QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis
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 anti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/22/4882 |
| _version_ | 1811262522333855744 |
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| author | Fernando Terroso-Saenz Andres Muñoz José M. Cecilia |
| author_facet | Fernando Terroso-Saenz Andres Muñoz José M. Cecilia |
| author_sort | Fernando Terroso-Saenz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T19:26:59Z |
| format | Article |
| id | doaj.art-14702e5c1b1149f2ba40794bce4dea72 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:26:59Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-14702e5c1b1149f2ba40794bce4dea722022-12-22T03:19:26ZengMDPI AGSensors1424-82202019-11-011922488210.3390/s19224882s19224882QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data AnalysisFernando Terroso-Saenz0Andres Muñoz1José M. Cecilia2Polytechnic School, Universidad Católica de Murcia (UCAM), Murcia 30107, SpainPolytechnic School, Universidad Católica de Murcia (UCAM), Murcia 30107, SpainPolytechnic School, Universidad Católica de Murcia (UCAM), Murcia 30107, SpainRoad 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.https://www.mdpi.com/1424-8220/19/22/4882taxi demandonline social networksmachine learningair pollutionsmart citiessocial media analysis |
| spellingShingle | Fernando Terroso-Saenz Andres Muñoz José M. Cecilia QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis Sensors taxi demand online social networks machine learning air pollution smart cities social media analysis |
| title | QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis |
| title_full | QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis |
| title_fullStr | QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis |
| title_full_unstemmed | QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis |
| title_short | QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis |
| title_sort | quadriven a framework for qualitative taxi demand prediction based on time variant online social network data analysis |
| topic | taxi demand online social networks machine learning air pollution smart cities social media analysis |
| url | https://www.mdpi.com/1424-8220/19/22/4882 |
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