Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies

Lécué, Freddy; Tucker, Robert E.; Bicer, Veli; Tommasi, Pierpaolo; Tallevi-Diotallevi, Simone; Sbodio, Marco Luca

doi:10.1007/978-3-319-07443-6_41

Cited by 33 publications

(11 citation statements)

References 11 publications

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“…Many times, even being an expert in the domain is not enough to understand the results. If underlying semantics of data is not correctly interpreted, results may not be as precise and consistent as they can be [46].…”

Section: Discussionmentioning

confidence: 98%

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

Esnaola-Gonzalez

Bermúdez

Fernández

et al. 2018

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Fulfilling occupants' comfort whilst reducing energy consumption is still an unsolved problem in most of tertiary buildings. However, the expansion of the Internet of Things (IoT) and Knowledge Discovery in Databases (KDD) techniques lead to research this matter. In this paper the EEPSA (Energy Efficiency Prediction Semantic Assistant) process is presented, which leverages the Semantic Web Technologies (SWT) to enhance the KDD process for achieving energy efficiency in tertiary buildings while maintaining comfort levels. This process guides the data analyst through the different KDD phases in a semiautomatic manner and supports prescriptive HVAC control strategies. That is, temperature of a space is predicted simulating the activation of HVAC systems at different times and intensities, so that the facility manager can choose the strategy that best fits both the user's comfort needs and energy efficiency. Furthermore, results show that the proposed solution improves the accuracy of predictions.

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Section: Discussionmentioning

confidence: 98%

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

Esnaola-Gonzalez

Bermúdez

Fernández

et al. 2018

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“…Freddy Lécué et al proposed a system named STAR-CITY integrating structured and unstructured data, static and stream data, which supports semantic analytics and reasoning for city traffic. They applied semantic web technologies to analyze, diagnose, explore and predict traffic scenarios such as spatial-temporal analysis of traffic status and prediction of road traffic conditions [34].…”

Section: Related Workmentioning

confidence: 99%

Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

Chen

Xiao

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-ofword model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.

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“…The case of museum tours also touches the domain of mobile applications, geolocated usages and data and dynamic information about occurring events. A growing application domain of the Semantic Web is the integration of APIs and data streams coming from connected objects, sensors, smart devices and smart places for instance to support spatial ontology-mediated query answering over mobility streams (Eiter et al, 2017) or predict the severity of road traffic congestion (Lécué et al, 2014).…”

Section: Semantic Web In Use: Application Scenarios and Domainsmentioning

confidence: 99%

A survey of the first 20 years of research on semantic Web and linked data

Gandon¹

2018

isi

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This paper is a survey of the research topics in the field of Semantic Web, Linked Data and Web of Data. This study looks at the contributions of this research community over its first twenty years of existence. Compiling several bibliographical sources and bibliometric indicators, we identify the main research trends and we reference some of their major publications to provide an overview of that initial period. We conclude with some perspectives for the future research challenges. RÉSUMÉ. Cet article est une étude des sujets de recherche dans le domaine du Web sémantique, des données liées et du Web des données. Cette étude se penche sur les contributions de cette communauté de recherche au cours de ses vingt premières années d'existence. En compilant plusieurs sources bibliographiques et indicateurs bibliométriques, nous identifions les principales tendances de la recherche et nous référencons certaines de leurs publications majeures pour donner un aperçu de cette période initiale. Nous concluons avec une discussion sur les tendances et perspectives de recherche.

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Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies

Cited by 33 publications

References 11 publications

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

A survey of the first 20 years of research on semantic Web and linked data

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