Traffic Management Techniques Research Articles

An integrated intelligent intersection management and control system design

While a fair amount of study has been done recently on integrated management and control models for large-scale traffic systems, there is comparatively little information available regarding the design of management and control systems for small-scale intersections. This study aims to create the structural diagram of a management system for intersections and control technologies. It does this by designing three distinct systems: congestion prediction, traffic signal timing, and congestion flow dredging. This study searches the current state of common technology and chooses the intersection integrated management system by means of comparison. Fuzzy TOPSIS algorithm and ADP adaptive dynamic programming are chosen as the calculation techniques of signal lamp timing, while BP neural network and ATT-GCN network are chosen as the key algorithms of congestion prediction after thorough investigation. In the end, conventional traffic management techniques like forbidding left turns are used to direct cars that haven't arrived at the busy crossroads beforehand. This paper aims to select the best model for an intersection management system currently available from the common technologies. The idealized management system and its structure diagram are designed in a straightforward manner, and they will serve as a guide for future road and vehicular intersection design.

Deep Q‐network learning‐based active speed management under autonomous driving environments

AbstractEfficient traffic safety management necessitates real‐time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time‐to‐collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs.

Determining of Appropriate Passenger Car Unit Values Due to a Change in Effective Width of Roadway

Legitimately, the passenger car equivalent value is determined based on the type and width of the road as well as the traffic volume. Interestingly, the effective width of roadway often varies due to land topography conditions and level of side friction. Theoretically, the number of survey posts should be determined based on road geometric and environmental conditions because traffic volume is a reflection of the choice of speed. Unfortunately, due to limited funds, traffic volume, travel speed and ride friction surveys were carried out at 1 survey point. As a result. the recommendations for strategies and/or traffic management and engineering techniques could be bias. This study aims to calculate the impact of differences in the effective road width on the equivalent value of passenger cars using the Speed Method. For this reason, a speed survey was carried out on a number of arterial roads in the Kupang urban area. It was found that changes in the effective width of road lanes have a significant impact on changes in motorbike speed so that the use of passenger car equivalent value for motorcycle should be based on the result of field survey, instead of a standardized value as it was recommended by Indonesian Highway Capacity Guide

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

Traffic management approaches using machine learning and deep learning techniques: A survey

Traffic management is improved in cutting-edge smart cities using technologies such as machine learning and deep learning to streamline daily tasks and boost productivity. However, traffic management still suffers from challenging issues such as poor traffic congestion prediction, lack of traffic flow management, public transportation optimization, and emergency management. In this article, we provide a thorough understanding of the benefits, drawbacks, and practical implications of leveraging machine learning and deep learning in Traffic Management Systems (TMSs) by methodically reviewing and critically analysing various traffic management techniques. We also present a generic traffic management architecture that uses a set of assessment criteria to evaluate 20 recently proposed research prototypes (i.e., published since 2019). Finally, we highlight the ongoing challenges and prospective trajectories within the rapidly evolving domain of traffic management, underscoring the need to address emerging issues and directions in its dynamic development. This survey article offers insights that might help in efficiently tackling the issues posed by traffic management while maximizing the potential of machine learning and deep learning techniques. This survey can be a significant resource for researchers, policymakers, and practitioners.

The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

Unlocking QoS Potential: Integrating IoT services and Monte Carlo Control for heterogeneous IoT device management in gateways

The exponential surge in IoT devices has presented significant challenges in managing network traffic and maintaining Quality of Service (QoS) in IoT networks. To tackle these challenges, ongoing efforts are focused on developing innovative solutions that leverage advanced traffic management techniques, robust QoS mechanisms, and enhanced security protocols. Addressing these obstacles is crucial for the continued growth and success of the IoT system, enabling seamless integration of smart devices and revolutionizing multiple industries and sectors by providing unparalleled connectivity and intelligence. This article proposes an innovative approach to enhance the management of heterogeneous IoT devices and ensure Quality of Service (QoS) in IoT networks. Integrating device categories into the QoS policy aims to optimize overall performance while addressing the diverse QoS requirements of different device categories. The benefits of considering device categories include tailored resource allocation, improved user satisfaction, customized QoS handling, and scalability. The proposed approach utilizes the Monte Carlo Control algorithm to learn an optimal QoS policy through simulations of traffic scenarios. Our paper highlights Monte Carlo Control’s (MCC) rapid learning in diverse IoT settings, peaking at 50 episodes, compared to Q-learning’s 100 episodes and dynamic programming’s 200 episodes. Specifically, in audio IoT, MCC achieved 730 packets/second throughput with a 50 ms delay, outperforming QL (723 packets/second, 134 ms) and DP (700 packets/second, 171 ms). These results underscore MCC’s vital role in elevating IoT Quality of Service, making it pivotal for system performance and user experience optimization.

Dynamic Capacity Management for Air Traffic Operations in High Density Constrained Urban Airspace

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used.

Intelligence-Based Traffic Control System for Ambulances Using IoT

This study has been undertaken to know “The Intelligence-Based Traffic Control System for Ambulance Using IoT” is an innovative solution designed to optimize emergency medical services and enhance the efficiency of ambulance operations. This system leverages the power of Internet of Things (IoT) technology and intelligent traffic management techniques to ensure the swift and safe passage of ambulances through congested road networks.

ATM Network Technologies

Abstract: ATM networks use a range of technologies, including wired and wireless communication systems, advanced security features, and specialized hardware components to provide various financial services to customers. Traffic management techniques are also employed to ensure efficient use of network resources. ABR (Available Bit Rate) traffic management is a specific technique that allows the network to analyze traffic levels and adjust bandwidth allocation in real-time, ensuring optimal network performance. By implementing these technologies, ATM networks are able to provide a high-quality user experience while managing network traffic in an efficient and effective manner.

Why and how edge cloud computing can address performance and economic sustainability issues for telco domestic networks

This paper analyzes some of the telecommunication companies' (telcos) domestic networks' main objectives and issues related to the application services Quality of Experience (QoE), i.e., "technical QoE," and the economic sustainability of the Very High Capacity (VHC) networks. Telco domestic networks and Over The Top (OTT) networks are the Internet segments connecting the end-user equipment to the servers/clouds that provide application services. With the advent of VHC networks, telcos afford difficulties in managing effectively and efficiently the application services and their business sustainability. This paper affords some issues related to traditional telcos' domestic network architectures and the approach to managing the application services' performance improvement. Some telcos started changing their architectures, although a massive transformation had not yet started in the industry. Traditionally, telcos focus on network services, i.e., the transport of IP packets. Performance improvement is obtained by QoS-based traffic management techniques, such as bandwidth reservation and packet prioritization. To manage application performance improvement, the use of layer 4 techniques and Edge Cloud Computing (ECC) is effective, as demonstrated by the multiyear experience of OTTs which already use these technologies. In telco domestic networks, layer 2 tunnels increase the complexity of deploying ECC. However, some vendors provide non-standard solutions to make the IP layer 3 user plane visible and to deploy ECC. ECC is the key factor for the transformation. It is a mini/micro-data center that distributes some applications and content closer to the end users. The distribution can provide a paradigm shift in a telco's business. In the paper, we first highlight the need for regulators to appreciate the need to encourage this industry transformation fully.

Real-Time multi-objective optimization of safety and mobility at signalized intersections

ABSTRACT Adaptive Traffic Signal Control (ATSC) is becoming a popular dynamic traffic management technique, especially with the emerging connected vehicles (CVs) technology. ATSC algorithms have been extensively considered in the literature for enhancing traffic mobility at signalized intersections. However, improving safety has rarely been used as an objective in existing ATSC algorithms. To fill this gap, this paper proposes a multi-criteria reinforcement learning based ATSC algorithm with two optimization objectives: real-time safety and mobility. The algorithm was trained on both objectives using traffic simulation. The safety objective was considered using extreme value theory (EVT) real-time crash risk evaluation models. Reducing the total intersection delay was the mobility objective. Different weights were considered in the training to account for both objectives simultaneously. The performance of the trained algorithm was then validated using real-world video data. Results show that the proposed multi-objective algorithm can improve both safety and mobility even under lower weights.

Urban Road Network Partitioning Based on Bi-Modal Traffic Flows With Multiobjective Optimization

The recent extension of a macroscopic fundamental diagram (MFD) into a bi-modal MFD (or 3D-MFD) provides the relationship among the total network circulating flows and the accumulations of private vehicles and public buses. 3D-MFD reveals the contribution of large occupancy vehicles such as buses in improving urban transportation efficiency. A lot of bi-modal traffic management techniques are introduced based on 3D-MFD to improve the urban traffic efficiency without using detailed origin-destination (OD) information. However, similar to MFD, 3D-MFD is also highly affected by the heterogeneity of a road network. In order to form 3D-MFDs with low scatter to be utilized for further bi-modal traffic management, this paper proposes a partition method to cluster road links into several homogeneous regions for a bi-modal urban network. It is comprised of three layers named as initial partition, merging, and boundary adjusting. At the initial partition layer, Seeded Region Growing (SRG) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated to obtain a number of subregions. A modified Genetic Algorithm (GA) is developed to merge the subregions into larger regions at the merging layer. Then, boundary adjusting is performed by changing the region to which a boundary is clustered to optimize the result. Multi-sensor data collected from Shenzhen in China are utilized to verify the effectiveness of the proposed partition method.

Analisis Tingkat Layanan Ruas Jalan dan Persimpangan di Kecamatan Gandus Akibat Pembangunan Jalan Tol Kayu Agung – Palembang - Betung

The construction of Musi V Bridge is part of the series of Kapal Betung Toll Road, with the toll road in and out of Social Street which is connected to Lettu Karim Kadir Street. This study aims to obtain the performance of roads and intersections in Gandus District, obtain transportation modeling of traffic movements due to KayuAgung-Palembang-Betung Toll Road interchange, obtain predictions of the performance of roads and intersections resulting from traffic simulation models and provide policy suggestions in the form of strategies and traffic engineering management techniques. Data processing used the 2014 PKJI method and simulation with Vissim program. Results of data processing show that the performance of roads and intersections in existing conditions is still able to accommodate traffic flow, indicated by the degree of saturation that meets 2014 PKJI requirements, namely (DJ ≤ 0.85) with service level C and Vissim simulation results show the largest delay and queue length located in segment 3 (M Amin Fauzi Street -Tph Sofyian Kenawas Street) which is 138.18 meters and 21.02 seconds and on Lettu Karim Kadir Street (Direction of Musi II) of 14.04 seconds and 58.96 meters. After the operation of the Musi V Bridge, there was an increase in traffic volume which resulted in decreased performance of roads and intersections. Therefore, to improve its performance, road widening is carried out. The results of 2014 PKJI analysis showed a decrease in the degree of saturation from 0.43 to 0.35, a delay from 9.02 seconds / cur to 8.22 seconds / cur and the chances of queuing from 8.50 - 20.39% to 6.04 - 15.91% on Lettu Karim Kadir Street (Musi II Direction).

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Autonomous vehicles (AVs) are emerging with enormous potentials to solve many challenging road traffic problems. The AV emergence leads to a paradigm shift in the road traffic system, making the penetration of autonomous vehicles fast and its coexistence with human-driven cars inevitable. The migration from the traditional driving to the intelligent driving system with AV’s gradual deployment needs supporting technology to address mixed traffic systems problems, mixed driving behaviour in a car-following model, variation in-vehicle type control means, the impact of a proportion of AV in traffic mixed traffic, and many more. The migration to fully AV will solve many traffic problems: desire to reclaim travel and commuting time, driving comfort, and accident reduction. Motivated by the above facts, this paper presents an extensive review of road intersection mixed traffic management techniques with a classification matrix of different traffic management strategies and technologies that could effectively describe a mix of human and autonomous vehicles. It explores the existing traffic control strategies and analyses their compatibility in a mixed traffic environment. Then review their drawback and build on it for the proposed robust mix of traffic management schemes. Though many traffic control strategies have been in existence, the analysis presented in this paper gives new insights to the readers on the applications of the cell reservation strategy in a mixed traffic environment. Though many traffic control strategies have been in existence, the Gipp’s car-following model has shown to be very effective for optimal traffic flow performance.

Density-scaling traffic management for autonomous vehicle environment—predictive learning-based technique

In this paper, traffic and vehicle density optimization issues are discussed, and a suitable solution for autonomous driving environments is proposed. The problem of scalable traffic flow management is hindered by the unpredictable vehicle density and its navigation. This problem increases the flow congestion at the intersection during the change of lane in particular. This paper introduces density-scaling traffic management for autonomous vehicle driving scenarios. The proposed technique relies on navigation output and the autonomous vehicles' trajectory decisions for predicting their availability and time duration. The traffic flow is streamlines based on the predictions; the prediction error is mitigated by rescheduling the successive traffic flow. In this process, predictive iterated learning is employed for improving the scalable traffic flows in an intersection. The predictive learning handles navigation assistance output change of trajectory independently and correlates the decisions' match. The process of prediction matching is performed for improving the flow management with the time factor. The traffic management technique is useful in providing latency-less travel and errorless decision and flow complexity and collision probability.

A Survey on Traffic Management in Software-Defined Networks: Challenges, Effective Approaches, and Potential Measures

Software-defined networks (SDNs), as an emerging paradigm by separating the control plane from the data plane, increases flexibility and network utilization and reduces redundancy and operational cost. Traffic management of software-defined networks can be defined as network traffic monitoring and analyzing measures to improve network performance and quality of service metrics. Traffic management as an effective instrument for optimizing network traffic can offer the appropriate services according to network situation. Due to the inherent characteristics of SDN, special techniques are required to analyze, predict, and adapt the network traffic in order to achieve an efficient traffic management mechanism. This paper surveys traffic management techniques of SDN in four distinct categories including, routing, load balancing, congestion control, and flow control to cover the impressible issues. Moreover, the differences between SDN and traditional networks are analyzed in terms of traffic management necessities across the various groups to further determine the dimensions affecting research in this area. Furthermore, the available algorithms in each group and their role in traffic management are reviewed as well as the research challenges and future trends.

How Air Traffic Control Intervention Effects Altitude Deviations on Optimized Profile Descent Arrivals

U.S. National Airspace System modernization began with the publication of the Next Generation Air Transportation System Integrated Plan (NextGen) in 2004 to accommodate forecast air travel demand increases in the United States. This framework proposed an integrated approach to safety, environmental sustainability, reduced fuel burn, and increased airspace and airport capacity by using automated capabilities. One of these capabilities, the Optimized Profile Descent (OPD) is an automated procedure created to link the en route phase of flight with the terminal area within the context of NextGen goals. This type of automated procedure was developed during the NextGen short phase (2004-2012) for both air traffic control and aircraft, but they continue to be used in a non-integrated manner. It is the confluence of incompatible automated and manual air traffic management techniques that produce a favorable location for an altitude deviation. The purpose of this study is to determine the effect of air traffic control intervention on altitude deviations reported during optimized profile descent arrival procedures in the U.S. National Airspace System from January 1, 2012 to January 1, 2018. Examination of aviation safety reports from this time period showed that air traffic control intervention did effect altitude deviations, specifically in the areas of aircrew error, communication error, and equipment malfunction or limitation. This analysis also demonstrated the failure of the altitude deviation rate to return to normal historic levels after the introduction of NextGen procedures, making altitude deviation a leading safety indicator for the U.S. National Airspace System.

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.

Data representation for CNN based internet traffic classification: a comparative study

It has been well established that the Internet of Things will bring an expansion in traffic volume and types. This will bring new challenges in terms of Quality of Service (QoS) and security, requiring innovative traffic management techniques. Traffic classification is a main network function that helps in managing both QoS and security. Different machine learning based methods have been applied for this aim. However, traditional machine learning methods rely on hand crafted features, limiting the model ability to learn. Deep Learning (DL), a branch of machine learning, is characterized by its representation learning ability. In this paper, we analyse two methods of data representation for DL-based classification: a raw packet-based representation and a quasi-raw flow-based representation. Different tests are performed to evaluate the robustness of these data representation methods. The tests include features’ importance, model robustness, and anonymization tests. The results show that raw data representation suffers from traffic anonymization and the fact that many packet fields are data-dependent. On the other hand, the flow-based representation is sensitive to the number of packets used for classification and to traffic obfuscation.