{"id":{"repo_id":"passau-thes","oai_identifier":"oai:kobv.de-opus4-uni-passau:1549"},"canonical_url":"https://search.dev.ndltd.org/etd/passau-thes/oai:kobv.de-opus4-uni-passau:1549","repository":{"repo_id":"passau-thes","name":"Universität Passau","base_url":"https://opus4.kobv.de/opus4-uni-passau/oai"},"display":{"title":"Multimodal Data Space for Cooperative Intelligent Transport Systems","abstract":"Modern Cooperative Intelligent Transport Systems (C-ITSs) are comprehensive applications that must cope with a multitude of challenges while meeting strict service and security standards. One of these challenges is a fast, secure, reliable, and universal way to store and exchange data in such a traffic system. Furthermore, multimodal scenarios where different types of vehicles (e.g., cars and Unmanned Aerial System) interact with each other, are increasingly emerging. To overcome these challenges, this thesis presents a set of key innovations to establish a multimodal capable data space for transport application. Therefore, a multimodal optimized geographic model is presented, called SpatialJSON, that is capable of depicting two- and three-dimensional geometries. To accomplish this feat, SpatialJSON extends the popular GeoJSON format with two new data types: area and corridor. Exchanging, managing, and storing data is handled in a novel data-centric middleware, called Large Scale Multimodal Data Processing Middleware for Intelligent Transport Systems (LDPM). This LDPM uses cryptographic- and trust-based schemas to allow secure data exchange and provide data quality assessment. Furthermore, a service architecture is introduced, that fulfils modern service requirements. Trust management is also another essential part of a C-ITS. Hence, a novel scheme to describe traffic related evidence in a multimodal environment is introduced. This schema allows assessing arbitrary traffic related data. This information is then processed in a specialized and modified Bayesian Inference (BI) function. Subsequently, a comprehensive data centric trust management method is introduced. Finally, a use case is presented that relies on the aforementioned technologies to collect data in a hazardous environmental. This data is then distributed and managed via the LDPM, and finally visualized.","abstract_html":"Modern Cooperative Intelligent Transport Systems (C-ITSs) are comprehensive applications that must cope with a multitude of challenges while meeting strict service and security standards. One of these challenges is a fast, secure, reliable, and universal way to store and exchange data in such a traffic system. Furthermore, multimodal scenarios where different types of vehicles (e.g., cars and Unmanned Aerial System) interact with each other, are increasingly emerging. To overcome these challenges, this thesis presents a set of key innovations to establish a multimodal capable data space for transport application. Therefore, a multimodal optimized geographic model is presented, called SpatialJSON, that is capable of depicting two- and three-dimensional geometries. To accomplish this feat, SpatialJSON extends the popular GeoJSON format with two new data types: area and corridor. Exchanging, managing, and storing data is handled in a novel data-centric middleware, called Large Scale Multimodal Data Processing Middleware for Intelligent Transport Systems (LDPM). This LDPM uses cryptographic- and trust-based schemas to allow secure data exchange and provide data quality assessment. Furthermore, a service architecture is introduced, that fulfils modern service requirements. Trust management is also another essential part of a C-ITS. Hence, a novel scheme to describe traffic related evidence in a multimodal environment is introduced. This schema allows assessing arbitrary traffic related data. This information is then processed in a specialized and modified Bayesian Inference (BI) function. Subsequently, a comprehensive data centric trust management method is introduced. Finally, a use case is presented that relies on the aforementioned technologies to collect data in a hazardous environmental. 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This LDPM uses cryptographic- and trust-based schemas to allow secure data exchange and provide data quality assessment. Furthermore, a service architecture is introduced, that fulfils modern service requirements. Trust management is also another essential part of a C-ITS. Hence, a novel scheme to describe traffic related evidence in a multimodal environment is introduced. This schema allows assessing arbitrary traffic related data. This information is then processed in a specialized and modified Bayesian Inference (BI) function. Subsequently, a comprehensive data centric trust management method is introduced. Finally, a use case is presented that relies on the aforementioned technologies to collect data in a hazardous environmental. 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To overcome these challenges, this thesis presents a set of key innovations to establish a multimodal capable data space for transport application. Therefore, a multimodal optimized geographic model is presented, called SpatialJSON, that is capable of depicting two- and three-dimensional geometries. To accomplish this feat, SpatialJSON extends the popular GeoJSON format with two new data types: area and corridor. Exchanging, managing, and storing data is handled in a novel data-centric middleware, called Large Scale Multimodal Data Processing Middleware for Intelligent Transport Systems (LDPM). This LDPM uses cryptographic- and trust-based schemas to allow secure data exchange and provide data quality assessment. Furthermore, a service architecture is introduced, that fulfils modern service requirements. Trust management is also another essential part of a C-ITS. Hence, a novel scheme to describe traffic related evidence in a multimodal environment is introduced. This schema allows assessing arbitrary traffic related data. This information is then processed in a specialized and modified Bayesian Inference (BI) function. Subsequently, a comprehensive data centric trust management method is introduced. Finally, a use case is presented that relies on the aforementioned technologies to collect data in a hazardous environmental. This data is then distributed and managed via the LDPM, and finally visualized."],"dc:format.medium":["application/pdf"],"dc:publisher":["Universität Passau"],"dc:rights":["Creative Commons - CC BY - Namensnennung 4.0 International"],"dc:title":["Multimodal Data Space for Cooperative Intelligent Transport Systems"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Passau"]},"updated_at":"2026-07-24T03:45:10Z"}