Estimation method for a skip-stop operation strategy for urban rail transit in China

The skip-stop operation strategy （SOS） is rarely applied to Chinese urban rail transit networks because it is a simple scheme and a less universally popular transportation service. However, the SOS has performance advantages, in that the total trip time can be reduced depending on the number of skipped stations, crowds of passengers can be rapidly evacuated at congested stations in peak periods, and the cost to transit companies is reduced. There is a contradiction between reducing the trip time under the SOS and increasing the passengers＇ waiting times under an all-stop scheme. Given this situation, the three objectives of our study were to minimize the waiting and trip times of all passengers and the travel times of trains. A comprehensive estimation model is presented for the SOS. The mechanism through which the trip time for all passengers is affected by the SOS is analyzed in detail. A 0-I integer programming formulation is established for the three objectives, and is solved using a tabu search algorithm. Finally, an example is presented to demonstrate that the estimation method for the SOS is capable of optimizing the timetable and operation schemes for a Chinese urban rail transit network.

Research and Design of IPv6 Network Management and Operations Support System

IPv6 is the foundation of the development of Next Generation Internet (NGI). An IPv6 network management and operations support system is necessary for real operable NGI. Presently there are no approved standards yet and relevant equipment interfaces are not perfect. A Network Management System (NMS) at the network layer helps implement the integrated management of a network with equipment from multiple vendors, including the network resources and topology, end-to-end network performance, network failures and customer Service Level Agreement (SLA) management. Though the NMS will finally realize pure IPv6 network management, it must be accommodated to the management of relevant IPv4 equipment. Therefore, modularized and layered structure is adopted for the NMS in order to implement its smooth transition.

ZTE Boosts European Presence with New Network Operation Centre in Budapest

8 May 2012, Budapest -- ZTE Corporation announced that Hungarian Minister of Economics, Gy^irgy Matolcsy, and ZTE senior vice president, Zhu Jinyun have signed an agreement to cooperate on the establishment of ZTE＇s new European Regional Network Operation Center in Budapest.

China's Observation Network For Earth's Crust Movement Now Operational

An observation network focusing on earthquakes wascompleted one year aheadof schedule and put into operationrecently. According to scientists, this135-million-yuan (U.S.$16.3million) project could also be usedfor geodetic surveying, ionosphereand sea-level observations,

Development Methodologies for Network Softwarization: A Comparison of DevOps, NetOps, and Verification

This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.

The construction and approximation of some neural networks operators

In this paper, the technique of approximate partition of unity is used to construct a class of neural networks operators with sigmoidal functions. Using the modulus of continuity of function as a metric, the errors of the operators approximating continuous functions defined on a compact interval are estimated. Furthmore, Bochner-Riesz means operators of double Fourier series are used to construct networks operators for approximating bivariate functions, and the errors of approximation by the operators are estimated.

Service Parameter Exposure and Dynamic Service Negotiation in SDN Environments

Software-defined networking （SDN） is a generic term and one of the major interests of the telecoms industry （and beyond） over the past two years. However, defining SDN is a somewhat controversial exercise. The claimed flexibility, as well as other presumed assets of SDN, should be carefully investigated. In particular, the use of SDN to dynamically provision network services suggests the introduction of a certain level of automation in the overall network service delivery process, from service parameter negotiation to delivery and operation. This paper aims to clarify the SDN landscape and focuses on two main aspects of the SDN framework： net- work abstraction, and dynamic parameter exposure and negotiation.

Potential Benefits of Increased Public Transit Ridership in Medium Sized Cities: A Case Study

At the beginning of the twentieth century, the United States was leading in the public transit sector, but following World War II, private automobiles became more affordable and gained popularity. Transportation infrastructure investments that increased road capacity further facilitated the increase in automobile use at the expense of reduced public transit ridership. With the increase of dependency on automobiles and the continuing growth of private automobile ownership and use, various problems became major challenges in big cities of USA. These include traffic congestion, air pollution, road and parking infrastructure costs, energy consumption, traffic safety, fewer mobility options for the non-drivers, and a decline in the image and use of public transit. This study uses a medium sized city, Birmingham as a case study to investigate the potential of public transit to reduce automobile trips and in turn improve the overall performance of the road network by addressing the abovementioned challenges. An agent-based simulation model was developed for the Birmingham metropolitan region using the Multi-agent Transport Simulation (MATSim) platform. Three scenarios were considered with gradually increased transit ridership to identify the benefits of increased public transit. Traffic volume, network average speed, and travel times were used as performance measures for the evaluation of the designated scenarios. Results suggest that modal shifts toward public transit and reduction in travel demand for an automobile can result in improvements in speed and travel time for all users. Therefore, investments for improving transit quality and frequency of service, as well as campaigns to improve the image of public transit and make it a mode of choice for transportation users can increase transit ridership and, in turn, improve network operations, thus are deemed worthy for medium sized cities.

Comparison of Economic Dispatch, OPF and Security Constrained-OPF in Power System Studies RN

Electricity network is a very complex entity that comprises several components like generators, transmission lines, loads among others. As technologies continue to evolve, the complexity of the electricity network has also increased as more devices are being connected to the network. To understand the physical laws governing the operation of the network, techniques such as optimal power flow (OPF), Economic dispatch (ED) and Security constrained optimal power flow (SCOPF) were developed. These techniques have been used extensively in network operation, planning and so on. However, an in-depth presentation showcasing the merits and demerits of these techniques is still lacking in the literature. Hence, this paper intends to fill this gap. In this paper, Economic dispatch, optimal power flow and security-constrained optimal power flow are applied to a 3-bus test system using a linear programming approach. The results of the ED, OPF and SC-OPF are compared and presented.

A Risk-based Competitive Bi-level Framework for Operation of Active Distribution Networks with Networked Microgrids

This paper presents a risk-based competitive bi-level framework for optimal decision-making in energy sales by a distribution company(DISCO)in an active distribution network(ADN).At the upper level of this framework,the DISCO and a rival retailer compete for selling energy.The DISCO intends to maximize its profit in the competitive market.Therefore,it is very important for the DISCO to make a decision and offer an optimal price for attracting customers and winning the competition.Networked microgrids(MGs)at the lower level,as the costumers,intend to purchase energy from less expensive sources in order to minimize costs.There is a bi-level framework with two different targets.The genetic algorithm is used to solve this problem.The DISCO needs to be cautious,so it uses the conditional value at risk(CVaR)to reduce the risk and increase the probability of making the desired profit.The effect of this index on the trade between the two levels is studied.The simulation results show that the proposed method can reduce the cost of MGs as the costumers,and can enable the DISCO as the seller to win the competition with its rivals.

Promoting Metro Operation Safety by Exploring Metro Operation Accident Network

Understanding the causation of accidents is essential to promote metro operation safety.In terms of 243 reported metro operation accident cases in China, a directed weighted network was constructed based on complex network theory, where nodes and directed edges denotes factors and event chains respectively. To reveal the key causal factors, the topological characteristics of metro operation accident network(MOAN) were analyzed from both global and local views. The results show that facility-type factors are more closely related to the occurrence of the accidents from the perspectives of average path length and cascading effects. Accident types like train delay and train suspension are the great risk recipients. Key causal factors with large out-degree, out-strength, betweenness centrality and cluster coefficient, such as communication and signal failure, vehicle failure and piling into the train should be noticed. The research framework proposed in the paper is not only applicable to China’s metro operation system, but also appropriate for other transportation system safety studies.

On Approximation by Neural Networks with Optimized Activation Functions and Fixed Weights

Recently,Li[16]introduced three kinds of single-hidden layer feed-forward neural networks with optimized piecewise linear activation functions and fixed weights,and obtained the upper and lower bound estimations on the approximation accuracy of the FNNs,for continuous function defined on bounded intervals.In the present paper,we point out that there are some errors both in the definitions of the FNNs and in the proof of the upper estimations in[16].By using new methods,we also give right approximation rate estimations of the approximation by Li’s neural networks.

Prediction of voltage distribution using deep learning and identified key smart meter locations

The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.