The effects of transportation network failure on people’s accessibility to hurricane disaster relief goods: a modeling approach and application to a Florida case study

2011 ◽  
Vol 59 (3) ◽  
pp. 1619-1634 ◽  
Author(s):  
Mark W. Horner ◽  
Michael J. Widener
Keyword(s):  
Disaster Relief ◽  
Transportation Network ◽  
Network Failure ◽  
Modeling Approach

scholarly journals Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

2021 ◽  
Vol 11 (8) ◽  
pp. 3487
Author(s):  
Helge Nordal ◽  
Idriss El-Thalji
Keyword(s):  
Simulation Model ◽  
Simulation Modeling ◽  
Study Data ◽  
Opportunistic Maintenance ◽  
Maintenance Strategies ◽  
Modeling Approach ◽  
Intelligent Maintenance ◽  
Diagnosis And Prognosis ◽  
Case Study Data

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

scholarly journals Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1478
Author(s):  
Penugonda Ravikumar ◽  
Palla Likhitha ◽  
Bathala Venus Vikranth Raj ◽  
Rage Uday Kiran ◽  
Yutaka Watanobe ◽  
...  
Keyword(s):  
Real World ◽  
Pattern Mining ◽  
Transportation Network ◽  
Frequent Pattern Mining ◽  
Temporal Databases ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Temporal Database ◽  
First Case

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

scholarly journals Understanding the Usage Patterns of Bicycle-Sharing Systems to Predict Users’ Demand: A Case Study in Wenzhou, China

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaomei Xu ◽  
Zhirui Ye ◽  
Jin Li ◽  
Mingtao Xu
Keyword(s):  
Cluster Analysis ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Transportation Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Demand Prediction ◽  
Usage Patterns ◽  
Accuracy Of Prediction

Bicycle-sharing systems (BSSs) have become a prominent feature of the transportation network in many cities. Along with the boom of BSSs, cities face the challenge of bicycle unavailability and dock shortages. It is essential to conduct rebalancing operations, the success of which largely depend on users’ demand prediction. The objective of this study is to develop users’ demand prediction models based on the rental data, which will serve rebalancing operations. First, methods to collect and process the relevant data are presented. Bicycle usage patterns are then examined from both trip-based aspect and station-based aspect to provide some guidance for users’ demand prediction. After that, the methodology combining cluster analysis, a back-propagation neural network (BPNN), and comparative analysis is proposed to predict users’ demand. Cluster analysis is used to identify different service types of stations, the BPNN method is utilized to establish the demand prediction models for different service types of stations, and comparative analysis is employed to determine if the accuracy of the prediction models is improved by making a distinction among stations and working/nonworking days. Finally, a case study is conducted to evaluate the performance of the proposed methodology. Results indicate that making a distinction among stations and working/nonworking days when predicting users’ demand can improve the accuracy of prediction models.

scholarly journals URBAN MODELLING WITH TYPOLOGICAL APPROACH. CASE STUDY: MERIDA, YUCATAN, MEXICO

2017 ◽  
Vol XLII-2/W5 ◽  
pp. 617-620
Author(s):  
A. Rodriguez
Keyword(s):  
3D Modeling ◽  
Three Dimensional ◽  
Historical Reconstruction ◽  
Colonial Era ◽  
Common Features ◽  
Modeling Approach ◽  
Urban Modelling ◽  
Typological Approach ◽  
Three Dimensional Models

In three-dimensional models of urban historical reconstruction, missed contextual architecture faces difficulties because it does not have much written references in contrast to the most important monuments. This is the case of Merida, Yucatan, Mexico during the Colonial Era (1542-1810), which has lost much of its heritage. An alternative to offer a hypothetical view of these elements is a typological - parametric definition that allows a 3D modeling approach to the most common features of this heritage evidence.

scholarly journals Citizen Participation in Responding to Natural Disasters Through Twitter Messages

2020 ◽  
Vol 5 (2) ◽  
pp. 123
Author(s):  
Rizky Pamuji ◽  
Ismiarta Aknuranda ◽  
Fatwa Ramdani
Keyword(s):  
Social Media ◽  
Natural Disasters ◽  
Citizen Science ◽  
Citizen Participation ◽  
Disaster Relief ◽  
Local Communities ◽  
Citizen Involvement ◽  
The Government

Citizen participation in collect and distribute information increase the role of the citizen involvement in local issues and increasing the benefits of society for the government and the environment. The contribution of citizens can be useful in helping to deal with environment problems and assist certain parties in meeting data needs, this is commonly referred to as citizen science. In its development, citizen science involvement in providing information began to involve social media as a platform for sharing information. In this study we try to explore citizen science of Indonesia, we conduct case study exploring how citizen in Indonesia used social media such as Twitter in response to one of the country’s worst disaster in 2018 namely Lombok Earthquake. By analyzing these user generate message we may know what the response of Indonesian citizen during event and understand more about citizen science in Indonesia through social media including its role and contribution. The information also may assist local communities in obtaining up-to-date information, providing assistance according to needs of the populace and use to manage and plan disaster relief both during and after the event.

Class-modeling approach to PTR-TOFMS data: a peppers case study

2014 ◽  
Vol 95 (8) ◽  
pp. 1757-1763 ◽  
Author(s):  
Cosimo Taiti ◽  
Corrado Costa ◽  
Paolo Menesatti ◽  
Diego Comparini ◽  
Nadia Bazihizina ◽  
...  
Keyword(s):  
Modeling Approach
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