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Prediction System for Decision-Making to Improve the Road Environment

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Cooperative Design, Visualization, and Engineering (CDVE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9929))

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Abstract

The aim of this research was to find the relations among traffic volume, travel speed, and road connectivity in order to predict the traffic congestion. The result showed that when a road has higher connectivity, the travel speed goes lower. Therefore, it was assumed that the congestion on the target area should be affected by high connectivity. The visualization of this prediction would significantly affect the urban planning that is related with the stakeholders of different kind.

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Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A1A2A10057503). And also this work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「U-City Master and Doctor Course Grant Program」.

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Authors and Affiliations

  1. Department of Convergence Engineering for Future City, Sungkyunkwan University, Suwon, Republic of Korea

    Yu-Mi Song & Sung-Ah Kim

  2. Department of Architecture, Sungkyunkwan University, Suwon, Republic of Korea

    Sung-Ah Kim

Authors
  1. Yu-Mi Song
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  2. Sung-Ah Kim
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Corresponding author

Correspondence to Sung-Ah Kim .

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Editors and Affiliations

  1. University of the Balearic Islands, Palma, Mallorca, Spain

    Yuhua Luo

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© 2016 Springer International Publishing AG

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Cite this paper

Song, YM., Kim, SA. (2016). Prediction System for Decision-Making to Improve the Road Environment. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2016. Lecture Notes in Computer Science(), vol 9929. Springer, Cham. https://doi.org/10.1007/978-3-319-46771-9_40

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  • DOI: https://doi.org/10.1007/978-3-319-46771-9_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46770-2

  • Online ISBN: 978-3-319-46771-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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