Ruben Verborgh’s data

Matches in Ruben’s data for { <https://dx.doi.org/10.1145/3041021.3051699> ?p ?o }

• 3041021.3051699 author me.
• 3041021.3051699 author olivier_janssens.
• 3041021.3051699 author joachim_van_herwegen.
• 3041021.3051699 author gilles_vandewiele.
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• 3041021.3051699 author me.
• 3041021.3051699 creator me.
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• 3041021.3051699 creator erik_mannens.
• 3041021.3051699 creator me.
• 3041021.3051699 about route_planning.
• 3041021.3051699 about Research.
• 3041021.3051699 about World_Wide_Web.
• 3041021.3051699 author me.
• 3041021.3051699 author olivier_janssens.
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• 3041021.3051699 author erik_mannens.
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• 3041021.3051699 type ScholarlyArticle.
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• 3041021.3051699 maker femke_ongenae.
• 3041021.3051699 maker erik_mannens.
• 3041021.3051699 maker me.
• 3041021.3051699 title "Predicting train occupancies based on query logs and external data sources".
• 3041021.3051699 isPartOf proceedings_of_the_7th_international_workshop_on_location_and_the_web.
• 3041021.3051699 name "Predicting train occupancies based on query logs and external data sources".
• 3041021.3051699 label "Predicting train occupancies based on query logs and external data sources".
• 3041021.3051699 name "Predicting train occupancies based on query logs and external data sources".
• 3041021.3051699 topic route_planning.
• 3041021.3051699 topic Research.
• 3041021.3051699 topic World_Wide_Web.
• 3041021.3051699 subject route_planning.
• 3041021.3051699 subject Research.
• 3041021.3051699 subject World_Wide_Web.
• 3041021.3051699 authorList b0_b2331.
• 3041021.3051699 topic route_planning.
• 3041021.3051699 topic Research.
• 3041021.3051699 topic World_Wide_Web.
• 3041021.3051699 abstract "On dense railway networks—such as in Belgium—train travelers are frequently confronted with overly occupied trains, especially during peak hours. Crowdedness on trains leads to a deterioration in the quality of service and has a negative impact on the well-being of the passenger. In order to stimulate travelers to consider less crowded trains, the iRail project wants to show an occupancy indicator in their route planning applications by the means of predictive modelling. As there is no official occupancy data available, training data is gathered by crowd sourcing using the Web app iRail.be and the Railer application for iPhone. Users can indicate their departure & arrival station, at what time they took a train and classify the occupancy of that train into the classes: low, medium or high. While preliminary results on a limited data set conclude that the models do not yet perform sufficiently well, we are convinced that with further research and a larger amount of data, our predictive model will be able to achieve higher predictive performances. All datasets used in the current research are, for that purpose, made publicly available under an open license on the iRail website and in the form of a Kaggle competition. Moreover, an infrastructure is set up that automatically processes new logs submitted by users in order for our model to continuously learn. Occupancy predictions for future trains are made available through an API.".
• 3041021.3051699 datePublished "2017".
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