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Open AccessArticle

QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Polytechnic School, Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(22), 4882; https://doi.org/10.3390/s19224882
Received: 3 October 2019 / Revised: 29 October 2019 / Accepted: 6 November 2019 / Published: 8 November 2019
(This article belongs to the Special Issue Architectures and Platforms for Smart and Sustainable Cities)
Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8. View Full-Text
Keywords: taxi demand; online social networks; machine learning; air pollution; smart cities; social media analysis taxi demand; online social networks; machine learning; air pollution; smart cities; social media analysis
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Terroso-Saenz, F.; Muñoz, A.; Cecilia, J.M. QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis. Sensors 2019, 19, 4882.

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