Consistency Between Convergence of Dynamic Assignment and Stochasticity of Microsimulation: Implication for Number of Runs

2017 ◽  
Vol 2667 (1) ◽  
pp. 88-95
Author(s):  
Neeraj Saxena ◽  
Vinayak V. Dixit ◽  
S. Travis Waller
Keyword(s):  
Network Performance ◽  
Dynamic Network ◽  
Computation Time ◽  
User Equilibrium ◽  
Medium Size ◽  
Modeling Framework ◽  
Network Characteristics ◽  
Dynamic Assignment ◽  
Minimum Number ◽  
Valid Estimate

Dynamic transportation models route vehicles by using the principles of dynamic user equilibrium. These models include a dynamic network loading (DNL) module that is used to evaluate link costs. However, an element of stochasticity creeps into the modeling framework when the analytical dynamic assignment (DA) procedure is used along with a stochastic microscopic DNL. A methodologically correct way of approaching this problem is by solving the entire DA with a microscopic DNL (DA-microDNL) model until convergence for a given random seed and then repeating the process with different seed values. This paper proposes an approach to determine the minimum number of replications of the DA-microDNL model to determine a statistically valid estimate of the measure of effectiveness (MOE). The approach was tested on a small and medium-size network having different demand and network characteristics. Results show that running the integrated DA-microDNL framework for a minimum number of replications provides a statistically significant MOE at much lower computation time. The consistent estimates obtained by using this approach would provide robust information to transportation planners and practitioners in evaluating the impacts of several policy decisions on network performance.

scholarly journals Cordon- or Link-Based Pricing: Environment-Oriented Toll Design Models Development and Application

2019 ◽  
Vol 11 (1) ◽  
pp. 258 ◽  
Author(s):  
Xijie Li ◽  
Ying Lv ◽  
Wei Sun ◽  
Li Zhou
Keyword(s):  
Road Network ◽  
Network Performance ◽  
Evaluation Method ◽  
User Equilibrium ◽  
Mixed Integer ◽  
Emissions Reduction ◽  
Pricing Models ◽  
Modeling Framework ◽  
Design Models ◽  
Congestion Mitigation

This study focuses on an environment-friendly toll design problem, where an acceptable road network performance is promised. First, a Traffic Performance Index (TPI)-based evaluation method is developed to help identify the optimal congestion level and the management target of a transportation system. Second, environment-oriented cordon- and link-based road toll design models are respectively proposed through the use of bi-level programming. Both upper-level submodel objectives are to minimize gross revenue (the total collected toll minus the emissions treatment cost) under different pricing strategies. Both lower-level submodels quantify the user equilibrium (UE) condition under elastic demand. Moreover, the TPI-related constraints for the management requirements of the network performance are incorporated into the bi-level programming modeling framework, which can lead to 0–1 mixed integer bi-level nonlinear programming for toll design problems. Accordingly, a genetic algorithm-based heuristic searching method is proposed for the two pricing models. The proposed cordon- and link-based pricing models were then applied to a real-world road network in Beijing, China. The effects of the toll schemes generated from the two models were compared in terms of emissions reduction and congestion mitigation. In this study, it was indicated that a higher total collected toll may lead to more emissions and related treatment costs. Tradeoffs existed between the toll scheme, emissions reduction, and congestion mitigation.

scholarly journals A Task-driven Grammar Refactoring Algorithm

10.14311/1636 ◽  
2012 ◽  
Vol 52 (5) ◽  
Author(s):  
Ivan Halupka ◽  
Ján Kollár ◽  
Emília Pietriková
Keyword(s):  
Programming Language ◽  
Structural Complexity ◽  
Computation Time ◽  
Medium Size ◽  
Specific Task ◽  
Arbitrary Size ◽  
Domain Specific ◽  
Context Free ◽  
Context Free Grammars

This paper presents our proposal and the implementation of an algorithm for automated refactoring of context-free grammars. Rather than operating under some domain-specific task, in our approach refactoring is perfomed on the basis of a refactoring task defined by its user. The algorithm and the corresponding refactoring system are called mARTINICA. mARTINICA is able to refactor grammars of arbitrary size and structural complexity. However, the computation time needed to perform a refactoring task with the desired outcome is highly dependent on the size of the grammar. Until now, we have successfully performed refactoring tasks on small and medium-size grammars of Pascal-like languages and parts of the Algol-60 programming language grammar. This paper also briefly introduces the reader to processes occurring in grammar refactoring, a method for describing desired properties that a refactored grammar should fulfill, and there is a discussion of the overall significance of grammar refactoring.

scholarly journals The use of Petri nets in the study of networked control systems

2019 ◽  
Author(s):  
S. Yuness ◽  
E.S. Lobusov
Keyword(s):  
Petri Nets ◽  
Control Systems ◽  
Communication Networks ◽  
Information Transfer ◽  
Network Performance ◽  
Networked Control Systems ◽  
Networked Control ◽  
Network Characteristics ◽  
Suspension Control ◽  
The Common

The use of communication networks in control systems has several important advantages, such as the ability of information transfer and remote control of various objects, the possibility of modifications and maintenance. On the other hand, the time between reading measurements from the sensor and sending a control signal to the actuator depends on the network characteristics (topology and routing scheme), and such a time delay can greatly affect the overall network performance. Delays, distortions and loss of transmitted data not only degrade the performance of the network management system, but also destabilize it. The paper considers the use of Petri nets as a method for modeling networked control systems (NCS) on the example of designing an active suspension control system for a car. When modeling, the star and common bus topologies were used, the comparison of which revealed that control systems with the common bus topology function 40% faster than systems with the star topology.

scholarly journals Feature Extraction of Real-World Traces Using K Means Clustering In OppNet

2021 ◽  
Author(s):  
Rahul Johari ◽  
Tanvi Gautam
Keyword(s):  
Network Topology ◽  
Real World ◽  
Network Performance ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Dynamic Network ◽  
Delivery Ratio ◽  
Communication Link ◽  
Alternative Measure ◽  
Intermittent Connectivity

Abstract Natural calamities leave people helpless by arising several situations such as network breakdown, zero communication, intermittent connectivity, dynamic network topology. In such situation an application of dynamic and intermittent routing scheme is essential to make further communication possible during likewise scenarios. An application of TCP/IP becomes futile in mentioned circumstances as it best works for static nodes and pre-defined network topology wherein source and destination nodes are first establishing the communication link with each other. An alternative measure of such hitches is to encounter an application of DTN protocol which possess all characteristics to withstand in such scenarios such as; dynamic network topology, intermittent connectivity, frequent path breaks, store – carry – forward fashion. In this paper we did thorough investigation of forest fire dataset (Uttarakhand) after exploring its implementation in ONE with Epidemic, Prophet, Spray and Wait, HBPR, GAER respectively. An extensive and thorough investigation for real world traces implementation has been done with OppNet routing protocols against mobility models namely; Shortest path map – based, Random Direction, Random Walk, Random Waypoint, Cluster Movement respectively for network performance metrics namely packet delivery ratio, packet overhead ratio and average latency ratio respectively with the application of K means clustering machine learning algorithm. With the help of this analysis, we explore the real-world traces characteristics and study the areas on which network performance can be improved.

Utilizing multi-commodity flow formulations to solve network protection problems

2017 ◽  
Author(s):  
◽  
Gokhan Karakose
Keyword(s):  
Network Performance ◽  
Optimal Allocation ◽  
Valid Inequalities ◽  
User Equilibrium ◽  
Dual Model ◽  
Minimax Optimization ◽  
University Of Missouri ◽  
Commodity Flow ◽  
Network Protection ◽  
The Impact

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The identification of critical network components is of interest to both interdictors wishing to degrade the network's performance, and to defenders aiming to preserve network performance in the face of disruption. This dissertation focuses on methods for identifying critical subsets of nodes and/or arcs to fortify and/or disable for the purpose of network protection. A common link connecting all studies in this dissertation is our incorporation of the multi-commodity flow formulations into larger multi-level (e.g., minimax) optimization models. ... The last study examines network fortification models that are able to differentiate between failures that are random (e.g., caused by nature) and strategic network failures (e.g., caused by terrorist activities) when performing the allocation of protective resources. This distinction cannot be achieved in the models presented previously in this dissertation. The desired properties of such differentiating formulations are derived by specifying a set of priori assumptions. The criticality indexes in these models, which are necessary to assess the impact of a disruption, are pre-computed through the resolution of the multi-commodity based User Equilibrium (UE) traffic assignment model and applied to urban transportation networks. Novel valid inequalities and linearization techniques are applied to the dual version of the nonlinear UE multi-commodity model to improve its computational efficiency. Computational results demonstrate that the reformulated linear dual model is effective to solve large size instances to near-optimality; and that the optimal allocation of resources as identified by a component-based formulation may potentially be suboptimal when a network is at risk of multiple simultaneous failures for both types of disruptions (i.e., nature- and terrorist-based). We also demonstrate that fortification models for component or scenario-based disruptions can provide different resource allocations for both types of disruptions.

Management and culture in successful networks

2019 ◽  
Vol 33 (4) ◽  
pp. 381-400
Author(s):  
Daniela Cristofoli ◽  
Mattia Martini ◽  
Benedetta Trivellato ◽  
Dario Cavenago
Keyword(s):  
Network Management ◽  
Network Performance ◽  
Management Practices ◽  
Qualitative Comparative Analysis ◽  
Critical Factor ◽  
Content Type ◽  
Network Characteristics ◽  
Network Culture ◽  
Managerial Behaviors ◽  
Funded Project

Purpose It is generally recognized that network management is a critical factor for network success. It is also acknowledged that different managerial behaviors are necessary in different network settings. Scholars have explored the relationships between network characteristics and managerial behaviors, but the role of network culture in influencing network managers’ activities remains under-investigated. The purpose of this paper is to address this gap. Design/methodology/approach The analysis is developed through a fuzzy-set qualitative comparative analysis of 18 country-based networks involved in the same EU-funded project. Findings The results shed light on two different combinations of network culture types and management practices simultaneously leading to high network performance. Originality/value The paper confirms the existence of a relationship between network management and certain characteristics of the networks, in particular network culture.

Privacy Implication of Location-Based Service: Multi-Class Stochastic User Equilibrium and Incentive Mechanism

2019 ◽  
Vol 2673 (12) ◽  
pp. 256-265
Author(s):  
Haitao Hu ◽  
Zhanbo Sun ◽  
Runzhe Liu ◽  
Xia Yang
Keyword(s):  
Travel Time ◽  
Incentive Mechanism ◽  
Time Estimation ◽  
User Equilibrium ◽  
Travel Time Estimation ◽  
Stochastic User Equilibrium ◽  
Location Based Service ◽  
Modeling Framework ◽  
Privacy Concerns ◽  
Traffic Guidance

As a tool to assist traffic guidance and improve service quality, location-based service (LBS) platforms such as route navigation apps rely heavily on the collection and analysis of users’ location/trajectory information, which may evoke privacy concerns. Because of such privacy concerns, users may choose not to provide their information. In certain cases, this may lead to the problem of insufficient data for LBS applications (e.g., travel time estimation). To address this issue, the paper develops a modeling framework to quantify the levels of privacy for mixed user groups and proposes an incentive mechanism to encourage users to provide their location/trajectory information. It is assumed that LBS users have smaller travel time perception error but experience some extra privacy costs compared with the non-LBS users. A bi-level optimal incentive model with stochastic user equilibrium and elastic demand is developed to capture the mixed behavior of multi-class network users. The problem is solved using a meta-heuristic approach combined of genetic algorithm, successive average algorithm, and multiple behavior equilibrium assignment algorithm. The results reveal that the modeling framework can capture the mixed behavior of groups with different privacy levels. The proposed incentive mechanism is able to ensure sufficient data, and simultaneously minimize the required incentive.

scholarly journals INTERGENERATIONAL EMPLOYEE NETWORK AGEISM: COLLABORATIVE INTERVENTION TRAINING CREATES ATTITUDE MODIFICATIONS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S726-S726
Author(s):  
Heath D Harllee ◽  
Amanda Noah ◽  
Becky P Knight
Keyword(s):  
Training Program ◽  
Dynamic Network ◽  
Pilot Training ◽  
Medium Size ◽  
Diversity And Inclusion ◽  
Positive Attitudes ◽  
Collaborative Intervention ◽  
Employee Network ◽  
Employee Groups ◽  
Survey Responses

Abstract Lack of positive attitudes towards aging has shown to cause challenges within intergenerational networks in employment situations. These can include job satisfaction, intrinsic motivation to work, and ageism subjectivity as underlying determinants and consequences. The collaborative intervention pilot training program goals were two-fold: 1) To expose and understand ageism as a discriminatory action. 2) To create a more positive social dynamic network in a diverse workplace in regard to general expectations of ageism. Two team-based learning intervention programs were created in order to increase collaborative awareness of ageism and were presented to a medium size intergenerational department staff (N=64) as part of a professional development series on equity, diversity and inclusion. Through three multidimensional self-help training activities, learning was done individually, within similar age employee groups, and within intergenerational employee groups. Participants were able to discuss and express general understandings and expectations of aging and learning tools such as intergenerational reactivity and emotion regulation strategies were presented. Within survey responses at the completion of the trainings, key findings showed that respondents had a better understanding of ageism (76%) and felt better equipped to work within an employment team of diverse ages (71%). Additionally, the subject matter of this pilot training program resulted in re-conceptualized positive aging (61%). Future implications and goals for the program include interventions to further increase positive intergenerational understanding and workplace generational inclusiveness.

OPNET-Based Simulation and Research of Communication Network Performance in Digital Traction Substation

2013 ◽  
Vol 440 ◽  
pp. 283-288
Author(s):  
Geng Ran Liu ◽  
Ying Hong Luo ◽  
Yu Zhou
Keyword(s):  
Communication Network ◽  
High Performance ◽  
Network Performance ◽  
Dynamic Network ◽  
Automation System ◽  
Traction Substation ◽  
Railway Traction ◽  
Time Requirements ◽  
Dynamic Network Modeling ◽  
Process Level

In order to keep pace with the development of IEC61850-based intelligent and digital power grid technology, a high-performance communication network must be configured in the process of electrified railway traction substation digitization to achieve the various functions of the substation automation system (SAS). First of all, the structure of communication network in a digital traction substation and the category of different data flow in it are analyzed in this paper. Secondly, the part of packet end-to-end delay (ETE Delay) is specifically discussed. In the last place, by using OPNET, a dynamic network modeling and simulating software, the networks both in substation level and process level is simulated and researched. The results show that the network built in this article can meet the real-time requirements of IEC61850.

scholarly journals Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Network Performance ◽  
Integrated Approach ◽  
Regularization Technique ◽  
Model Based ◽  
Minimum Number ◽  
Sdr Model

Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.

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