Semantic Traffic Diagnosis with STAR-CITY: Architecture and Lessons Learned from Deployment in Dublin, Bologna, Miami and Rio

Lécué, Freddy; Tucker, Robert E.; Tallevi-Diotallevi, Simone; Nair, Rahul; Gkoufas, Yiannis; Liguori, G.; Borioni, Mauro; Rademaker, Alexandre; Barbosa, Luciano

doi:10.1007/978-3-319-11915-1_19

Cited by 14 publications

(10 citation statements)

References 10 publications

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“…STAR-CITY (Semantic Traffic Analytics and Reasoning for CITY), an IBM project, is deployed in four smart cities: Dublin, Bologna, Miami, and Rio de Janeiro [38]. The project is focused on designing ontologies to diagnose and predict road traffic congestions.…”

Section: A Existing Ontologies For Smart Citiesmentioning

confidence: 99%

Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

Gyrard

Zimmermann

Sheth

2018

IEEE Internet Things J.

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The Internet of Things (IoT) plays an ever-increasing role in enabling Smart City applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: READY4SmartCities, LOV, OpenSensingCity (OSC) and, LOV4IoT. To support semantic interoperability with the reuse of ontology-based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies. Our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by OSC and LOV4IoT ontology catalogs. Researchers and developers have completed a survey based evaluation of the LOV4IoT catalog. The usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance. The quality of IoT and smart city ontologies have been evaluated to improve the ontology catalog quality. We also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools.

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Section: A Existing Ontologies For Smart Citiesmentioning

confidence: 99%

Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

Gyrard

Zimmermann

Sheth

2018

IEEE Internet Things J.

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“…Originally deployed in Dublin, the STAR-CITY system has shown limitations in terms of flexibility and scalability when being applied to other cities. Therefore Lécué, et al [23] have introduced the "any-city" architecture which addresses some of these issues to a degree. Since the individual components are strongly connected within the system, applying the solution to other Smart City applications is hindered.…”

Section: Correlation Analysis In Smart City Datamentioning

confidence: 99%

“…To give a simple example, consider the temperature measurements of m1 = [−5, −5, −5, −3, −3, −5], m2 = [−5, −4, −3, −2, −3, −1]and m3 =[25,25,25,23,23,25]…”

mentioning

confidence: 99%

Using LDA to Uncover the Underlying Structures and Relations in Smart City Data Streams

Puschmann

Barnaghi

Tafazolli

2018

IEEE Systems Journal

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Recent advancements in sensing, networking technologies\ud and collecting real-world data on a large scale and from various environments\ud have created an opportunity for new forms of real-world services\ud and applications. This is known under the umbrella term of the Internet\ud of Things (IoT). Physical sensor devices constantly produce very large\ud amounts of data. Methods are needed which give the raw sensor measurements\ud a meaningful interpretation for building automated decision\ud support systems. To extract actionable information from real-world data,\ud we propose a method that uncovers hidden structures and relations\ud between multiple IoT data streams. Our novel solution uses Latent\ud Dirichlet Allocation (LDA), a topic extraction method that is generally\ud used in text analysis. We apply LDA on meaningful abstractions that\ud describe the numerical data in human understandable terms. We use\ud Symbolic Aggregate approXimation (SAX) to convert the raw data into\ud string-based patterns and create higher level abstractions based on\ud rules.\ud We finally investigate how heterogeneous sensory data from multiple\ud sources can be processed and analysed to create near real-time intelligence\ud and how our proposed method provides an efficient way to\ud interpret patterns in the data streams. The proposed method uncovers\ud the correlations and associations between different pattern in IoT data\ud streams. The evaluation results show that the proposed solution is able\ud to identify the correlation with high efficiency with an F-measure up to\ud 90%

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“…Lécué et al . [50–52] developed a system named STAR-CITY to analyze, diagnose, explore, and predict traffic states using semantic web technologies. Recently, Zheng et al .…”

Section: Introductionmentioning

confidence: 99%

Framework for fusing traffic information from social and physical transportation data

Zheng

Wang

et al. 2018

PLoS ONE

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Tremendous volumes of messages on social media platforms provide supplementary traffic information and encapsulate crowd wisdom for solving transportation problems. However, social media messages manifested in human languages are usually characterized with redundant, fuzzy and subjective features. Here, we develop a data fusion framework to identify social media messages reporting non-recurring traffic events by connecting the traffic events with traffic states inferred from taxi global positioning system (GPS) data. Temporal-spatial information of traffic anomalies caused by the traffic events are then retrieved from anomalous traffic states. The proposed framework successfully identified accidental traffic events with various scales and exhibited strong performance in event descriptions. Even though social media messages are generally posted after the occurrence of anomalous traffic states, resourceful event descriptions in the messages are helpful in explaining traffic anomalies and for deploying suitable countermeasures.

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Semantic Traffic Diagnosis with STAR-CITY: Architecture and Lessons Learned from Deployment in Dublin, Bologna, Miami and Rio

Cited by 14 publications

References 10 publications

Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

Using LDA to Uncover the Underlying Structures and Relations in Smart City Data Streams

Framework for fusing traffic information from social and physical transportation data

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