A Semantic Framework for Integrated Modeling and Simulation of Transportation Systems

Zhou, Qunzhi; Prasanna, Viktor K.; Chang, Hwan; Wang, Yun

doi:10.3182/20090902-3-us-2007.0111

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…An example of the application framework structure, based on the ontological data model, is given in [12]. The more or less deep ontology use for multi-agent modeling is described also in [9], [6] and other works.…”

Section: Related Workmentioning

confidence: 99%

“…This level is usually consisting of the highly abstract concepts, but it seriously affects the overall model structure. For example, the higher level of the model is often started with distinction between physical and nonphysical object classes [12], which immediately rises some important questions. We will review some of them later.…”

Section: Related Workmentioning

confidence: 99%

“…Ontologies may be used for bridging various models: for example, one model may provide source data for another, exposing its interface as a service having ontological description [1]. OWL, the core standard for semantic knowledge representation, offers the requisite level of expressiveness to capture the rich relationships, and can be easily extended [12]. OWL also offers the great flexibility in ontology modeling, which brings many benefits and some problems.…”

Section: Related Workmentioning

confidence: 99%

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Building Ontologies for Agent-Based Simulation

Gorshkov¹

2015

Advances in Swarm and Computational Intelligence

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Abstract. Using ontologies for simulation models construction has some advantages that cannot be underestimated. Building the ontology, a modeler has to choose conceptualization method, which significantly affects the structure and usability of resulting models. A tendency of using standard ontologies without critically estimating their applicability for particular tasks may even lead to the loss of the model's efficiency and reliability. In this work, we are considering a simple criterion which may be used to pragmatically assess applicability of particular modeling techniques for building ontologies for simulation models. We will specially focus on the temporal aspect, states and events representation methods in the model. A fragment of ontology for the city social infrastructure optimization modeling will be considered. Keywords: Ontologies modeling · Ontology assessment · Collaborative modeling IntroductionSemantic Web technologies became attractive for the implementers of agent-based simulation methods since they have emerged. Semantic Web-oriented software is providing a convenient way for digital representation of conceptual models. The further development of this idea leads to the representation of the simulation model itself in the ontological form, and building a model execution framework around the semantic data storage [11]. This approach has many advantages, among which are:─ Simplification of the information gathering from various sources. A semantic model can be used for merge heterogeneous data into a single representation. ─ Ease of the model management during its development and exploration. Adding new object types, attributes and interaction rules into semantic model is simple. It is much more flexible that the relational data oriented model. ─ The ontologies are very convenient for expressing agent behavior and interaction rules in simulation models -especially the logical behavioral patterns. ─ The reasoning software can automate rules execution during the simulation.It is natural that the use of rather complex and flexible modeling methods and information representations is producing some uneasy problems. To ensure that the use of semantic technologies will benefit modeling process and result, it is necessary to adequately construct the target model ontology, and implement it. The term

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Building Ontologies for Agent-Based Simulation

Gorshkov¹

2015

Advances in Swarm and Computational Intelligence

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Intelligent Transportation Systems

Narayanaswami¹

2016

Advances in Civil and Industrial Engineering

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“Intelligent Transportation systems” is what everyone wants to know about, and about which very little is available as know-how. ITS technologies and monitoring systems are quite popular and reasonably well deployed in developed countries, particularly the roadways and airways. ITS holds a greater promise than ever before, as both availability of niche technologies and demand for more efficient transportation systems have increased multi-fold in recent years. Of late, there are huge railway projects all over the world that spans through several techniques, such as light / heavy rails, monorails etc. Apart from the social benefits that can be envisaged, these projects are genuine examples of public-private partnerships along with global business operations. Many of these projects demonstrate a classy trend of moving towards automation of operations of very large scales. Few agent architectures are discussed in brief in this chapter.

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A Semantic Framework for Integrated Modeling and Simulation of Transportation Systems

Cited by 2 publications

References 16 publications

Building Ontologies for Agent-Based Simulation

Building Ontologies for Agent-Based Simulation

Intelligent Transportation Systems

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