Traffic Optimization Research Articles

Addressing the urban congestion challenge based on traffic bottlenecks

Historically, urban congestion and street life quality depended on city network hierarchies, shaped by land use and street layout. Yet navigation apps have shifted focus to travel time as the key route selection factor, challenging traditional urban structures. We review the development of an innovative approach to urban traffic management that leverages real-time data for the identification and analysis of traffic bottlenecks. This approach, combined with urban planning, aims to improve traffic flow and tackle modern urban challenges. It includes real-time bottleneck detection and cost, congestion analysis and designing a decentralized traffic management system that can serve planners. Based on complex system principles, it promises dynamic traffic optimization, merging urban planning with digital advancements. The research demonstrates the potential of different applications of the proposed methodologies to predict significant congestion from early bottleneck formation, offering urban planners a powerful toolset for reasserting their role in shaping the urban experience. This article posits that a nuanced understanding of traffic dynamics, coupled with advanced traffic management technologies, can restore the influence of urban planning in the digital era, fostering more liveable, equitable and efficient urban environments. This article is part of the theme issue ‘Co-creating the future: participatory cities and digital governance’.

Fast-Time Simulations to Study the Capacity of a Traffic Network Aimed at Urban Air Mobility

This article investigates viable solutions to implement an Urban Air Mobility network in Milan, Italy, and analyzes its influence on the airspace capacity. The network comprises eight vertiports for passenger transport among two main airports in the area and the city using electric vertical take-off and landing aircraft (eVTOLs). A Fast-Time Simulation (FTS) model with the software AirTOp (Air Traffic Optimization) allowed the evaluation of the ideal capacity of the network by varying two configurations, which differ from each other in terms of the number of Final Approach and Takeoff areas (FATOs). The results show how it is possible to reach high hourly capacities (in the order of one hundred), thus allowing the use of the service for about 4% of the total passengers passing through the two airports during the reference day chosen for this study. However, the results are ideal due to the strong idealism of the system, which overlooks several factors, and they should be considered as the maximum limit that can be obtained. Despite this, the method presented in this article can also be adapted for other urban areas with high population densities. In addition, the use of a simulation tool of this type allows, in addition to a numerical analysis, a qualitative analysis of the network behavior in terms of traffic, thus highlighting the criticalities of the proposed systems.

Research on Traffic Optimization and Growth Strategy under Precision Marketing -- A Case Study of Social Media Xiaohongshu

With the development and progress of social media, digital marketing has ushered in a new era. With over 200 million monthly active users, Xiaohongshu has shown brands the potential of precision marketing and traffic optimization on social media. Major brands have joined Xiaohongshu to improve their communication matrix. Therefore, this article adopts the literature analysis method to analyze the marketing status and reasons and problems for the existence of traffic on the Xiaohongshu platform, and explores strategies for optimizing traffic on the Xiaohongshu platform based on the 4C template. In the era of digital marketing, the Xiaohongshu brand needs to reposition itself accurately, promote personalized marketing, use technological methods to create creative content, attract user attention, and use diverse interactive methods to establish emotional resonance with users. In order to improve the drawbacks of Xiaohongshu’s traffic growth, enhance its traffic utilization rate, improve Xiaohongshu’s user reputation, and provide more accurate theoretical basis and development direction for social media precision marketing, ushering in long-term and healthy development.

Design and Implementation of a Microcontroller-Based Adaptive Four-Way Traffic Light Control System for Traffic Optimization

This paper presents the design and construction of a microcontroller-based four-way traffic light control system aimed at optimizing traffic flow by automatically adjusting signal timing based on traffic density at each intersection. The system is built around an Arduino ATmega328 microcontroller inter-faced with break beam infrared (IR) sensors (transmitters and receivers) and LED displays. The IR sensors are installed on both sides of the lanes at regulated intervals to detect traffic density. The system is powered by a 12V DC battery and a 5V, 3A power supply is provided using a buck converter IC (LM2596), which steps down the 12V from the battery to 5V, 3A. This 5V power is used to run the Arduino microcontroller and the Darlington pair ICs for current sinking and sourcing. As vehicles pass through the areas monitored by the IR sensors, the traffic density is measured for each opposing lane, allowing the system to determine which lane should be prioritized for traffic flow. The corresponding LED indicators are then activated accordingly.

Large Language Models Meet Next-Generation Networking Technologies: A Review

The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.

A dictionary-based Bayesian approach to optimizing left-turn restriction locations in grid networks

Left-turn movements at signalized intersections pose significant safety risks for the drivers and efficiency concerns for the traffic operations in urban networks. Restricting left-turn movements at selected locations has been shown to be effective at improving operational efficiency and mitigating safety concerns. However, determining optimal locations to restrict left-turns is a complex combinatorial optimization problem that is compounded by the lack of analytical forms for the objective function and constraints, as well as potential interdependencies between the decision variables. Following the common solution paradigm for this type of optimization problems, this paper presents a novel Bayesian approach that utilizes dictionary-based embeddings and is tailored for high-dimensional combinatorial (or even mixed) spaces. Simulation studies conducted using the Aimsun software under perfect or imperfect grid networks demonstrate that the presented method can consistently find promising left-turn restriction configurations that outperform the all-or-nothing strategies (to restrict all or none left-turn movements at all intersections), as well as the population based incremental learning algorithm. In addition, the presented method often does so with less simulation cost, thus showcasing its potential for efficient solution of more general traffic optimization problems.

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.

Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the MK-LDA (MeanShift Kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-K-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

Connected vehicles ecological driving based on deep reinforce learning: Application of Web 3.0 technologies in traffic optimization

With the fast development of Web3.0 technology, connected vehicles can now handle and communicate data more safely and effectively. When combined with 5G/6G communication technology, these vehicles can optimize emissions in transportation networks to a greater extent. This study proposed an ecologically car-following model from natural driving data by using deep reinforcement learning under the context of the rapid development of Web 3.0 technologies. Firstly, by utilizing naturalistic driving data, an environment for connected vehicle car-following is created. Secondly, this paper uses SAC (Soft Actor-Critic) deep reinforcement learning algorithm and designs novel reward function based on ecological driving principles and car-following characteristics to reduce fuel consumption and emissions while maintaining safe distance with leading vehicle. Subsequently, the established model is tested, and results indicate that model not only performs well in terms of collision occurrences, Time-to-Collision (TTC), and driving comfort on test set but also achieves reduction of 5.50% in fuel consumption and reductions of 15.04%, 5.63%, and 9.60% in pollutant emissions (NOx, CO, and HC) compared to naturalistic manually driven vehicles.

Dynamic Traffic Optimization in Smart Cities (DTOS): Integrating OpenStreetMap, IoT, and Fog Computing

Dynamic Traffic Optimization in Smart Cities (DTOS): Integrating OpenStreetMap, IoT, and Fog Computing

INTEGRATED CONVOLUTIONAL NEURAL NETWORKS APPLICATION FOR ROAD TRAFFIC EFFICIENCY

Road traffic is a problem that every country has to face for the benefit of its population. Everything related to the transportation of people, goods, and services can be delayed due to a deplorable infrastructure. From minor tasks like individual shopping to important services like ambulances and fire trucks, all vehicles in Romania will eventually pass through a traffic light-controlled intersection that is not managed in real-time and that is also highly inefficient. At such a location, it is critical for traffic flow to be as smooth as possible to prevent time losses, and for traffic information to be stored into a cloud for process optimization. The purpose of the developed application is to reduce the real-time traffic jams to allow the optimal vehicles movement in safe conditions for all traffic participants. Using deep learning (DL) and artificial intelligence (AI) techniques, the application is based on two convolutional neural networks (CNN’s), respectively one for real-time detection of cars and people, and another one for real-time detection of cars license plates. The cars detections with CNN’s is important for road traffic optimization, for the efficient pedestrian detection necessary for crosswalks, and the car licence plates detection necessary for preventing traffic violations. The developed application contains several modules that operate based on deep learning and devices that communicate with each other, ensuring efficient data saving in cloud, data visualization, real-time traffic light control, and the information processing from traffic cameras.

Real-Time Traffic Optimization in Urban Areas Using Liquid Neural Networks

Real-Time Traffic Optimization in Urban Areas Using Liquid Neural Networks

Leveraging SUMO for Real-World Traffic Optimization: A Comprehensive Approach

This paper illuminates the utilization of SUMO as a powerful tool for addressing real-world traffic management issues. There is a gap in testing and validating solutions to in-field conditions due to the high cost and complexity of urban and suburban road networks. The validation step is often skipped, which can lead to a higher risk in implementing sophisticated solutions that exist in our multimodal transportation environment. This challenge is addressed by introducing simulations as a crucial preliminary step before real-world application. Accurate simulations require detailed data on intersection geometries, vehicle distribution, and driver behavior to accurately mirror real-world conditions. To meet these criteria, detailed sensor data on trajectories, types of road users, and their locations are extensively employed. This data forms the foundation for calibrated traffic simulations by NoTraffic™ . In conclusion, an in-depth demonstration of the method used to address a real-world traffic problem with SUMO is provided, emphasizing SUMO’s effectiveness in building confidence for deploying solutions in the field.

Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

A bi‐level emergency evacuation traffic optimization model for urban evacuation problem

A bi‐level emergency evacuation traffic optimization model for urban evacuation problem

Multi-agent modeling of traffic organization in urban agglomerations

The authors consider the features of multi-agent modeling for traffic optimization in the central areas of cities. While evaluating the unique challenges associated with the high concentration of vehicles, pedestrians and historical buildings, the potential of multi-agent systems to effectively solve the problem of congestion, safety and quality of life in urban areas is investigated. The potential of multi-agent modeling in the context of traffic management in the central areas of the city allows us to identify the key challenges and opportunities. Many scientists address the main aspects of such modeling and use them in the transport and road sectors. A review of current research and development has shown that multi-agent models aim to simulate and optimize the supervision and control of transportation in various traffic scenarios. Modeling traffic organization in the central areas of cities is one of the main elements of urban development planning and management. Due to the growing population of cities and the increasing number of vehicles, the problems of congestion, air pollution, and inefficient use of infrastructure are becoming increasingly relevant. Therefore, it can be noted that multi-agent traffic modeling opens up new prospects for developing effective traffic management strategies, providing a flexible and adaptive solution to these problems. The research analyzes the existing approaches, identifies the system`s key components, and develops a model that demonstrates the interaction between agents and the environment based on a mathematical description. A practical simulation of the model, carried out using the AnyLogic software on the example of Lesia Ukrainka Boulevard in Kyiv, confirms the effectiveness of the multi-agent approach. The results of the study indicate the possibility of applying the developed model to improve intelligent information systems for traffic flow management, which opens up new prospects for improving traffic in the central areas of cities.

Interpersonal Communication Interconnection in Media Convergence Metaverse

The metaverse aims to provide immersive virtual worlds connecting with the physical world. To enable real-time interpersonal communications between users across the globe, the metaverse places high demands on network performance, including low latency, high bandwidth, and fast network speeds. This paper proposes a novel Media Convergence Metaverse Network (MCMN) framework to address these challenges. Specifically, the META controller serves as MCMN's logically centralized control plane, responsible for holistic orchestration across edge sites and end-to-end path computation between metaverse users. We develop a model-free deep reinforcement learning-based metaverse traffic optimization algorithm that learns to route flows while satisfying the Quality of Service (QoS) boundaries. The network slicing engine leverages artificial intelligence and machine learning to create isolated, customized virtual networks tailored for metaverse traffic dynamics on demand. It employs unsupervised and reinforcement learning techniques using network telemetry from the META controller to understand application traffic patterns and train cognitive slicer agents to make quality of service -aware decisions accordingly. Optimized delivery of diverse concurrent media types necessitates routing intelligence to meet distinct requirements while mitigating clashes over a shared infrastructure. Media-aware routing enhances traditional shortest-path approaches by combining topological metrics with workflow sensitivities. We realize an edge-assisted rendering fabric to offload complex processing from bandwidth-constrained endpoints while retaining visual realism. Extensive simulations demonstrate MCMN's superior performance compared to conventional networking paradigms. MCMN shows great promise to enable seamless interconnectivity and ultra-high fidelity communications to unlock the true potential of the metaverse.

Cooperative traffic optimization with multi-agent reinforcement learning and evolutionary strategy: Bridging the gap between micro and macro traffic control

The emergence of connected and autonomous vehicles (CAVs) holds promise for fine-grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is a need for an in-depth exploration of integrating the microscopic speed control of CAVs with the macroscopic variable speed limit (VSL) of human-driven vehicles (HDVs). This paper proposes a Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME) framework, which combines microscopic CAV control with macroscopic VSL control. The framework incorporates a Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework for intelligent decision-making by microscopic-level vehicles. Additionally, an evolutionary strategy is developed to design the VSL network architecture, enabling macroscopic level real-time speed limit adjustments based on infrastructure. A multi-objective reward function is proposed to optimize both micro and macro efficiency and safety, accounting for both vehicle behavior and traffic flow. Experiments on the designed Bottleneck traffic scenarios show that the proposed approach, CTO-ME, is able to achieve superior performance and outperforms other baselines in terms of traffic throughput, average speed, and safety. Specifically, CTO-ME enhances average velocity by 37%, increases overall throughput by 309%, and raises arrival ratio by 70% than traditional Intelligent Driver Model (IDM).