Network Operation Performance Research Articles

Node Importance Evaluation of Urban Rail Transit Based on Signaling System Failure: A Case Study of the Nanjing Metro

Assessing the importance of nodes in urban rail transit systems helps enhance their ability to respond to emergencies and improve reliability in view of the fact that most of the existing methods for evaluating the importance of rail transit nodes ignore the disturbance effect of signaling system failures and are unable to objectively identify critical stations in specific disturbance scenarios. Therefore, this paper proposed a method for evaluating the importance of urban rail transit nodes in signaling system failure scenarios. The method was based on the research background of the signaling system failure that occurs most frequently and analyzed the network failure mechanism after the occurrence of a disturbance. The node importance evaluation indices were selected from the network topology and network operation performance in two aspects. The variation coefficient–VIKOR method was employed to comprehensively assess the significance of urban rail transit stations during signaling system failures. The Nanjing Metro network was also used as an example to evaluate the importance of network stations. The results showed that under the attack method of signaling system failure, most ECC and interlocking stations experienced significantly higher network performance losses compared to the original attack method, and a few interchange stations showed smaller performance losses. The critical stations identified based on the proposed method are mainly distributed in the passenger flow backbone of the Nanjing Metro and were constructed in the early stage; of these, 85% are ECC stations or interlocking stations, which are easily neglected in daily management, in contrast to interchange stations with heavy passenger flow. The results of this study can provide an important reference for the stable operation and sustainable construction of urban rail transit.

A bi-level planning approach of logistics unmanned aerial vehicle route network

The rapid growth of e-commerce in urban areas has put incremental pressure on delivery. Unmanned Aerial Vehicle (UAV) has great potential in the logistics field with the aggravation of ground congestion. This study proposes a logistics UAV route network planning approach based on the bi-level program, which from the perspectives of network managers and users. The upper model focuses on the macro network configuration, while the lower model focuses on the UAV traffic flow distribution. The two-layer Simulated Annealing (SA) is presented to deal with the iterative process of network topology and traffic flow balance allocation. A case study is carried out using a region from Nanjing, to introduce the application of this bi-level approach. The results suggest that there is a positive correlation between the network overall length and the flow entropy, a negative correlation between the network overall length and the nonlinear coefficient. When the maximum transit time of UAV is 3, the comprehensive performance of network operation is optimal. This study provides a new way for logistics UAV network planning in urban areas, which is significant to regulate the operation of large-scale UAVs.

Intelligent-Slicing: An AI-Assisted Network Slicing Framework for 5G-and-Beyond Networks

5G-and-beyond networks are designed to fulfill the communication and computation requirements of various industries, which requires not only transporting the data, but also processing them to meet/address diverse key performance indicators (KPIs). Network Function Virtualization (NFV) has emerged to enable this vision by: (i) collecting the requirements of diverse services, using graphs of Virtual Network Functions (VNFs); and (ii) mapping these requirements into network management decisions. Because of the latter, we need to efficiently allocate computing and network resources to support the desired services, and because of the former such decisions must be jointly optimized considering all KPIs associated with supported services. Thus, this paper proposes an optimized, intelligent network slicing framework to maintain a high performance of network operation by supporting diverse and heterogeneous services, while meeting new KPIs, e.g., reliability, energy consumption, and data quality. Different from the existing works, which are mainly designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, VNF placement and data routing, as well as data compression ratios, from the end users to the cloud. Our results depict the efficiency of the proposed framework in enhancing the network performance when compared to baseline approaches that consider partial network view or fair resource allocation.

A population-based incremental learning algorithm to identify optimal location of left-turn restrictions in urban grid networks

The treatment of left turns at signalized intersections drives the development of signal phasing and timing plans and also plays an important role in overall traffic network operations. Accommodating left turns allows for the most direct routeing but reduces intersection capacity, whereas restricting left turns improves capacity but requires some vehicles to travel longer distances. This paper proposes a population-based incremental learning (PBIL) algorithm to determine at which intersections left-turn restrictions should be enacted to maximize a network's operational performance. The performance of each configuration is tested in a micro-simulation environment on both perfect and imperfect square grid networks. Comparison with a partial enumeration of feasible options reveals that the PBIL algorithm is effective at identifying a near-optimal configuration of left-turn restrictions. The resulting configurations suggest that left turns should be generally restricted at intersections that carry the most flow. These intersections typically occur in the central portion of the network when demands are relatively uniform. Doing so helps to provide additional intersection capacity at the locations where it is most necessary, while minimizing the additional travel distance that is incurred due to detours caused by the left-turn restrictions. These provide insight as to how urban traffic networks might be managed to improve network efficiency by only enacting left-turn restrictions at a subset of locations.

Part-time shoulder use operational impact on the safety performance of interstate 476

Objective With every change in a roadway to improve the network operational performance, the roadway safety behavior is impacted. Part-time shoulder use (PTSU) is one strategy that significantly impacts the operational performance of a roadway for several hours each workday. This study evaluates the impact of PTSU on safety behavior and the possibility of crash prevention. Method Current predictive safety measures do not account for the part-time dimensional changes of PTSU effectively. Using Vissim-based microsimulation trajectory data in the Surrogate Safety Assessment Model (SSAM) program, developed by the Federal Highway Administration, this study gauged the safety disposition of Interstate 476 (I-476) in southeast Pennsylvania (PA). The two key safety evaluation factors include the number of conflicts and the time-to-collision (TTC) of each conflict. The number of conflicts represents unsafe scenarios that could become crashes if neither possibly impacted vehicles adjusts their route. TTC is the minimum time for the conflict to become a crash if either vehicle’s route was not adjusted to avoid the collision. Both factors were averaged across six 20-min periods during the 2-h commuting period simulated. Results Due to the improved operational performance, the TTC values for the PTSU scenarios decreased, reducing safety; however, the number of conflicts occurring reduced significantly, counterbalancing the reduced safety from the TTC reduction. Conclusions PTSU has great operational improvement effects which create a faster-moving transportation network. The higher speeds caused by the enhanced operations increases the risk of a crash during unsafe situations, but the number of unsafe situations occurring will significantly reduce. The reduced number of unsafe situations indicates an increased probability of crash avoidance but does not indicate a definitive reduction in crashes.

Network-Level Coordinated Speed Optimization and Traffic Light Control for Connected and Automated Vehicles

This study develops a methodology for coordinated speed optimization and traffic light control in urban street networks. We assume that all vehicles are connected and automated. The signal controllers collect vehicle data through vehicle to infrastructure communications and find optimal signal timing parameters and vehicle speeds to maximize network throughput while harmonizing speeds. Connected and automated vehicles receive these dynamically assigned speeds, accept them, and implement them. The problem is formulated as a mixed-integer non-linear program and accounts for the trade-offs between maximizing the network throughput and minimizing speed variations in the network to improve the network operational performance and at the same time smoothen the traffic flow by harmonizing the speed and reducing the number of stops at signalized intersections. A distributed optimization scheme is developed to reduce the computational complexity of the proposed program, and effective coordination ensures near-optimality of the solutions. The case study results show that the proposed algorithm works in real-time and provides near-optimal solutions with a maximum optimality gap of 5.4%. The proposed algorithm is implemented in Vissim. The results show that coordinated signal timing and speed optimization improved network performance in comparison with cases that either signal timing parameters or average speed of vehicles are optimized. The coordinated approach reduced the travel time, average delay, average number of stops, and average delay at stops by 1.9%, 5.3%, 28.5%, and 5.4%, respectively compared to the case that only signal timing parameters are optimized.

Unbalance Mitigation via Phase-Switching Device and Static Var Compensator in Low-Voltage Distribution Network

As rooftop solar PVs installed by residential customers penetrate in low voltage distribution network (LVDN), some issues, e.g. over/under voltage and unbalances, which may undermine the network's operational performance, need to be effectively addressed. To mitigate unbalances in LVDN, dynamic switching devices (PSDs) and static var compensator (SVC) are two equipment that are cost-effective and efficient. However, most existing research on operating PSDs are based on inflexible heuristic algorithms or without considering the network formulation, which may lead to strategies that violate operational requirements. Moreover, few pieces of literature have been reported on mitigating unbalances in LVDN via SVC and PSDs together. This paper, after presenting the dispatch model of SVC, formulates the decision-making process as a mixed-integer non-convex programming (MINCP) problem considering all practical operational requirements. To efficiently solve the challenging problem, the MINCP is reformulated as a mixed-integer second order-cone programming (MISOCP) problem based on either exact reformulations or accurate approximations, making it possible to employ efficient off-the-shelf solvers. Simulations based on a modified IEEE system and a practical system in Australia demonstrates the efficiency of the proposed method in mitigating unbalances in LVDN.

Optimal Placement and Optimal Combination of DTC, STATCOM and Line Reconfiguration to Minimize the Power Loss in Distribution Networks

Due to deregulation, exponential growth in the electricity demand, integration of renewable energy sources, lack of analytical computing facility and expansion of network increases the complexity with poor operation of the network. Existing analytical computing facility is failed to give efficient and accurate results for secure operation of the distribution network. Many researchers are working to give potential solution to improve the performance of network operation considering the real time variables. In this paper minimization of power loss is chosen as objective function. Considering the network parameters the optimal placements with different combination of DTC, STATCOM and line reconfiguration are tested on IEEE-15 bus system using MiPower simulation package. The obtained result shows more than 50% power loss reduction, which leads to efficient and stress free operation of the distribution networks.

Failure prediction, detection & recovery algorithms using MCMC in tree-based network topology to improve coverage and connectivity in 3D-UW environment

In an Underwater Wireless Sensor Networks (UW-WSN), one of the most challenging issue is the fault tolerance during data transmission. There are various constraints like lack of power, physical damage, hardware and software problem that leads to failure or blocked sensor node. Due to the faulty sensor node, it is quite difficult to communicate the information in a particular time period. This failure affects the overall network operation performance. In order to improve the coverage, connectivity and network performance, we propose a failure prediction, detection and recovery algorithm using Markov Chain Monte Carlo (MCMC) process. In the failure prediction algorithm, the failures of sensor node are identified by using error pattern of delayed messages and the threshold limit is based on time probability distribution function. In the failure detection algorithm, the faulty sensor node is detected using threshold limit and residual energy. In the recovery algorithm, the faulty sensor node is replaced by the nearest neighbouring sensor node with high energy capacity. Theoretical analysis and experimental simulation results are evaluated based on performance metrics such as Coverage Ratio, Failure Prediction, Network Lifetime and Recovery Policy. The results shows that the proposed 3-D UW-WSN system has a maximum coverage ratio of 23.07%, maximum increase of recovery sensor nodes 30%, maximum decrease of the predictive probability of failure node 11.11% and maximum increase of network lifetime 50%. The simulation results shows better performance and the proposed method is more efficient than the coverage of static and mobile sensor in 2-D UW-WSN algorithms. The proposed coverage of static and mobile sensor in 3-D UW-WSN mechanism performs to improve coverage and connectivity, reduces the predictive probability of failure nodes and increases network lifetime.

Enhancement of DUBAL Network Operational Performance Using Resistive High Temperature Superconducting Fault Current Limiter

Power systems under expansion suffer from escalating fault levels that impact equipment integrity, operational flexibility, and the overall security of the system. The fault current limiter (FCL) is one of approaches used by utilities to limit fault current levels and in Dubai Aluminum (DUBAL) series current limiting reactors are currently used. However, more effective (FCL) topologies are sought and, in this paper, a case study is proposed using resistive high temperature superconducting fault current limiters (HT-SFCLs). The application of HT-SCFLs is aimed here at reducing short-circuit currents, while at the same time enhancing the stability and security of the network. The study involves analysis of three-phase and single-line-to-ground faults, evaluation of the voltage levels and total harmonic distortion (THD) levels at busbars considering different fault scenarios, and demonstrates how the use of HT-SFCLs at various locations improves the plant performance. The ideal HT-SFCL model is adopted for this analysis since the aim is to look at the steady-state performance rather than the transient performance. Comparison with series reactor FCLs which are currently installed in the plant show better performance with the proposed HT-SFCL. Voltage profile values and total harmonic content were also compared with measurement data available at the plant.

Co-simulation of electricity distribution networks and peer to peer energy trading platforms

There has been significant recent interest in local electricity trading platforms, and particularly in the application of distributed ledger and blockchain technology for distributed, or peer-to-peer energy trading in local energy communities. Several projects worldwide have demonstrated this concept on a small scale in Low Voltage (LV) distribution networks and microgrids. However, previous work in this area has not sufficiently addressed the potential impacts of peer-to-peer energy trading and other local electricity trading mechanisms on the control, operation and planning of the electricity distribution networks. Accordingly, this paper presents a methodology for the co-simulation of power distribution networks and local peer-to-peer energy trading platforms. The distribution system simulator is interfaced with a peer-to-peer energy trading platform, which employs a blockchain-based distributed double auction trade mechanism. The presented co-simulation approach is demonstrated using a case study of typical European suburban distribution network. It is demonstrated in the paper that this approach can be used to analyse the impacts of peer-to-peer energy trading on network operational performance. The analysis presented in the paper suggests that a moderate level of peer-to-peer trading does not have significant impacts on network operational performance.

Energy Efficient Clustering of Statistically Distributed Heterogeneous Wireless Sensor Networks for Internet-of-Things

This paper shows the effect of random as well as six statistical distributions on heterogeneous wireless sensor network (WSN) for internet-ofthings (IoTs). The distribution of sensor nodes in a WSN reflects the significant variation occurring in the lifetime performance of network operation. Novel models of heterogeneous clustering permit the sensor nodes to communicate with different devices of the future WSN-IoT systems in a unique fashion. Different statistical distributions viz. Random, Exponential, Chi-Square, Weibull, Gamma, Gaussian and Beta on heterogeneous broad transmission distance protocol (HT-BTDP) are used here for observing the changes in the performance parameters such as network lifetime, residual energy and energy variance. The strategies are validated on the basis of simulation and compared with various distributions on the classical protocols like stable election protocol (SEP) and energy efficient heterogeneous clustered (EEHC) scheme also.

Osservatorio sismico delle strutture: the Italian structural seismic monitoring network

The OSS (acronym of the Italian name “Osservatorio Sismico delle Strutture”) is a network of permanent seismic monitoring systems installed in public buildings, and also in some bridges and a dam. Each system consists of a number of sensors sufficient to completely describe the dynamic behaviour of the structure. Structural typologies have been selected according to their representativeness of the public building stock and to their importance for the emergency management. Some relevant aspects are addressed, including: (i) a brief description of the network, (ii) a resume of data usually collected and organized for each structure (structural survey and in situ tests on materials, experimental modal analysis, FEM models and structural identification) and (iii) a description of data management tools and procedures (network operation performance, post-earthquake automatic analysis, public access to data). At the end, on-going and future use of OSS data is briefly discussed, with reference to statistical analysis of relevant parameters of the available records, real-time post-earthquake damage assessment and an in-field reconnaissance visit after a recent earthquake.

Influence Models of Urban Road Network Operation Performance Base on Spatial Autocorrelation Theory

In order to better identify the spatial influence between adjacent parts of road networks, the paper introduces the spatial autocorrelation theory in evaluating the operation performance of urban road networks. The research proposes several spatial correlation validation indicators to verify the spatial characteristics among the road networks. Based on the analysis of spatial characteristics, the relationship between operation performance and influencing factors under the impact of spatial effect is examined. Furthermore, a spatial autocorrelation based influence models at three traffic flow levels is developed by using the data from a partial urban road network in Beijing. The model analysis shows that the spatial autocorrelation model is more effective in analyzing the urban road network operation performance under the influence of various factors. This model will be beneficial in identifying traffic network problems and improving traffic operations of the urban road network.

New Methodology to Calculate Unblocked Reliability to Assess Road Network Operation Performance

This paper presents a new methodology to calculate unblocked reliability of a road network. Unblocked reliability analysis is used to assess the operation performance of a road network at peak hour in order to support the decision-making involved in road network planning. A four-level model of road network unblocked reliability (link, path, Origin-destination pair and whole network) is shown to assess the operation performance of each level of the road network. A method of sensitivity analysis is provided to determine the priority of the road section in the planning scheme. However, the most difficult task in unblocked reliability analysis is how to find a route choice pattern, namely, which routes are selected and how proportions of Origin-destination flows are loaded on these used routes between every Origin-destination pair. In order to resolve this problem, a new traffic assignment method is proposed in this paper based on the Frank-Wolf algorithm. This algorithm not only can find the link flows but can also solve the route choice pattern, which satisfy the User Equilibrium criterion. The proposed methodology is applied to solve the goal of traffic demand control and the necessary road capacity when a road network is improved to the expected operation performance. The proposed methodology provides a useful tool to assess the operation performance of a road network at several levels of road capacity and traffic demand, and can be used to evaluate various road network planning and to design a reliable road network system.