Semantic Traffic Sensor Data: The TRAFAIR Experience

Desimoni, Federico; Ilarri, Sergio; Po, Laura; Rollo, Federica; Trillo-Lado, Raquel

doi:10.3390/app10175882

Cited by 9 publications

(3 citation statements)

References 50 publications

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“…Besides the general sector, academic research [38,39] and social network topics [25,40,41] are commonly using Graph databases for their data processing. In the most private sector, researchers in Transportation really need Graph processing, such as to simulate traffic management [42], find the best route like [43]. Another area that is often used for graph databases is the Geographical sector, refer to publications [44,45] and [46].…”

Section: Research Resultsmentioning

confidence: 99%

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Rizaldy¹,

Fahriah

Hartono³

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semantic Web, Big Data, and Parallel computing. A total of 19 (30%) studies used Neo4j as their database. Apart from Social Networks, the industries that implement GDB the most are the Transportation sector, Scientific Article Networks, and general sectors such as Enterprise Data, Biological data, and History data. This Literature Review concludes that research on the topic of the Graph Database is still developing in the future. This is shown by the breadth of application and the variety of new derivatives of GDB products offered by researchers to address existing problems.

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Section: Research Resultsmentioning

confidence: 99%

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Rizaldy¹,

Fahriah

Hartono³

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…In the evolution of smart cities, the accessibility of geospatial data sources is as important as the data itself. Therefore, the efficiency of urban planning and sustainable efforts heavily relies on both the diversity of these sources and the levels of accessibility they can provide [125]. In this sense, governmental and public sector agencies are the primary sources of geospatial data.…”

Section: Data Sources and Accessibilitymentioning

confidence: 99%

Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches

Costa,

Bittencourt,

Oliveira

et al. 2024

Sustainability

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In recent years, the concept of smart cities has become increasingly important in the pursuit of sustainable development goals. In general, common urban challenges have been addressed through smart-city services, and new perspectives for more sustainable cities have emerged. To realize the full potential of such smart urban environments, geospatial approaches have been used as a focal point, offering a plethora of applications that contribute to a better understanding of urban challenges and innovation potentials. Nevertheless, although significant progress has been made, different problems may arise when the available technologies and resources are not understood or even when their potentialities are not properly capitalized. This article reviews the state of the art in the field, highlighting success cases and remaining challenges in exploiting geospatial data-driven strategies, particularly when leveraging geographic information systems, satellites, and distributed sensors to produce and process geospatial data and datasets in urban scenarios. Moreover, a more organized perspective of the area is provided in this article, as well as future development trends, supporting new research efforts in this area when empowering smart cities for a more sustainable future.

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“…Combining traffic and pollution data has attracted several stances in the recent literature. For example, within the consortium of the TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project, authors of [28,29] presented a system that aimed at understanding the correlation between vehicle traffic and urban air quality. They basically designed a system for providing real-time and predicted air quality values for many cities in Europe by deploying cost-effective air quality monitoring sensors, in addition to other tasks such as data capturing and integration from heterogeneous sources, thus offering a unified umbrella for joint analytics of urban air quality based on traffic flows.…”

Section: Related Literaturementioning

confidence: 99%

Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees

Jawarneh

Bellavista

2023

Future Internet

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Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.

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Semantic Traffic Sensor Data: The TRAFAIR Experience

Cited by 9 publications

References 50 publications

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches

Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees

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