A Framework for Intelligent Decision Support System for Traffic Congestion Management System

Traffic congestion problem is one of the major problems that face many transportation decision makers for urban areas. The problem has many impacts on social, economical and development aspects of urban areas. Hence the solution to this problem is not straight forward. It requires a lot of effort, expertise, time and cost that sometime are not available. Most of the existing transportation planning software, specially the most advanced ones, requires personnel with lots practical transportation planning experience and with high level of education and training. In this paper we propose a comprehensive framework for an Intelligent Decision Support System (IDSS) for Traffic Congestion Management System that utilizes a state of the art transportation network equilibrium modeling and providing an easy to use GIS-based interaction environment. The developed IDSS reduces the dependability on the expertise and level of education of the transportation planners, transportation engineers, or any transportation decision makers.

Application of improved PSO to power transmission congestion management optimization model

The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the adoption of the improved PSO algorithm were solved and the results were analyzed. The results show that it is beneficial to obtaining the optimal solution by increasing the number of particles but that will also increase the operation time. On the aspects of solving continuous differentiable non-linear optimization model with equality and inequality constraints, the optimization result of PSO algorithm is the same as that of the interior point method. Compared with genetic algorithms (GA), PSO algorithm is more effective in the local optimization, and unlike GA, it will not be early maturity. Meanwhile, PSO algorithm is also more effective in the boundary optimization than genetic algorithm.

Cost Control of the Transmission Congestion Management in Electricity Systems Based on Ant Colony Algorithm

This paper investigates the cost control problem of congestion management model in the real-time power systems. An improved optimal congestion cost model is built by introducing the congestion factor in dealing with the cases: opening the generator side and load side simultaneously. The problem of real-time congestion management is transformed to a nonlinear programming problem. While the transmission congestion is maximum, the adjustment cost is minimum based on the ant colony algorithm, and the global optimal solu-tion is obtained. Simulation results show that the improved optimal model can obviously reduce the adjust-ment cost and the designed algorithm is safe and easy to implement.

Congestion Management Strategies of Real-Time Market

The high penetration of distributed energy resources (DERs) will significantly challenge the power system operation and control due to their stochastic, intermittent, and fluctuation characteristics. This enhances the difficulty of congestion management of power systems in cross-border electricity market among different regions. For handling this, the Real-Time Market is proposed for balancing capacity trading against congestions. Several strategies for Real-Time Market dealing with congestions are proposed. The strategy of two-stage crossborder markets in Day-ahead, Intra-day and Real Time Market are introduced with the congestion constraints complied. Pre-Contingency strategy is proposed as the advance preparation for the future congestion, and In-Day redispatch is used for regulation. Accordingly, the requirements on facilities considering telemetry and remote control in a fast manner are discussed at last.

COCM: Class Based Optimized Congestion Management Protocol for Healthcare Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes which can be used in many new emerging applications like healthcare. One of the major challenges in healthcare environments is to manage congestion, because in applications, such as medical emergencies or patients remote monitoring, transmitted data is important and critical. So it is essential in the first place to avoid congestion as much as possible and in cases when congestion avoidance is not possible, to control the congestion. In this paper, a class based congestion management protocol has been proposed for healthcare applications. We distinguish between sensitive, non-sensitive and control traffics, and service the input traffics based on their priority and quality of service requirements (QoS). The proposed protocol which is called COCM avoids congestion in the first step using multipath routing. The proposed AQM algorithm uses separate virtual queue's condition on a single physical queue to accept or drop the incoming packets. In cases where input traffic rate increases and congestion cannot be avoided, it mitigates congestion by using an optimized congestion control algorithm. This paper deals with parameters like end to end delay, packet loss, energy consumption, lifetime and fairness which are important in healthcare applications. The performance of COCM was evaluated using the OPNET simulator. Simulation results indicated that COCM achieves its goals.

LMP Congestion Management Using Enhanced STF-LODF in Deregulated Power System

Nowadays electricity market industry is become a major impact on power system for privatization and deregulation of power in global wise. As per the limitation of the transmission system, the complexity arises and the supply fact will be with demand at the time of balancing. The congestion of the power system is occurring based on the transmission limits of the power desire and amount for operating the system. In order to avoid transmission line congestion, enhanced STF-LODF method is proposed. It shows the regulated line transmission flow with generating units by implementing renewable energy resources (RER) based on enhanced STF-LODF in power systems. It avoids the congestion of transmission line frequently in the power system and manages price based on pricing and sensitivity approach, and also manages optimal location of congestion transmission and instability issues of voltage. The congestion management of Locational Marginal Pricing (LMP) is performed with minimum line loss, less cost, line flow, better sensitivity and better performances in optimal power flow and control flow. The efficiency of the proposed power system is analyzed and verified by the simulation results of tested IEEE 14 bus system.

An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real Power Generation Rescheduling

In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a hybrid optimization based on two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

Active Queue Management技术的研究与发展

Active Queue Management(AQM)技术通过有效控制输出队列的丢包时间和丢包方式,对拥塞进行早期通告,这在TCP拥塞控制的实现中至关重要。目前对AQM进行较全面介绍和总结的文献尚不多见,以RandomEarly Detection(RED)为重点介绍了这种第一代AQM技术的设计思想、优缺点以及为此出现的多种RED变种方法,另外还简单介绍了其它几种与RED设计思路不同的AQM方法,以期对AQM技术的研究和发展进行较全面的总结,并促进国内学者以及设备制造商对这一技术的关注。

Active Queue Management Exploiting the Rate Information in TCP-IP Networks

In this paper, we propose a new mechanism called explicit rate notification(ERN) to be used in end-to-end communications. The ERN scheme encodes in the header of transmission control protocol(TCP) packets information about the sending rate and the round trip time(RTT) of the flows. This new available information to the intermediate nodes(routers) is used to improve fairness, increase utilization, decrease the number of drops, and minimize queueing delays. Thus, it induces a better management of the queue. A comparison of our scheme with preexistent schemes, like the explicit congestion notification scheme, shows the effectiveness of the proposed mechanism.

A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS

Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the pre- dominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theo- retical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.

A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technolo-gies can be used to facilitate traffic incident management.If an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work.Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled.Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is delivered.The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

Congestion control algorithm in large-delay uncertain networks

Based on Smith-fuzzy controller, a new active queue management （AQM） algorithm adaptable to the large-delay uncertain networks is presented. It can compensate the negative impact on the queue stability caused by the large delay, and it also maintains strong robustness under the condition of dynamic network fluid. Its stability is proven through Lyapunov method. Simulation results demonstrated that this method enables the queue length to converge at a preset value quickly and keeps the queue oscillation small, the simulation results also show that the scheme is very robust to disturbance under various network conditions and large delay and, in particular, the algorithm proposed outperforms the conventional PI control and fuzzy control when the network parameters and network delay change.

Emission Patterns under Alternative Congestion and Motor Vehicle Pollution Mitigation Policies in Shanghai

As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.

Management of acute heart failure-Is there a paradigm shift around the corner?

It has become increasingly apparent that the looming epidemic of heart failure calls for systematic treatment approaches tailored to the needs of individual patient phenotypes. Although chronic heart failure (CHF) therapies are continuously evolving based on the increasing understanding of the involved etiology, acute heart failure (AHF) therapies are still based on hemodynamic improvements and symptom alleviation. Guidelines on AHF management have highlighted that the currently administered AHF therapies lack evidence and have raised concerns on the safety and efficacy of some of the hitherto accepted treatment modalities. Additionally, the high mortality and morbidity rates associated with the current AHF therapies also add to the imperative need to revisit AHF management. The last decade has witnessed a paradigm shift in the way we define and diagnose AHF. Apart from it being recognized as a distinct clinical entity, research has also led to new data on the pathophysiological changes associated with AHF. These developments along with the limited short- and long-term effects of currently used therapies may herald a paradigm shift in the way we plan and deliver management strategies to treat the pathological progression of heart failure.

An Active Queue Management Algorithm: CRED

By applying the method of average and variance, a new queue management algorithm named the Classified-Random Early Detection （CRED） algorithm is presented which can identify the media streaming, TCP traffic and other UDP traffic at the edge routers. The algorithm discriminates the slow start and the congestion control phase of the TCP traffic and combines the TCP congestion control with the IP congestion control to alleviate the congestion effectively. Simulation shows that CRED can not only make the media streaming obtain the resources needed but also protect the TCP traffic transmitted effectively and reliably.

A Transmission Congestion Cost Allocation Method in Bilateral Trading Electricity Market

Bilateral trading in electricity is one of the typical deregulated power market trading methods and it has its own way to calculate and allocate congestion cost. In this paper, using as a practical bilateral structure example, the British electricity market is stated in details especially the operation mechanism and the methodology of imbalance settlement. A corresponding congestion cost allocation method for bilateral market is introduced briefly by equations and is simulated in a modified IEEE-14 bus model to investigate its pros and cons.

RED-DTB: A Dual Token Bucket Based Queue Management Algorithm

Improving the Quality of Service (QoS) of Internet traffic is widely recognized as a critical issue for the next-generation networks. In this paper, we present a new algorithm for the active queue management, namely RED-DTB. This buffer control technique is used to enforce approximate fairness among a large number of concurrent Internet flows. Like RED (Random Early Detection) algorithm, the RED-DTB mechanism can be deployed to actively respond to the gateway congestion, keep the gateway in a healthy state, and protect the fragile flows from being stolen bandwidth by greedy ones. The algorithm is based on the so-called Dual Token Bucket (DTB) pattern. That is, on the one hand, every flow is rate-limited by its own token bucket, to ensure that it can not consume more than its fair share of bandwidth; On the other hand, to make some compensations to less aggressive flows, such as connections with larger round trip time or smaller sending window, and to gain a relatively higher system utilization coefficient, all flows, depending on their individual behavior, may have a chance to fetch tokens from the public token bucket when they run out of their own share of tokens. The algorithm is analyzed and evaluated by simulations, and is proved to be effective in protecting the gateway buffer and controlling the fair allocation of bandwidth among flows.

Allocation of congestion relief cost based on flow change on the congested facilities

Congestion management in an electricity market is introduced in this paper and a new method of allocating congestion cost to transactions is proposed. The proposed method is a two-step process, in which the total congestion cost is firstly allocated to congested facilities and then to each transaction involved. The cost of relieving a congested facility allocated to each transaction is proportional to the power flow change on the congested facility caused by the transaction. The more the power flow change is on the congested facility caused by the transaction, the deeper the degree of involvement by the transaction. Therefore, cutting down the magnitudes of such transactions contributes to relieving congestion. Test results on a 5-bus system indicate that the proposed method can reflect reasonably the degree of involvement by each transaction in the congestion and provide correct price signals contributing to relieving congestion.