Traffic Management Approach Research Articles

An adaptive framework for real-time freeway traffic estimation in the presence of CAVs

Advancements in sensor technologies, vehicle automation, communication, and intelligent transportation systems create unforeseen possibilities for the development of novel traffic management approaches in road transport systems. Furthermore, data observations with different accuracy and noise levels are fused towards advanced traffic state estimators. This work builds on the existing family of data assimilation techniques in the literature and proposes an online adaptive framework, fusing observations from static and moving sensors, along with estimations inferred from a traffic flow model and performing real-time traffic estimation in the presence of Connected and Automated Vehicles (CAVs). A real-world case study was used for validation and assessment of the proposed framework against well-known methodologies in the literature. The benefits and downsides of each approach for different scenarios are discussed, as well as the performance of each framework for different traffic models and penetration rates of CAVs.

Incentive-based cooperative traffic management: A state-of-the-art review

The DIT4TraM project capitalises on the concepts of swarm intelligence and mechanism design. One of its use-cases focuses on developing incentive-based traffic management approaches for local bottlenecks to nudge users towards the system-optimal behaviour, in the context of novel technological settings. This means that a traffic control problem has to be solved considering the technological setting, and that appropriate incentives for each driver need to be determined to ensure maximal fairness, compliance, and thus reachability of the desired state. To assess feasibility of such an approach, this paper reviews key literature in four areas: V2X communication technologies and architectures, data collection and state estimation for cooperative control, control methods for local, cooperative traffic management, and incentive schemes for traffic and mobility management.

Passenger car units for different midblock sections in Sri Lanka under mixed traffic condition

Traffic-related analyses are essential to implementing traffic management approaches to create mobility more convenient. In this context, Passenger Car Unit (PCU) values as a uniform measure of vehicles are instrumental in analysis such as traffic capacity estimation, traffic flow model development, level of service determination, etc. These studies always demand a recently calculated set of PCU values since the traffic conditions in arterials often change. PCU values for expressway and three-lane (one-way) road types have not been calculated in the Sri Lankan context, and the available set of values has become somewhat outdated. Hence, this study used Chandra's method to assess the PCU values of ten vehicle categories for four different mid-block road sections in the Colombo district, Sri Lanka. The data relating to the two main variables of this method, categorical speed, and area of vehicles, were collected from field surveys. The results of four-lane arterials were aggregated and compared with the existing PCU values, intending to propose an updated set for general transportation studies in Sri Lanka.

A combined computing framework for load balancing in multi-tenant cloud eco-system

<span>Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required.</span>

Factors Impacting Serviceability of Bypass Road

Abstract: Urban traffic congestion is one of the major problems of the developing countries. Growing volumes of through traffic are often attributed to traffic flow challenges. Bypass roads are constructed to reduce congestion in the city center. Bypass diverts through traffic away from town centers leaving local streets to local traffic. Often these bypasses are built to accommodate increasing volumes of traffic particularly truck traffic. The aim of this research is how to improve travel time on bypass roads and to study various traffic congestion management approaches in big cities. Traffic volume, travel time, vehicle specifications and geometric parameters were determined in the field as input for the microsimulation model. Different facilities were incorporated on bypass road to improve travel time. Calibration and validation of the model were done, and results of traffic volume and travel time were compared with actual field data. The results obtained shows that in most of the cases congestion was found near the intersections. The options of a flyover, bus bays, pedestrian overhead bridge and slip lines were assumed in the analysis; in result highest travel time decrease on bypass road due to provision of slip lines

SWSCAV: Real-time traffic management using connected autonomous vehicles

Traffic management methods aim to increase the infrastructure’s capacity to lower congestion levels. Using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) connectivity technologies, connected autonomous vehicles (CAVs) have the potential to operate as actuators for traffic control. In this study, a CAV-based alternative approach for traffic management is proposed (SWSCAV), and its performance is compared to that of lane control signals (LCS) and variable speed limits (VSL), which are also traffic management systems. When a shockwave is detected due to an incident, the CAVs on the road slow until they reach the speed of the observed shockwave (SWS), according to this proposed procedure. Thus, the incoming traffic flow towards the incident is slowed, preventing the queue behind from extending. In a simulation of the urban mobility (SUMO) environment, the suggested method is evaluated for 4800 scenarios on a three-lane highway by varying the market penetration rate of CAVs in traffic flow, the control distances, the incident lane, and the duration. The proposed method reduces the incidence of density values of over 38 veh/km/lane and 28 veh/km/lane in the vicinity of the incident region by 12.68 and 8.15 percent, respectively. Even at low CAV market penetration rates, the suggested method reduces traffic density throughout the network and in the location of the incident site by twice as much as the LCS system application.

INTELLIGENT TRANSPORT SYSTEMS AS TRAFFIC FLOW MANAGEMENT TOOL (THE CASE OF KYIV)

The low quality of public transport services, the inconvenience of routes, the long duration of trips and several other problems encourage Kyiv residents to increasingly choose a car for everyday correspondence, which, in turn, increases delays, travel time and leads to environmental pollution of territories. These problems are especially acute in large cities, where numerous industrial, labour, and cultural and household connections are within the city. There is a need to create a single efficient transport system that serves as a city and implement effective traffic flow management approaches. 
 First of all, the article defines the concepts of "traffic flow management", "urban mobility" and "urban mobility regime". Four groups of problems of urban transport systems were identified and two ways of solving them were formed: extensive and intensive. Using the instrumental case of Kyiv, the level of implementation of systems for monitoring and managing traffic flows in Ukrainian cities was evaluated. The levels of the legal framework regulating the urban mobility of the population in Ukraine are described. The characteristics of intelligent transport systems and their key subject areas have been studied. Commonly accepted services for users of intelligent transport systems (ITS) are distinguished and segmented according to the needs of these system users. Features, methods of application and technologies of intelligent transport systems are presented. Services that are a priority for potential users of intelligent transport systems in Ukrainian cities are highlighted. The world experience of the formation and development of intelligent transport systems, as well as European programs for their implementation, is characterized. A SWOT analysis of the implementation of intelligent transport systems in Kyiv was carried out and recommendations were made for the creation of an ITS implementation program in Kyiv. 
 The information base of the article consists of publications by prominent Ukrainian and foreign researchers, data from the Tom Tom transport analytics service, the current legal framework of Ukraine, which regulates urban transport systems, and urban development strategies. The following methods of scientific research are used in the research process: dialectical method; method of scientific abstraction, analysis and synthesis; method of induction and deduction; comparative method; method of systematization and graphic method.

Automated vehicles in swarm configuration: Simulation and analysis

Driving automation is becoming an increasing trend in the automotive industry as vehicles become more and more intelligent, while information is shared among them and with the infrastructure via wireless communication. Vehicle organization in swarms is considered an interesting approach for future traffic management, since it could significantly improve road efficiency and safety, while saving resources and increasing system resiliency. In this work, modelling and simulation of swarm traffic flow have been addressed. A decision support simulation tool for swarm intelligence traffic flow modelling including different types of vehicles and roads has been implemented using MATLAB. Functionalities such as driving, lane changing, overtaking and swarm cooperation are included. The behaviour of swarms of multi-brand platooning vehicles on different roads and driving conditions has been analyzed. The parameters that define the automated vehicle swarm have also been studied, such as its size, desired speed, inter-vehicle distance, etc., and how the swarm configuration depends on the application (highway traffic, traffic jam assist, …). The result of this research shows how traffic flow can be maximized when vehicles are managed in a swarm configuration.

Vanet‐TSMA: A traffic safety management approach for smart road transportation in vehicular ad hoc networks

SummaryOver the past two decades, vehicular collisions have been a significant concern for governments, researchers, and automobile manufacturers. However, collisions are unexpected and occur regularly on roads, resulting in deaths, infrastructure destruction, and health injuries. As a result, it is essential to avoid, prevent, and detect traffic collisions at the highest degree possible for minimizing human loss. In this article, the trend of road traffic accidents in India has been analyzed graphically. Towards the end, a VANET‐based traffic safety management approach (VANET‐TSMA) has been proposed to reduce road accidents in India. This approach uses message distribution, traffic management, and congestion control efficiently for reducing the overall rate of road accidents. This method is based on multiple information and communication technology (ICT)‐based safety mechanisms which include collision avoidance, collision detection, and traffic congestion avoidance for the protection of future connected automobiles on roads.

Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data.

Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city’s traffic management.

Scanning the Issue

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Two-Stage Scalable Air Traffic Flow Management Model Under Uncertainty</b> <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>G. G. N. Sandamali, R. Su, K. L. K. Sudheera, Y. Zhang, and Y. Zhang</i> <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In order to efficiently balance the current and future air traffic demands with the system capacity, a proper air traffic flow management (ATFM) approach is required. The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities while maintaining the required safety separation between aircraft. Yet, only a minor focus is given to the inherent uncertainty in the air transportation system (ATS), especially to its adverse effect on safety and day-to-day operations. To this end, the authors propose an ATFM framework for scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach. Moreover, anticipating the high volumes in air traffic in the future, the authors propose to split the model into two stages, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage transforms them into individual flight plans, enhancing scalability. The two models are formulated as an integer linear programming (ILP) problem, and a mixed-integer linear programming (MILP) problem at stages I and II, respectively. The NP-hard nature of the overall problem is minimized by transforming the problem into a maximum weighted independent set (MWIS) finding problem.

Simulation Modelling of Traffic Flows in the Central Business District Using PTV Vissim in Pekanbaru, Indonesia

The state of art simulation modelling in managing traffic flow within a busy central business district (CBD) is demonstrated in this article. The objectives of this article were to; identify the existing condition of the street V/C ratios within the CBD area, and to develop various simulations modelling. The development of microscopic traffic simulations encompassing 5 streets within the CBD area in Pekanbaru, Indonesia using the PTV Vissim application was performed as a case study. The existing conditions of these 5 streets were very busy (Level of Service C and D) with on-street parking and on-street traders (vendors) along the streets. Several simulations model based on 4 traffic management approaches were applied; (i) controlling on street traders (vendors) activity, (ii) controlling parking lots, and (iii) changing the on-street parking angle and (iv) combination of these approaches. The simulation results have indicated that the optimum simulation modelling in managing CBD traffic flow within these 5 streets was conducted by managing the on-street parking angle at the position of 300 as well as controlling on street traders activity. Hence, by conducting simulations using this application improved the streets Level of services (LOS) within this CBD area from D (0,75<VC<0,84) to C (0.45<VC<0.74).

Impact of Managed-Lane Pricing Strategies on Vehicle-Sourced NOx and HC Emissions

Ground-level ozone is a secondary air pollutant that is formed by chemical reactions between precursors, including nitrogen oxides (NOx) and hydrocarbon (HC). Highway traffic, which can be controlled by traffic operational strategies, is one of the main sources of atmospheric NOx and HC. Managed-lane pricing is one of the popularly used freeway traffic management approaches, while its impacts on ground-level ozone-related vehicle emissions is, however, still unclear. This motivated the purpose of this research. A case study in Houston, USA indicates that, vehicles on managed lanes had fewer hard accelerations/decelerations and higher average speed, which resulted in higher per-vehicle emissions in grams/hour, while the total emissions of a vehicle were roughly comparable to what they would be on a general-purpose lane. Total daily NOx and HC emissions per managed lane were 31.9%–42.6% of those per general-purpose lane. The weight ratios between HC and NOx show that, the ground-level ozone formation of this area is hydrocarbon-limited.

Assessing the effects of smart roads on autonomous vehicle navigation an analysis of real-time traffic management and data-driven approaches

This study examines the impact of smart road infrastructure on autonomous vehicle navigation, with a focus on advancements in real-time traffic management and data-driven methodologies. In response to the increasing complexity of urban environments, smart roads—incorporating sensors, wireless communication, and adaptive signaling—demonstrate significant potential for enhancing autonomous vehicle performance, particularly in the areas of route optimization, collision avoidance, and energy efficiency. This research evaluates current smart road initiatives and analyzes their influence on autonomous navigation using data-driven traffic models and real-time control applications. Findings show that with integrated smart road technologies, travel time can be reduced by up to 30%, collision risk lowered by 50%, and fuel consumption decreased by approximately 30%. The combined impact of these technologies enables autonomous vehicles to anticipate and respond dynamically to changing traffic conditions, improving safety and minimizing delays. Through case studies and empirical data, this paper highlights critical innovations in smart road technology, showcasing its role in facilitating seamless, efficient navigation for autonomous systems. These insights offer a foundational understanding of the interplay between smart infrastructure and autonomous vehicle systems, underlining its potential to drive forward autonomous mobility solutions.

Density based Traffic Monitoring system in Haze and FOG Conditions

The problem of traffic congestion has increased now-a-day’s due to the rapid growth of population in major cities. Overwhelming number of vehicles and insufficient roads are the major causes of traffic congestion. This needs new technologies to be adopted, and a better approach for effective traffic management. In the literature, researchers use conventional methods such as IR sensor, wireless sensor, and Fuzzy logic to measure the traffic density. The main limitations of such conventional methods are that they require personal monitoring of the traffic and ineffective to work in foggy weather. The main aim of this work is to develop a real-time adaptive density-based traffic management system that can quantify number of vehicles on roads under foggy weather conditions. The proposed system involves video acquisition, frame extraction, fog removal and vehicle counting. At first, the video is captured by camera and split into number of frames using frame extraction process. The Dark channel prior (DCP) algorithm is used to remove the fog from each frame and the background subtraction method and certain morphological operations are used to count the number of vehicles in real-time. Based on the vehicle count, the system specifies the time required to clear the traffic. This could facilitate ease traffic flow, save time, and even operate in foggy weather conditions, which is an improvement from the conventional timer-based operations of traffic signals.

ПОДХОД К УПРАВЛЕНИЮ ТРАНСПОРТНЫМИ ПОТОКАМИ НА ОСНОВЕ СТАНДАРТА МЭК 61499

The number of vehicles on public roads is constantly increasing, and the development of roadinfrastructure is proceeding at a slow pace, and not high-quality transport management entails anincrease in transportation costs, an increase in accidents, noise levels, and environmental pollution.As a consequence, there is a need to apply advanced algorithms and approaches to transport managementin order to maximize the use of the existing road network and increase road capacity. In thecourse of recent studies, it has been revealed that adaptive approaches to traffic management aremost effective on sections of the road network with high traffic intensity and variability. The essenceof the approaches to adaptive management used today is that they are based on the analysis of trafficcongestion and change the phases of traffic light operation depending on the received data in realtime .. Adaptive traffic management shows much better results compared to tight control , significantlyreduces transport delays, travel time and emissions of harmful substances into the atmosphere,therefore, modern researchers are developing new and improving existing approaches and algorithmsfor adaptive transport control. For example, traffic management approaches based on theconcept of IoT and the use of cloud computing are actively developing. The concepts of applying theagent-based approach to adaptive control are also being developed. The paper proposes a methodfor managing traffic flows and automating road infrastructure using an agent-based approach. Theproposed approach includes distributed management of various elements of the road network andtheir direct interconnection with each other. To implement this concept, the open standard of distributedcontrol and automation systems IEC 61499 was used, and to test the feasibility of implementation,several models of traffic intersections were used, one of which was created on the basis of realdata and SUMO - a microscopic and continuous traffic simulation package.

A Machine Learning-based Approach for Network Traffic Analysis and Management

For a network to function properly and remain secure, network traffic management and analysis are essential. In this field, machine learning-based techniques have demonstrated considerable potential by offering precise and effective network traffic analysis and anomaly detection. In this research, we offer a machine learning-based methodology for network traffic monitoring and management. This method analyses network data and identifies network anomalies using a variety of machine learning methods. Using the NSL-KDD dataset and other machine learning methods, such as decision trees, SVM, neural networks, and random forests, we assess the effectiveness of our strategy. The outcomes of our tests show how successful our suggested strategy is, with high accuracy rates and low false positive rates. In numerous network management and security applications, our suggested approach beats cutting-edge machine learning-based algorithms for network traffic analysis and management. The suggested strategy offers a positive perspective for improving network administration and security through machine learning.

Knapsack problem-based control approach for traffic signal management at urban intersections: Increasing smooth traffic flows and reducing environmental impact

Urbanism development makes cities more congested and then more polluted. Hence, the primary factor that influences the urban environment directly is traffic flow. Therefore, this complex system requires efficient control methods to reduce its impact at urban zones, in particular for traffic light within intersections. In this paper, a dynamic control strategy of traffic signal at urban intersections is proposed. This strategy is based on Knapsack-problem and enables to manage the green light duration autonomously following the queue length for each road lane. The dynamic behavior of this system is considered as a discrete event system. Consequently, a modular Timed Synchronized Petri Net (TSPN) model is developed. Depending on real-time communication, the TSPN modules represent the ”slaves”, and the responsive controller represents the ”master” that manage optimally the vehicles evacuation at the intersection. Moreover, for the system dependability, some interesting properties of the system are checked through the developed Petri net model. SUMO based simulations are performed and analyzed to validate the proposed approach. Through the performed simulations and the analysis of the proposed dynamic control approach and findings show the efficiency of our control policy about smooth traffic increasing and environmental impact reducing.

Two-Stage Scalable Air Traffic Flow Management Model Under Uncertainty

In order to efficiently balance the current and future air traffic demands with the system capacity, a proper Air Traffic Flow Management (ATFM) approach is required. The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities, while maintaining the required safety separation between aircraft. Yet, only a minor focus is given to the inherent uncertainty in the Air Transportation System (ATS), especially to its adverse effect on safety and day-to-day operations. To this end, we propose an ATFM framework scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach. Moreover, anticipating the high volumes in air traffic in the future, we propose to split the model into two stages, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage transforms them into individual flight plans, enhancing scalability. The two models are formulated as an Integer Linear Programming (ILP) problem, and a Mixed Integer Linear Programming (MILP) problem at stages I and II, respectively. The NP-hard nature of the overall problem is minimized by transforming the problem into a Maximum Weighted Independent Set (MWIS) finding problem.

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.