Visual Analytics Approach to User-Controlled Evacuation Scheduling

Andrienko, Gennady; Andrienko, Natalia; Bartling, Ulrich

doi:10.1057/palgrave.ivs.9500174

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…The system provides interactive visual tools for viewing, assessing, and comparing proposed candidate solutions. The task of transportation scheduling is addressed by Andrienko et al [3]. The general problem is to create a schedule for transporting a given set of items from their current locations to suitable destination places within a given time budget using an available fleet of transportation means.…”

Section: Modeling and Planningmentioning

confidence: 99%

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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124

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This is the accepted version of the paper.This version of the publication may differ from the final published version. City Research Online  Abstract-Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions including social transportation, recommender systems, and policy implications. Permanent repository link

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Section: Modeling and Planningmentioning

confidence: 99%

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

Self Cite

124

View full text Add to dashboard Cite

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“…The genetic algorithm with its varieties of derivations has been used in the scope of disaster prediction and prevention, e.g. for the decision making [6] or the global scheduling of resistance forces [7].…”

Section: The Basis Of the Integrated Modelmentioning

confidence: 99%

A Nonlinear Heuristic-Optimized Neural Network Modeling Approach for the Prediction of Natural Disasters

Wan

Liang

2013

AMR

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The disaster early-warning and corresponding prediction based on the nonlinear models is one of the important research aspects in the scope of natural disaster prediction and prevention. In this paper, the principle of applying heuristic algorithms to modify the classical neural networks so that some improved models with more efficiency can be achieved is proposed. The detailed structure and implementation of such model is also provided by building a heuristic-optimized neural network (HNN) model. Some regional flood data of China is then applied to conduct the prediction with the proposed model. Simulation results demonstrate that the proposed model can achieve higher accuracy and find an appropriate trade-off between the cost of processing time and precision, compared to the normal nonlinear models.

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“…The GeoDA package, which is free but not open source, provides easy-touse software for exploring spatial dependency, and outstanding facilities for defining spatial weights (Anselin et al 2006). Evacuation scheduling is another interesting application of space-time modelling (Andrienko et al 2008a). …”

Section: Interactive Exploration Of Spatio-temporal Structurementioning

confidence: 99%

Analysing spatio-temporal autocorrelation with LISTA-Viz

Hardisty

Klippel

2010

International Journal of Geographical Information Science

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-Many interesting analysis problems (for example, disease surveillance) would become more tractable if their spatio-temporal structure was better understood. Specifically, it would be helpful to be able to identify autocorrelation in space and time simultaneously. Some of the most commonly used measures of spatial association are LISA statistics, such as the Local Moran's I or the Getis-Ord Gi*, however these have not been applied to the spatio-temporal case (including many time steps) due to computational limitations. We have implemented a spatio-temporal version of the Local Moran's I, and claim two advances: First, we exploit the fact that there are a limited number of topological relationships present in the data to make Monte Carlo estimation of probability densities computationally practical, and thereby bypass the "curse of dimensionality". We term this approach "spatial memoization". Second, we developed a tool (LISTA-Viz) for interacting with the spatiotemporal structure uncovered by the statistics which contains a novel coordination strategy. The potential usefulness of the method and associated tool are illustrated by an analysis of the 2009 H1N1 pandemic, with the finding that there was a critical spatio-temporal "inflection point" at which the pandemic changed its character in the United States.

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Visual Analytics Approach to User-Controlled Evacuation Scheduling

Abstract: This is the unspecified version of the paper.This version of the publication may differ from the final published version. convenience. In the case of acceptance of the paper, we shall convert the excessive color figures to black and white. Permanent

Cited by 8 publications

References 9 publications

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

A Nonlinear Heuristic-Optimized Neural Network Modeling Approach for the Prediction of Natural Disasters

Analysing spatio-temporal autocorrelation with LISTA-Viz

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