Traffic Load Distribution Research Articles

The integration of En route flow optimization, complex network clustering, and rule-based approach to airspace sub-sectorization for enhanced air traffic monitoring

This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization. The integrated framework combines the multi-commodity flow optimization approach, complex network clustering approach, and Minimum Bounding Geometry (MBG)-coupled Rule-based Approach for boundary design. A decomposition-based discrete particle swarm optimization (DPSO) is used to solve the clustering problem. The output of the flow optimization is used as a guiding standard for the DPSO. Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations (maxgen). The findings reveal that the multi-commodity flow approach captures system-wide flow operations. Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load. The MBG-coupled Rule-based Approach leads to compact and convex sub-sector boundary design. Major implications of this research include dynamic adaptability of the integrated framework, increased sensitivity of sector design to network evolution, and a computationally tractable framework. The higher controllability of the proposed framework also offers an increased acceptance among practitioners.

Congestion Transition on Random Walks on Graphs.

The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities. In this work, we propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand. Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow. In the approximation of the single-step process, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle. Our results explain how congested nodes emerge as the total traffic load increases, analogous to a percolation transition where the appearance of a congested node is an independent random event. However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes. Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network.

Extreme Value Prediction of Traffic Loads Using the Average Conditional Exceedance Rate Method

An efficient prediction of the extreme value of traffic loads is crucial for the structural design, reliability evaluation, maintenance planning, and further life-cycle cost analysis of bridges. In this work, a novel method is proposed for predicting the appropriate extreme traffic load distribution. Specifically, the average conditional exceedance rate (ACER) statistical model is estimated from the historical traffic loads which was collected through a weigh-in-motion system installed in toll stations. The basic idea of the ACER approach lies in the introduction of a cascade of conditioning approximations and the average exceedance rate to capture the dependence effects and obtain the data tail, the trend features of which are fitted with a similar Gumbel distribution function and extrapolated to the concerned level. An illustration case dealing with traffic loads using the ACER strategy is presented, the extreme value and confidence interval (CI) in any return period can be predicted by application of this approach. Furthermore, the peaks-over-threshold (POT) method based on the asymptotic extreme theory is also applied to illustrate the advantages of the ACER method. The ACER method has advantages in analyzing extreme traffic loads, with good robustness and the ability to handle extreme value prediction for different sampling strategies, it also can produce more accurate confidence intervals and predicts consistent extreme values. The study results are expected to help accurately determine traffic loads and ensure safety in bridge engineering.

Load Balancing On Mikrotik at Karang Jaya Health Center Using NTH Method

Communication needs are very important along with the progress and development of today's communication technology. This requires better network capabilities to handle large data traffic and improve access speed and quality. Karang Jaya Health Center only uses 1 ISP as the main running internet network. The network often experiences downtime and slowness because quite a lot of clients use the network. Applying the nth method of load balancing techniques is a solution to maintaining the quality of the internet network at the Karang Jaya Health Center. The research method for implementing the nth load balancing method is divided into two, namely the data collection method consisting of the stages of Observation, Interview, Documentation, and Literature Study. As well as the development method, namely the Analysis Stage, Design Stage, Simulation Stage, Implementation Stage, Monitoring Stage, and Management Stage. After configuring the nth load balancing method on the proxy device at the UPTD Karang Jaya Health Center, it was found that the traffic results from the division of the two ISP lines were active and the traffic load was divided evenly so that overloads could be avoided. Distribution of traffic load is prioritized by seeing which ISP is not busy with greater bandwidth speed. As well as managing bandwidth can optimize the performance of the internet network at the Karang Jaya Health Center.

Cooperative Game-Based Resource Allocation Scheme for Heterogeneous Networks with eICIC Technology

Heterogeneous network (HetNet) is considered to be the most promising approach for increasing communication capacity. However, HetNet control problems are difficult due to their intertier interference. Recently, the enhanced intercell interference coordination (eICIC) technology is introduced to offer several benefits, including a more equitable traffic load distribution across the macro and embedded small cells. In this paper, we design a new resource allocation scheme for the eICIC-based HetNet. Our proposed scheme is formulated as a joint cooperative game to handle conflicting requirements. By adopting the ideas of Kalai and Smorodinsky solution (KSS), multicriteria Kalai and Smorodinsky solution (MCKSS), and sequential Raiffa solution (SRS), we develop a hybrid control algorithm for an adaptive resource sharing between different base stations. To effectively adjust the eICIC fraction rates, the concepts of MCKSS and SRS are applied in an interactive manner. For mobile devices in the HetNet, the assigned resource is distributed by using the idea of KSS. The key insight of our algorithm is to translate the originally competitive problem into a hierarchical cooperative problem to reach a socially optimal outcome. The main novelty of our approach is its flexibility to reach a reciprocal consensus under dynamic HetNet environments. Exhaustive system simulations illustrate the performance gains along different dimensions, such as system throughput, device payoff, and fairness among devices. The superiority of our proposed scheme is fully demonstrated in comparison with three other existing eICIC control protocols.

An Effective Resource Allocation Strategy for Three-Layer Networks

The unbalance of resource allocation often confines the traffic capacity of network. The limitation of network resources highlights the necessity of rational allocation of network resources. In this paper, a resource allocation strategy based on node degree is proposed for three-layer network with different speed physical layers. Through simulation comparison, the traffic capacity of proposed strategy is about 1.5 times that of the average allocation strategy, and the reduction rate of total load can reach more than 60%. In addition, assigning appropriate delivery capacity to nodes reduces the average traveling time. Finally, through the proposed load-related indicators, the traffic load distribution comparison can be intuitively obtained.

A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals

The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP.

A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs).

WSN is one of the most efficient technologies in intelligent communication and because of its advantages, this technology has been utilized in various applications. By using WSNs, different types of data can be collected and analyzed in wide environments. The high variety of applications and types of data available in this network can cause several challenges about heterogeneous data routing. This research, presents a Fuzzy Model for Content-Centric Routing (FMCCR) in WSN to solve these challenges. The performance of FMCCR is based on two basic steps: "topology control", and "data transmission through content-centric and fuzzy logic-based routing algorithm". In the first step of FMCCR, the network topology is constructed. In the second step of the proposed method, data transmission paths are determined based on network topology and content type, and data transmission is performed. The performance of FMCCR has been evaluated in a simulation environment and the results have been compared with previous algorithms. The results show that FMCCR reduce energy consumption and improve the traffic load distribution in the network in addition to increasing the network lifetime. According to the results, FMCCR can increase network lifetime at least 10.74% and at the same time, deliver at least 88.1% more packets through the network, compared to previous methods. These results, prove the efficiency of the proposed method for using in real-world scenarios.

An improved system for long-term monitoring of full-bridge traffic load distribution on long-span bridges

Vision-based traffic load monitoring has attracted considerable attention in recent years due to its promise to enable continuous identification of full-bridge traffic load (FBTL). However, previous FBTL monitoring approaches have limits in small object detection and occluded object tracking, and have never been implemented in the whole range of long-span bridges. This paper proposes a long-term oriented FBTL monitoring system by introducing three improvements in vehicle detection, tracking and position correction to the existing framework that is based on the information fusion of weigh-in-motion (WIM) systems and multiple cameras. In the new system, vehicle detection is achieved by a YOLO-v4-based model, while vehicle tracking is realised by a dual-metric strategy combining vehicle motion and its appearance features. In addition, an efficient correction method for transverse vehicle position is derived from projective geometry, enabling the system to process a large number of vehicles at the same time. As the first complete implementation of FBTL monitoring on long-span bridges, the proposed system has been applied and verified on a cable-stayed bridge. The results suggest the proposed system is expected to provide long-term FBTL distribution, accompanied by faster calculation speed, higher small object detection accuracy, and better capacity to cope with the occlusion problem.

Dynamic Fog Federation Scheme for Internet of Vehicles

Federated fog computing is an answer for horizontally upscaling fog resources to improve the Quality of Service (QoS) of Internet of Things (IoT) applications. However, the dynamic nature of some IoT’s crucial components, such as the ones of Internet of Vehicles (IoV), may hinder the QoS improvement and result in its deterioration instead. Specifically, delays can occur due to the unoptimized distribution of services and unbalanced network traffic loads on the fog nodes. The current federated fog architectures ignore the mobility of users during the formation of fog federations. In this work, we present an adaptive and efficient fog federation formation scheme using game theory according to the service requirements. The problem formulation in terms of forming the federations and offloading requests among fog members is formulated as an integer program, then modeled as a Hedonic game. We adopt the Merge & Split as a formation technique, where the federations that are not satisfied in terms of QoS merge with other federations that would enhance the service performance. Our adaptive fog federation formation mechanism is designed to cope with the environmental changes in the IoV paradigm. Experimental evaluation shows that our framework can acquire better QoS and lower time to form the federations compared to the literature.

Reproduction of spatial–temporal distribution of traffic loads on freeway bridges via fusion of camera video and ETC data

Understanding the spatial–temporal distribution of traffic loads on bridge decks is essential for managing traffic flow and assessing bridge load effects. While computer vision can already identify on-bridge vehicle parameters through traffic surveillance cameras, obtaining cost-effective vehicle load information remains a challenge, even though it is the most important service load for bridges. In this study, we present a methodology that applies computer vision algorithms to fuzzy match video-identified data with electronic toll collection (ETC) recorded data to reproduce the spatial–temporal distribution of traffic loads on freeway bridges. The proposed method involves three critical steps. First, multi-object detection and trajectory tracking of vehicle contours, vehicle heads, vehicle tires, and vehicle license plates are implemented using YOLOv5 and DeepSort algorithms. Second, the conversion of image coordinates to real-world coordinates is automatically calibrated using pre-measured roadside reflection points. Third, vehicle license plate characters are identified by the LPRnet algorithm and the results are fused with highway ETC records to determine vehicle weight information, while the KNN clustering algorithm is used to further infer vehicle axle weight. After these three steps, the vehicle load sequence in the video surveillance area can be reproduced. Finally, the effectiveness and feasibility of the proposed method are verified in an example of a highway bridge.

Pavement Evaluation of Kampi Ya Moto - Eldama Ravine - Kamwosor B77 Road

Assessment of pavement performance is essential in determining the serviceability and structural state of the pavement structure. Pavement failure and deterioration always commence immediately after the road has been opened to traffic. Several factors contributing to pavement failure and damage include traffic load, soil, environmental, economic, and stress distribution factor. Therefore, flexible pavement failure and deterioration are well-defined by alligator cracks, concentrated potholes, ruts, settlement, and localised depression. Pavement evaluation determines highway sections' functional and structural conditions for frequent monitoring or planning for maintenance. Kampi ya Moto-Eldama Ravine- Kamwasor (B77) road is a Kenya National Highways, Authority (KeNHA) class B road constructed in 2008. The length of the road is approximately 79.5 Km. However, the pavement had numerous distress features which developed even before the design life of the pavement was attained. The study, therefore, concentrated on evaluating the performance of the pavement to identify the type and level of severity of pavement distress; to establish a surface condition by measuring the level of roughness and rut depth and to perform a visual evaluation survey of the existing flexible pavement distresses; to identify the reason of structural and functional failures on Eldama Ravine-Kamwosor, and finally, to establish the residual structural strength of the existing flexible pavement and suggests the most viable maintenance intervention measures.

Optimal Traffic Load Allocation for Aloha-Based IoT LEO Constellations

The deployment of satellite networks is key to providing global wireless connectivity for the Internet of Things (IoT). In this line, we consider a cluster of IoT devices served by a constellation of low-Earth-orbit (LEO) satellites, while slotted Aloha is used as a medium-access-control (MAC) technique in the uplink. To characterize the channel, we employ an ON–OFF fading channel model that estimates the quality of the links between the cluster of IoT devices and the LEO satellites within the constellation, by taking into account their relative positions. Since each relative position of the constellation with respect to the cluster of IoT devices leads to a different throughput for a given traffic load, we propose a novel traffic load distribution strategy based on successive convex approximation (SCA) to maximize the system throughput. The method adequately allocates the traffic load among the different constellation positions with respect to the IoT cluster. Finally, the results show that the proposed method outperforms other recently proposed strategies based on heuristics for traffic load allocation, while it also achieves a stable nonzero throughput even for large traffic loads.

Traffic aware dynamic load distribution in the Data Plane of SDN using Genetic Algorithm: A case study on NSF network

For a particular topology, with the best controller and switch positions SDN occasionally exhibits subpar performance, resulting in annoyance-inducing delays and unsatisfied users. Increased traffic volume can lead to congestion, which raises the burden on specific switches and controllers and lengthens the delay of data transmission. Additionally, the failure of specific nodes or links might result in an anomalous rise in the traffic load on specific switches and controllers. The conventional switch migration and congestion control techniques might not be sufficient to deliver the best throughput and capacity for frequent and unpredictable network traffic variations. Also, certain switches/controllers may experience heavy traffic load as compared to other switches/controllers. Under such adverse situations, the performance of the network becomes extremely poor. In light of these factors, this paper suggests an effective approach based on Genetic Algorithm for traffic load distribution in the Data Plane of SDN, which effectively reroutes packet flows (when such an adverse situation arises) in order to avoid abnormal load on switches/links and maintain acceptable network performance with the available resources. The proposed approach was applied to the NSF network and a case study was performed. The proposed approach was evaluated and found to be quite effective in reducing abnormal loads on switches/links as it is found that there can be a decrease in the average load by 396 units at the cost of increasing the average latency by 1.29 ms only.

General Tikhonov regularization‐based load estimation of bridges considering the computer vision‐extracted prior information

Estimating the load distribution of a bridge structure enables to evaluate the in-service state and predict the structural responses. This paper develops an iterative strategy to inversely estimate the traffic load distribution of a bridge from limited measurements. The computer vision technologies, including the YOLO network-based object detection and a pixel coordinate-based positioning approach, are used to locate the vehicle positions on the bridge deck and form a prior information vector of the input positions. Then, a generalized Tikhonov regularization method is proposed to estimate the load distribution using the bridge response and prior information. The regularization parameter is determined by the L-curve method. The fusion of computer vision and regularization can improve the load identification accuracy and reduce the overfitting effect. The developed approach is applied to numerical and experimental examples under various load conditions. The load can be accurately identified in all cases, and the full-field responses of the structures can be reconstructed with minor errors.

Effect of coupling structure on traffic-driven epidemic spreading in interconnected networks

Interconnected networks, as the good abstraction of many real complex systems, have recently attracted a lot of attentions. In this paper, we propose a traffic-driven epidemic model in interconnected networks to show the effects of interplay induced by the coupled structure. Through simulations on interconnected scale-free networks, we find that in the free flow state, the disassortative coupling can effectively suppress the spreading of epidemics by introducing a more uniform distribution of traffic load, especially for high coupling strength. This result has also been confirmed by our mean-field solution. When the system is congested, the results show that the epidemic threshold is subject to the critical point of the traffic process, which depends on the coupling mode, traffic routing and allocation mechanism of node capacity. We also check our model under different routing protocols, the results show that our findings are robust, indicating that the coupling structure is the determining factor of the dynamics on interconnected networks.

Weighted high priority packet transmission using ozmos bio-inspired mechanism for traffic load distribution (WH-DTO) in WSNs

Weighted high priority packet transmission using ozmos bio-inspired mechanism for traffic load distribution (WH-DTO) in WSNs

Integrated Simulation-Optimization Framework for Assessing the Impact of I-66 Dynamic Toll Pricing on Pavement Deterioration and Maintenance Decisions

Tolled facilities are undoubtedly expected to alter the distribution of traffic across the transportation network. On the other hand, traffic volumes and loading have an impact on deteriorating pavement conditions. These traffic volumes are considered by Departments of Transportation (DOTs) while allocating annual budgets to maintain and rehabilitate roadway segments to sustain pavement performance targets. This research studies the specific site around I-66 inside the Beltway, which newly applied dynamic tolls during a.m. and p.m. peak hours. An integrated traffic-management/pavement-treatment framework was applied to address both the operational and the pavement performance of the network. Aimsun hybrid macro/meso dynamic user equilibrium experiments were used to simulate the network with the modified cost function taking care of the dynamic pricing along the I-66 tolled facility. An optimization was Python-coded into a Pyomo framework to specify the optimal maintenance and rehabilitation treatment plan, taking into account critical condition index (CCI) deterioration based on the traffic load distribution on the network. Finally, the results of the simulation showed the importance of having an optimized treatment schedule to achieve optimal pavement performance outcomes, with a difference in CCI index that could range all the way from 68 to 95.

Improving Traffic Load Distribution Fairness in Mobile Social Networks

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.

EEG alpha band functional connectivity and network structure mark hub overload in Mild Cognitive Impairment during memory maintenance

IntroductionWhile decreased alpha-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences mark cognitive decline in the early stages of the disease.ObjectivesOur study aimed to analyze EEG FC and network differences in the alpha frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints.MethodsFC and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task. FC was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree approach.ResultsIncreasing memory load enhanced the mean alpha-band FC in the control group. In contrast to that, after an initial increase, the MCI group showed significantly (p<0.05) diminished FC in the highest memory load condition. Mean alpha AEC-c correlated significantly with the size and mean diffusivity of medial temporal lobe structures in the entire sample. The network analysis revealed a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution compared to the control group.ConclusionsAlpha-band FC correlates with cognitive load-related modulation, with medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism.DisclosureNo significant relationships.