic Flow in smart cities Prediction (HPOHDL-TFPM). The HPOHDL-TFPM approach's primary goal is to accurately and rapidly forecast traffic flow. The HPOHDL-TFPM technique uses Z-score normalization to normalize the traffic data to achieve this. In addition, the CBLSTM-AE model, which combines convolutional bidirectional long short-term memory and autoencoder, is utilized in the prediction of traffic flow in smart cities. Moreover, the HPO technique is applied as a hyperparameter optimizer to select the hyperparameter values properly. The experimental validation of the HPOHDL-TFPM approach is tested in several contexts. Numerous comparative studies demonstrated the improved performance of the HPOHDL-TFPM approach over other existing methods.

IoT Frameworks: Comparing Efficiency and Scalability in Smart City Applications

As Smart City apps change quickly, IoT platforms are very important for making it possible to install linked devices and services quickly and on a large scale. The main goal of this study is to compare how well and how easily different IoT systems can be expanded to work in Smart City settings. Being efficient in IoT systems means that they can make the best use of resources, reduce delay, use less energy, and keep data transfer stable. Concerning scalability, on the other hand, we look at how well these frameworks can grow to support more devices and people without affecting security or speed. This study looks at a number of well-known IoT systems, each with its own layout and set of rules made to solve specific Smart City problems. Frameworks like MQTT (Message Queuing Telemetry Transport) focus on lightweight communication and publish-subscribe messaging models. This makes them perfect for situations where low bandwidth and fast data sharing between many devices in cities are needed. CoAP (Constrained Application Protocol) is unique because of its RESTful architecture, which allows for scalability thanks to its stateless design and support for resource discovery. This makes it ideal for use in situations where device interactions are dynamic and spread out, like in traffic management systems or environmental monitoring. The study also looks into systems such as LwM2M (Lightweight Machine-to-Machine), which is known for its effective device control and support for devices that don't have a lot of memory or processing power. Its protocol design makes sure that data sharing and device control work reliably, which is important for keeping service going in large-scale setups like those found in Smart City infrastructures. The study also looks at how these frameworks compare in terms of performance measures like speed, delay, and flexibility when networks are changed and workloads are distributed in ways that are common in Smart City settings.

Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.

Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments

Air Traffic Controllers’ (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems to adapt to varying task demands. Most methodologies have computed task complexity based on basic parameters such as air-traffic density; however, literature research has shown that it also depends on many other factors. In this paper, we present a study in which we explored the possibility of predicting task complexity and performance through mental workload measurements of participants performing an ATM task in an air-traffic control simulator. Our findings suggest that mental workload measurements better predict poor performance and high task complexity peaks than other established factors. This underscores their potential for research into how different ATM factors affect task complexity. Understanding the role and the weight of these factors in the overall task complexity confronted by ATCos constitutes one of the biggest challenges currently faced by the ATM sphere and would significantly contribute to the safety of our sky.

Vehicle-Pedestrian near miss analysis at signalized mid-block crossings

IntroductionThis study aims to identify the factors related to pedestrian and roadway characteristics that affect vehicle–pedestrian Post Encroachment Time (PET) and Relative Time to Collision (RTTC) under traffic control systems at mid-block pedestrian crossings. MethodologyA total of 112 h of video data were collected using multiple cameras from Pedestrian Hybrid Beacon (PHB) and Rectangular Rapid Flashing Beacon (RRFB) sites. To extract vehicle and pedestrian trajectories and construct an accurate dataset, where each observation corresponds to a specific timeframe, with a recorded speeds of both vehicles and pedestrians, a self-developed cutting-edge Computer Vision (CV) technology was deployed. A bivariate regression approach is employed to capture the relationship between near misses and various factors. Results and ConclusionsThe findings reveal that both pedestrian and roadway characteristics significantly influence PET and RTTC. Pedestrian characteristics, such as gender, clothing color, distraction, waiting time, and crossing speed, significantly affect both PET and RTTC. The presence of children as pedestrians, eye contact with drivers, and pedestrian signal compliance rate has a significant influence on PET. Among roadway characteristics, the presence of a median, hourly traffic flow, and land use diversity of the crossing area were found to be significant determinants of both PET and RTTC. The results indicate that there is no difference in the influence of RRFB and PHB on PET values, but there is a significant difference in the influence of RRFB and PHB on RTTC values. PHB increases RTTC relative to RRFB. Finally, this study enriches existing literature by incorporating unique factors that impact pedestrian safety. Practical ApplicationsThe findings underscore the importance of data-driven approach to pedestrian safety, encouraging transportation agencies to implement targeted and effective safety strategies. In the future, the integration of artificial intelligence (AI) in traffic management and safety systems could greatly benefit from incorporating these findings.

An intelligent and resolute Traffic Management System using GRCNet-StMO model for smart vehicular networks

An intelligent and resolute Traffic Management System using GRCNet-StMO model for smart vehicular networks

Egyptian car plate recognition based on YOLOv8, Easy-OCR, and CNN

This research presents an innovative approach to Egyptian car plate recognition using YOLOv8 and optical character recognition (OCR) technologies. Leveraging the powerful object detection capabilities of YOLOv8, the system efficiently detects car plates within images, videos, or real-time. Subsequently, OCR algorithms are applied to extract alphanumeric characters from the identified plates, facilitating accurate license plate recognition. The integration of YOLOv8 and OCR enhances the system's robustness in varying conditions, contributing to improved performance in real-world scenarios. This study advances the field of automatic license plate recognition, showcasing the potential for practical applications in traffic management, law enforcement, and security systems. A public dataset of Egyptian car plates is used for training and testing the model. Two OCR approaches are used and tested which proved their performance, while CNN-based approach reaches 99.4% accuracy.

Railway Signal Digitalization with the European Rail Traffic Management System and Positive Train Control: Industry 4.0 Expectations and Reality

Positive train control (PTC) and the European Rail Traffic Management System (ERTMS) are digital railway signal systems in North America and Europe, respectively. They are frequently described as interchangeable, but they are not. This paper explains the history and motivations for each continent, and the general technical and capability differences between the two signal systems. In general, North America revised their signal systems to respond to safety concerns, and Europe committed to replacing their signals to encourage cross-border train traffic. The ERTMS is significantly more expensive than PTC, and the cost has been justified with expectations of greater capacity. Multiple studies find no basis for large capacity increases after implementation of the ERTMS. In addition, the added cost of the ERTMS threatens an already weak rail freight market in Europe.

Special Issue: Recent Advances in Intelligent Vehicular Networks and Communications

Over the last few decades, research on intelligent vehicular networks and communications has grown significantly due to the increasing demand for advanced vehicular technologies and the need for enhanced road safety, traffic management, and efficient transportation systems [...]

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

Optimization of energy consumption in vertical mobility systems of high-rise office buildings: A case study from a developing economy

Elevator systems serve as the primary mode of transportation in tall buildings which consumes approximately 5–15% of a building's total energy demand. This research explores the potential for energy savings in elevator systems while maintaining passenger comfort through the implementation of green approaches. The research concentrates on a contemporary high-rise office building situated in the central business district of a developing economy. It employs a case study methodology involving traffic simulation to determine optimal elevator specifications for industry-standard service levels. By conducting the simulation, the researchers identified the optimal number of elevators, elevator capacity, speed, and the most suitable elevator management system. Following that, a range of green measures were implemented, including the incorporation of a regenerative system, to effectively reduce the electrical energy consumption of the elevator system. Subsequently, a passenger traffic simulation model was integrated with an energy calculation model to jointly simulate and calculate the elevator system's overall energy consumption and regeneration. The elevator energy requirements were optimized, while maintaining user-friendliness and requirements related to guidelines given in the standards. The results showed that 36% of the energy consumption was reduced by incorporating an energy regenerative option into the elevator system in addition to selecting efficient mechanical components and implementing an efficient elevator traffic management system. This research contributes to the limited body of literature on energy optimization in elevators and emphasizes the importance of balancing energy efficiency with service quality. These findings provide guidance for establishing benchmarks in reducing energy consumption, in relation to elevator systems.

YOLO-Based Vehicle Detection: Literature Review

This research aims to evaluate the implementation of the You Only Look Once (YOLO) algorithm and its variants in the context of vehicle detection in traffic management systems. The importance of implementing intelligent transportation systems (ITS) in increasing transportation efficiency and reducing traffic problems such as congestion and accidents. The methodology used involves a critical review of current literature utilizing the YOLO algorithm for vehicle detection, with a focus on improving the accuracy of detection models. The research results show that the YOLO algorithm and its variants, such as YOLOv4 and YOLOv8, show a significant increase in vehicle detection accuracy reaching 90% in various environmental conditions. However, weaknesses in detecting small objects and in extreme lighting conditions still need further attention. This study also reviews several improvement approaches proposed in the literature, including the use of image augmentation techniques and the integration of deep learning models to improve the performance of the YOLO algorithm. The implementation of the YOLO algorithm in vehicle detection in intelligent transportation systems has great potential in increasing the efficiency and accuracy of traffic monitoring. This research provides recommendations for further development so that the YOLO algorithm can be better adapted to various environmental conditions and different types of data.

Challenges and Opportunities in integrating UTMS with Existing Airspace

The aviation industry has both potential and substantial obstacles as it integrates unmanned aerial vehicles (UAVs) into regulated airspace. In order to ensure safe and effective operations in shared airspace, this study examines the difficulties associated with integrating unmanned traffic management (UTM) systems with the current air traffic management infrastructure. By means of an in-depth examination of technological and operational factors, the research delves into the principal obstacles that stakeholders encounter when attempting to regulate the growing number of unmanned aerial vehicles. To promote seamless integration, the study also identifies areas for innovation, including standardized communication protocols, traffic flow management, and cooperative decision-making frameworks. This essay offers important insights into the future of autonomous traffic management and its consequences for the larger aviation ecosystem by fusing current academic findings with industry insights.

Intelligent Traffic Control with Convolution Neural Networks

In cities across the globe growing populations and more cars on the roads have led to worst traffic jams. This causes delays, stress higher fuel use, and more air pollution, with big cities feeling the worst effects. There's an urgent need to check road traffic density in real-time to control signals and manage traffic better. Our Smart Traffic Management System uses Convolutional Neural Networks (CNNs) and CCTV cameras to tackle this issue. This new approach allows for exact traffic density calculations, which helps adjust traffic signals based on how many vehicles are present. As a result, it eases congestion, speeds up travel, and cuts down on pollution. By using CCTV Cameras adding Convolutional Neural Networks, and tapping into the strengths of Computer Vision, our solution has a big impact on city travel. [4].