An Analysis of Effects of Emergency Fine Dust Reduction Measures and National Petition Using Regression Analysis and Text Mining


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 11, pp. 427-434, Nov. 2018
10.3745/KTSDE.2018.7.11.427, Full Text:
Keywords: Fine Dust, Transportation, Regression, Text Mining, topic modeling
Abstract

Recently, the Seoul government implemented ‘Free Public Transportation’ policy and ‘Citizen Participatory Alternative-Day-No-Driving’ system as ‘Emergency Fine Dust Reduction Measures’. In this paper, after identifying the effectiveness of the two traffic policies, suggestions for direction of future fine dust policy were made. The effect of traffic on the fine dust was analyzed by regression analysis and the responses to the two traffic policies and petitions were analyzed using text mining. Our experimental results show that the responses to the policy were mostly negative, and the influence of the domestic factors was considerable unlike expectation of citizens. Moreover, the result made us possible to know people’s specific needs on fine dust reduction policy. Finally, based on the result, the suggestions for fine dust reduction policy direction were provided.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
Annie Kim, So-Hee Jeong, Hyun-Bin Choi, and Hyon Hee Kim, "An Analysis of Effects of Emergency Fine Dust Reduction Measures and National Petition Using Regression Analysis and Text Mining," KIPS Transactions on Software and Data Engineering, vol. 7, no. 11, pp. 427-434, 2018 [DOI: 10.3745/KTSDE.2018.7.11.427].

[ACM Style]
Kim, A., Jeong, S., Choi, H.,, and Kim, H. H. 2018. An Analysis of Effects of Emergency Fine Dust Reduction Measures and National Petition Using Regression Analysis and Text Mining. KIPS Transactions on Software and Data Engineering, 7, 11, (2018), 427-434 [DOI: 10.3745/KTSDE.2018.7.11.427].