A Deep-Learning Model for Estimating the Impact of Social Events on Traffic Demand on a Cell Basis

In cellular networks, a deep knowledge of the traffic demand pattern in each cell is essential in network planning and optimization tasks. However, a precise forecast of the traffic time series per cell is hard to achieve, due to the noise originated by abnormal local events. In particular, mass soc...

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Bibliographic Details
Published inIEEE access Vol. 9; pp. 71673 - 71686
Main Authors Bejarano-Luque, Juan L., Toril, Matias, Fernandez-Navarro, Mariano, Gijon, Carolina, Luna-Ramirez, Salvador
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Cellular communication
cellular network
Computer architecture
Concerts
context
Datasets
Deep learning
Demand
Directories
Forecasting
Microprocessors
multi task
Neural networks
Neurons
Optimization
Predictive models
social events
time series
Time series analysis
traffic forecast
Traffic information
Traffic models
Traffic planning
Wireless communications
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Summary:In cellular networks, a deep knowledge of the traffic demand pattern in each cell is essential in network planning and optimization tasks. However, a precise forecast of the traffic time series per cell is hard to achieve, due to the noise originated by abnormal local events. In particular, mass social events (e.g., concerts, conventions, sport events...) have a strong impact on traffic demand. In this paper, a data-driven model to estimate the impact of local events on cellular traffic is presented. The model is trained with a large dataset of geotagged social events taken from public event databases and hourly traffic data from a live Long Term Evolution (LTE) network. The resulting model is combined with a traffic forecast module based on a multi-task deep-learning architecture to predict the hourly traffic series with scheduled mass events. Model assessment is performed over a real dataset created with geolocated social event information collected from public event directories and hourly cell traffic measurements during two months in a LTE network. Results show that the addition of the proposed model significantly improves traffic forecasts in the presence of massive events.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3078113