Congestion Factor Research Articles

An Efficient Congestion Avoiding Approach for Optimal Path Finding for Emergency Vehicle

Background: Emergency vehicles required a quick clearance so as to reach the destination with minimum delay and the human life could be saved. Emergency vehicle required a dedicated path for clearance. The dedicated path creates a chaos by blocking the entire route for others vehicles and it is not always possible to create a dedicated path. So there is an imperative need for a smart road navigation system which adapts to the traffic congestion in real-time velocity of vehicle, count of vehicle, number of lanes and distance from source to destination. Objective: The objective of this paper is to find an optimal route for providing a least congested optimal route for emergency vehicle with least delay considering various issues such as congestion, numbers of vehicles, average traffic flow on the roads and width of the lane between source and destination. Proposed approach: Real-time traffic data like number of vehicles, velocity of the vehicles, and the width of the road and distance of the route are used to determine the congestion factor on all possible route. Congestion factor is used for finding the shortest route to requesting emergency vehicle. Result: Experimental results establish that the travel time of a vehicle is reduced by approximately 26%, when the vehicle uses the optimized route. This is beneficial for any emergency vehicle as the optimal path is provided on a real-time basis. Conclusion: This research work proposes an analytical approach that provides the least congested optimal route on-demand based on real-time traffic.

TCPW-F: A Congestion Control Algorithm Based on Noise-aware in Wireless Networks

Due to the wireless link with more noise packet loss and higher latency as compared with the wired link, conventional TCP Westwood (TCPW) variants over wireless channels perform unsatisfactorily when competing for the shared capacity of the bottleneck link with other variants. The shortcoming is these variants mistakenly interpret noise packet loss as a congestion signal. This leads to frequent delay jitter and long latency due to the inability to distinguish the type of packet loss, which reduces the transmission efficiency of the network. Therefore, we propose a novel TCP variant (TCPW-F) that distinguishes noise loss and congestion loss. By constructing the congestion factor F, the real-time available bandwidth can be calculated. When receiving congestion signals, it ignores the false of them induced by noise packet loss upon the wireless link but responds to true congestion according to backlog-constrained-based judgment. Therefore, TCPW-F is widely used in wireless networks with large noise interference. Numerical analysis and simulation results show that TCPW-F resists noise interference, and smooths the delay jitter per unit time. And the throughput of TCPW-F is significantly higher than other TCPW variants. TCPW-F improves the transmission performance in wireless networks.

Air Traffic Assignment for Intensive Urban Air Mobility Operations

In high-density urban air mobility (UAM) operations, mitigating congestion and reducing structural constraints are key challenges. Pioneering urban airspace design projects expect the air vehicles to fit into structured UAM corridor networks. However, most existing air transport networks are not capable of handling the increasing traffic demand, which is likely to cause congestion, traffic complexity, and safety issues. To adapt the increasing demand to the current airspace capacity, a novel macroscopic traffic assignment model is proposed to mitigate the congestion and organize the structure of air traffic flow. Firstly, the UAM corridor is designed and fitted into graph representation. Then, a traffic assignment problem based on linear dynamic system is formalized to minimize the congestion factors and the intrinsic air traffic complexity. A two-step resolution method based on Dafermos’s algorithm is introduced to efficiently solve this optimization problem. A case study is carried out on a two-layer air transport network with intensive UAM operations. The results demonstrate that the proposed model can successfully mitigate urban airspace congestion and organize the UAM traffic into a low-complexity flow pattern. This approach can be used as a tool to assist air navigation service provider in strategic planning for a given transportation network.

Data-Driven Methodology to Support Long-Lasting Logistics and Decision Making for Urban Last-Mile Operations

Last-mile operations in forward and reverse logistics are responsible for a large part of the costs, emissions, and times in supply chains. These operations have increased due to the growth of electronic commerce and direct-to-consumer strategies. We propose a novel data- and model-driven framework to support decision making for urban distribution. The methodology is composed of diverse, hybrid, and complementary techniques integrated by a decision support system. This approach focuses on key elements of megacities such as socio-demographic diversity, portfolio mix, logistics fragmentation, high congestion factors, and dense commercial areas. The methodological framework will allow decision makers to create early warning systems and, with the implementation of optimization, machine learning, and simulation models together, make the best utilization of resources. The advantages of the system include flexibility in decision making, social welfare, increased productivity, and reductions in cost and environmental impacts. A real-world illustrative example is presented under conditions in one of the most congested cities: the megacity of Bogota, Colombia. Data come from a retail organization operating in the city. A network of stakeholders is analyzed to understand the complex urban distribution. The execution of the methodology was capable of solving a complex problem reducing the number of vehicles utilized, increasing the resource capacity utilization, and reducing the cost of operations of the fleet, meeting all constraints. These constraints included the window of operations and accomplishing the total number of deliveries. Furthermore, the methodology could accomplish the learning function using deep reinforcement learning in reasonable computational times. This preliminary analysis shows the potential benefits, especially in understudied metropolitan areas from emerging markets, supporting a more effective delivery process, and encouraging proactive, dynamic decision making during the execution stage.

Status and influential factors of water-borne diseases in bathing beaches in three cities of China from 2019 to 2020

To understand the health status and influencing factors of people in bathing beach after bathing. A questionnaire survey was conducted among the beach tourists in Hebei, Shandong and Shanghai Provinces from May to September, 2019-2020, including personal basic information, seawater/beach exposure in the last 7 days, beach activities, personal protective measures, physical health, related symptoms or diseases after bathing, etc. The seawater samples and sand of the three bathing beaches were sampled and detected. A total of 1222 valid questionnaires were collected. Skin infection(26. 19%), nasal congestion(12. 36%) and eye infection(8. 18%) were the most common symptoms of the tourist after seawater bath. Multivariate logistic regression analysis showed that previous physical discomfort(OR=0. 08-140. 73, 95%CI 0. 04-443. 64) was the common factor of all symptoms(P& lt; 0. 05), the risk factors of stomach cramps, eye infection, nasal congestion and sore throat were no wear of turbinate(OR=4. 65, 95% CI 1. 53-14. 08) and goggles(OR=541. 52, 95% CI 121. 58-2411. 85), swallowing seawater(OR=2. 29-79. 78, 95%CI 19. 83-296. 78) respectively(P& lt; 0. 05). Personal protective measures and physical conditions affect people& apos; s symptoms and diseases after bathing. There is microbial pollution in beach water and sand.

The allocation scheme of distribution network’s emergency service stations based on feeder fault prediction results

In order to solve the problems of low efficiency and long time consuming to determine the location scattering of distribution network emergency service stations on account of geometric shortest path method, this paper proposes an optimal allocation scheme of distribution network emergency service stations based on feeder fault prediction results. In this method, the weight of feeder fault level is fully considered by constructing the optimization model of emergency service station in accordance with the prediction results of feeder fault, and the process parameters such as path congestion factor, path obstacle factor, feeder bending and twisting factor, weather influence factor and average speed of rush repair vehicles are applied to the location optimization model of emergency service station. Compared with the original method, this model can obtain the total time which significantly shortens the road time of rush repair, and leaves as much free time as possible for the subsequent fault repair stage.

Modified quality video: transmission control protocol (TCP) friendly for controlling a congestion

Real-time data transmission through telepresence surgery consideration as an important issue due to using low video quality transmission techniques for transferring video data. Many types of research have been conducted on real-time video transmission but very few have been focused on surgical telepresence and improving video quality during the surgery. The main aim of this research is used to improve the video quality by improving the path quality which the video data is transferred. The proposed system consists of a Transmission Control Protocol (TCP) Friendly for controlling congestion while selecting the best quality of path for transmitting the video data. It selects the best quality of path that is less likely to be affected by network impairments and provides a higher priority to the TCP friendly packets than the non-TCP friendly packets. Results for the end video quality has been improved by 4.2 dB compared to the state of art based on path quality, PSNR and Packet Traffic are based on congestion and also the processing time has been improved by 9 ~ 13 Frames per Second on average against 5 ~ 9 Frames per Second of the state of art. The proposed system makes sure that the packets are sent through the best path and that is done by giving the TCP friendly packets higher priority than non-TCP friendly packets, also considers the available bandwidth, loss rates and other parameters that are utilized in other systems. In addition to the state of art, it adds congestion factor and makes sure that only the path with minimum distortion was used to send maximum data so that the receiver site can handle the transmitted data.

Analysis delay factor distribution of ready–mix concrete PT. Kreasibeton Nusapersada

PT Kreasibeton Nusapersada must be able to provide the best service to meet customer needs, one of which is in terms of timely delivery of goods safely to the destination. The process of sending goods with different locations still often occurs, such as delays in shipping goods caused by various factors to find the cause of the delay in the shipment of Ready-mix on PT Kreasibeton Nusapersada then the data analysis technique used the writer is a fishbone diagram or a causal diagram. From the analysis of activities carried out by PT Kreasibeton Nusapersada, it has been going well. However, based on data in the field, it turns out, there is still a delay. Of the twelve (12) factors, it turns out, there are four (4) factors that cause delays, 84.37% of congestion factor is stating that congestion are the most cause of late distribution of Ready-mix Concrete products, 68.75% is state the number of projects or Demand for Ready-mix Concrete causes the product distribution constraints, 43.75% the Batching Plant machine was damaged so that the process of making Ready-mix Concrete was disturbed, and for product delivery was delayed and 75% is state the availability of raw materials affected the distribution delay.

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

Background: The Load Balancing (LB) schemes in cloud computing consider both current and future utilization of resources to decide the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). But, the possibility of network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, a less-than-optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Objective: To enhance the efficiency of LB and reduce the possibility of congestion occurrence during VM migration in cloud data centers. Methods: Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction with Multipath Traffic Routing (OH-BAC-FUP-MTR) mechanism are presented in this article to achieve the above objective. Initially, the OH-BAC-FUP mechanism is performed to decide the most suitable VMs to be migrated to the most suitable PMs. During VM migration, if any congestion exists due to high bandwidth use or traffic flows, then the MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the highest traffic on the path is applied as a congestion factor. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes with no consideration of the past use of the paths. Findings: The simulation outcomes demonstrate the OH-BAC-FUP-MTR mechanism consumes 8.74% of overall energy, 6.8% of Service Level Agreement violation Time per Active Host (SLATAH), 27.63% of Performance Degradation due to Migration (PDM), 22.98% of SLA Violation (SLAV), 27.27% of VM migrations and 30.77% of hosts shutdown compared to the OH-BAC-FUP using Linear Regression (LR) and Optimal Piecewise LR (OPLR). Keywords: Cloud computing; load balancing; VM migration; OH-BAC-FUP; multipath traffic routing

Why are we still queuing? Exploring landside congestion factors in Australian bulk cargo port terminals

This empirical research improves the understanding of landside congestion factors in bulk cargo port terminals. It presents a model for identifying factors and interactions contributing to congestion severity.Ports play critical roles at the intersection of multiple independent supply chains and have an enormous potential to improve supply chains' competitiveness. However, this point of intersection for supply chains also creates significant coordination challenges best exemplified by the presence of landside congestion. Although landside congestion is an issue that plagues many ports and terminals, research on congestion remains limited, especially in bulk cargo port terminals. This lack of research may also partially explain the dominance of market-based mechanisms for managing congestion. Importantly, market-based approaches often fail or shift congestion to other parts of the supply chain because they are not aligned with the causal factors and their interactions that contribute to congestion.This paper analyses three case studies. Each case centres on an Australian bulk cargo port terminal for forest products and its associated landside supply chain. The research findings identify social, technical and behavioral factors and their different types of interactions at the terminal and related supply chains and reveal how these factors contribute to the appearance and severity of landside congestion. Based on these findings, the paper presents a new definition of landside congestion and a model for identifying and understanding interactions among congestion factors. Improved knowledge of landside congestion can refine congestion management through contextually tailored solutions depending on the factors’ presence and interactions.

Optimizing Vertical Link Placement and Congestion Aware Dynamic Elevator Assignment for Partially Connected 3D-NoCs

The fully connected 3D-NoCs in which all routers are vertically connected with their neighbors above and below need a lot of Through-Silicon-Vias (TSVs), and they will occupy a large silicon area and reduce the fabrication yield. Thus, the idea of partially connected 3D-NoCs has emerged. The optimal number and placement of the vertical links (elevators) must be determined at the chip design stage, which is a multiobjective optimization problem of the performance and the cost. However, optimizing the static elevator placement needs a great amount of calculation and we can not examine all possible solutions at design time. Therefore, we propose a hybrid heuristic strategy for the static elevator placement and assignment, in which the genetic algorithm and the tabu search are combined. The dynamic assignment method is essential for the partially connected 3D-NoCs, and it leads to different traffic distributions and therefore has a huge impact on performance. Many previous static assignment methods can not dynamically change the elevator assignment according to the real-time states of the network, thus it may lead to network congestion. A congestion-aware dynamic assignment (CDA) scheme is proposed in this article, which considers the impact of the distance factor and the congestion factor on the network performance. Experiments show that the proposed CDA method can improve the network performance by 67%-86% compared with the random selection algorithm and can improve the reliability of the partially connected 3D-NoC as well. The key component for the CDA method, the path selection module (PSM), is implemented in FPGA, and the results show that its area cost is negligible compared with a router.

Determination of Exponential Congestion Factor of Road Traffic Flow Caused By Irregular Occurrences

The present paper deals exponential congestion model of road traffic flow caused by irregular occurrences. Congestion that is happened by unpredictable events, for example, auto collisions, handicapped vehicles, climate conditions, over burdens and unsafe materials of vehicles. On account of these sorts of sudden occasions, the travel times taken on the roadways are questionable. We established the steady state conditions based on number of vehicles on road links. The large c values of those links, M/M/1 queues model under the batch service interruptions may be used. The formulation and assumptions of the proposed models have been developed. The exponential congestion factor (ECF) models based on M/MSP/C queuing have been presented. Finally, the numerical examples have also been discussed.

Ridesourcing Behavior Analysis and Prediction: A Network Perspective

This paper investigates the spatiotemporal characteristics and predictability of the emerging modern traffic behavior, ridesourcing. We collect a comprehensive data set of Didi ridesourcing cars on a large geographical scale of a capital city in China, including both the temporal order information and the GPS-recorded spatial trajectories. To extract the features of this kind of traffic behavior, we construct a large-scale network by considering every traffic flow of the orders. Therein, a driver consecutively visiting different regions of the city connects the relationship of these sites. The weighted ridesourcing network shows a consistency of the distribution of trip orders and the Clark model for population distribution. The network also has spatial and temporal features with power laws, sometimes with exponential truncations and log-normal distributions. Furthermore, we propose a general analytical method to quantify the predictability of this kind of behavior by calculating the entropy at a collective level, which can be extended to quantify other traffic behaviors. Finally, by considering the traffic congestion factor, we propose a better neural network based model for predicting dwelling time of the ridesourcing behavior. We suggest that the traffic behavior of ridesourcing cars indicates specific non-Markovian characteristics, which can be systematically analyzed from the viewpoint of network sciences.

Improving acceptance rate of QoS-guaranteed point-to-multipoint traffic flows in elastic optical networks

Abstract This paper studies the load-balanced routing and admission control for point-to-multipoint traffic flows in elastic optical networks (EONs). EONs have emerged as a promising technology to provide high capacity for next-generation networks. Elastic bandwidth allocation promotes the spectrum utilization efficiency. We consider the problem of constructing bandwidth-guaranteed trees for on-demand point-to-multipoint session requests. A request is accepted, if a multicast tree with sufficient bandwidth on each link can be established. The main objective is to increase the multicast acceptance rate in the network. For this purpose, we propose an efficient scheme, namely optical load-balanced multicast routing with admission control (OLMR-AC), to improve the wavelength resources utilization in the network. As an advantage, OLMR-AC jointly considers both distance and congestion factors to create a multicast session. For each admitted session, a bandwidth–guaranteed steiner tree is provided in which not only the source node has a minimum-cost path to each receiver, but also its overall cost is minimal. The proposed scheme distributes the on-demand traffic flows over the available links fairly. Simulation results show the efficiency of the OLMR-AC algorithm in terms of multicast acceptance rate, average length of lightpaths, throughput, number of transmissions, and load variance.

Research Traffic Congestion in Malta: An Analysis of the Attributes, Attitudes and Solutions

Congestion in Malta is now reaching epidemic levels, driven by the fact that the number of cars being registered on a daily basis has increased significantly, and there is no view that this scenario will attenuate. Due to our limited geographical landmass, and that fact that the population and the ratio of cars per person are both increasing, traffic congestion has become acute. It is therefore important to understand the underlying factors that are creating this phenomenon. Congestion is not a problem that can be attributed to Malta only, as many countries battle this issue on a daily basis. Understanding what causes congestion, what the solutions are, the relationship between said factors and congestion, as well as understanding which solutions could garner public support, are the underlying research aims of this study. The data for this study was gathered through a survey containing Likert scale responses (for both dependent and independent variables) to facilitate its quantitative analysis. In order to satisfy the objectives of this research, the strength or lack of correlation between these variables was tested to ensure that any conclusion derived is sound and statistically proven. The multivariate analysis comprises a Factor Analysis that identifies 4 key components, followed by a Linear Regression Analysis to test the correlation between these new composite factors and congestion factors. The results show a robust correlation which identifies political responsibility as the main driver of this societal daily wait in traffic until a destination is reached. These findings confirm the present situation Malta finds itself in and, from a solution point of view, there is no doubt that respondents want the government to make bold steps forward. However, due to the lack of support for any fiscal disincentive as a solution, no government has yet taken the leap of faith to promote a real solution beyond widening roads, commissioning surveys and acknowledging that there is a serious problem that requires a long-term solution.

Research of Wireless Congestion Control Algorithm Based on EKF

The random variation of bandwidth in wireless networks causes some significant challenges to the congestion control protocols based on bandwidth estimation. In this paper, a wireless congestion control scheme based on extended Kalman filtering and bandwidth (CSEKB) is proposed. The CSEKB can effectively perceive the bandwidth oscillation of wireless networks and distinguish the type of packet loss by establishing a noise perception factor. According to the congestion factor, the congestion control parameters are adjusted to correspondingly improve the performance of the wireless network. Moreover, the variation trend of the size of the congestion window presents a law of similar normal distribution curve, which has a certain degree of local symmetry. The CSEKB was implemented in Network simulator 3 (NS3) and compared with TCP Westwood (TCPW), CUBIC, and extended Kalman filtering-based bandwidth estimation (EBE). Through extensive simulation studies, the proposed CSEKB demonstrated the significant performance in wireless networks. First, the CSEKB can achieve congestion control based on the accurate prediction of available bandwidth, and improve average throughput and link utilization. In addition, the CSEKB has good fairness and friendliness compared with several other well-known congestion control methods.

ANALISIS FAKTOR KEMACETAN TERHADAP KINERJA LALU LINTAS DI RUAS JALAN JENDERAL BASUKI RAHMAT (STUDI KASUS: MALL BASSURA)

Commercial buildings in the Bassura Mall area (Jalan Jenderal Basuki Rahmat) have resulted in congestion in the area. Vehicles use road bodies to park their vehicles, sidewalks that are used as trading places by street vendors, and the position of the entrance / exit of Bassura Mall for four-wheeled vehicles that is not strategic. These things have caused the author to choose a location in the Bassura Mall area (Jalan Jenderal Basuki Rahmat), East Jakarta which is a local route, where East Jakarta as one of the big cities in the Special Capital Region of Jakarta also experiences high vehicle growth, p. This can be seen on roads in East Jakarta which often experience congestion during rush hour. The purpose of this research is to determine the traffic performance in the area. The method used in this research is direct observation method at the research location and field data collection in the form of parking volume data, pedestrian volume, vehicle volume entering / exiting Mall Bassura, traffic volume, and road performance. Observations were made on weekdays and holidays. The results of the analysis of the performance of the road sections show that the traffic volume on the roads around Mall Bassura is 32,433 SMP / hour on weekdays and 22,577 SMP / hour on holidays, with the service level index being in the F category on weekdays and holidays. Thus, the congestion factors mentioned above have resulted in a decrease in road capacity which has an impact on road performance.

ANALISIS PEMILIHAN FAKTOR KESESAKAN TRAK DI DEPOH LUAR PELABUHAN MALAYSIA: PENDEKATAN PROSES HIRAKI ANALITIK

The growth of container handling in the maritime industry has led to rapid growth in the transport services sector. Transportation is one of the most important logistics activities for the movement of goods if needed by the customer. However, the encouraging growth of container volume has caused congestion and adversely affected truck access to the container terminal or storage area. Therefore, this study aims to determine the ranking of the congestion factors, as well as the stakeholders that mostly influence this issue through the Analytic Hierarchical Process (AHP) technique. In the AHP analysis, the shipping line (43%) represented the highest percentage which affected the congestion problems, and operational handling (34%) was the main contributor among the five factors. Finally, future studies can be carried out by taking into account the factors and stakeholders involved to minimize congestion problems.

Optimization of Multi-Intersection Traffic Signal Timing Model Based on Improved Q-Learning

With the economic development and the increase of car ownership, major cities in China are facing plenty of problems, such as traffic congestion and environmental pollution caused by traffic congestion. The effective solution to solve this kind of problem is to adopt intelligent control to the traffic signal timing scheme. Based on traditional Q-learning, this paper adds the influence of phase difference and congestion factors, and uses the traffic condition data of adjacent intersections and this intersection to predict the next state, so as to adjust the timing scheme of signal lights at each intersection in the road network. The VISSIM simulation experiments show that this method can effectively reduce the average vehicle delay time and the average number of parking times, alleviate traffic congestion, and improve the traffic efficiency in the overall road network.

The network patterns characteristics research as one of the factors of traffic congestion

The article presents the planning structures analysis method of urbanized areas base on the geometric indicators of graph theory. For research, we developed a software package written in the Python programming language. The methodology of the analysis consists of identifying areas with a planning structure of a certain type in urban area and evaluating the selected area according to several calculated indicators. According to the results of the study, hypotheses were made about the impact of the type of planning structure on the level of traffic load of the city’s road network.