Modeling of utility function for real-time prediction of spatial information

Keiichiro Sato, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki, Takanori Iwai, Takeo Onishi, Takahiro Nobukiyo, Dai Kanetomo, Kozo Satoda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Real-time prediction of spatial information has attracted a lot of attention. Machine learning enables us to provide real-time prediction of spatial information such as road traffic by using aggregated sensor data. The amount of mobile traffic is forecasted to increase exponentially, thereby causing serious transmission delays when traffic loads are heavy. If a part of the data used for predicting spatial information in real time does not arrive on time, the prediction accuracy degrades because the prediction is done without the missing data. A utility-based scheduling technique has been suggested as a way of prioritizing such delay-sensitive data. However, no study has not addressed the utility-based scheduling for the real-time prediction of spatial information. Therefore, this paper proposes a scheme that enables modeling the utility function for real- time prediction of spatial information. The scheme is roughly composed of two steps: the first creates training data from original time-series data and a machine learning model using the data, while the second models the utility function using the feature selection method in the learning model. Feature selection method enables extracting the importance of data in terms of how much the data contributes to the prediction accuracy. This paper assumes the road traffic prediction as a scenario and shows the utility function modeled by the proposed scheme using real spatial datasets. A numerical study demonstrates how the model of the utility function works effectively in prioritizing data for real-time prediction in terms of accuracy.

Original languageEnglish
Title of host publication2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109626
Publication statusPublished - 2019 Dec
Externally publishedYes
Event2019 IEEE Global Communications Conference, GLOBECOM 2019 - Waikoloa, United States
Duration: 2019 Dec 92019 Dec 13

Publication series

Name2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings


Conference2019 IEEE Global Communications Conference, GLOBECOM 2019
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Health Informatics


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