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Computer Science > Machine Learning

arXiv:2109.04831 (cs)
[Submitted on 10 Sep 2021 (v1), last revised 14 Sep 2021 (this version, v2)]

Title:Simulating the Effects of Eco-Friendly Transportation Selections for Air Pollution Reduction

Authors:Keiichi Ochiai, Tsukasa Demizu, Shin Ishiguro, Shohei Maruyama, Akihiro Kawana
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Abstract:Reducing air pollution, such as CO2 and PM2.5 emissions, is one of the most important issues for many countries worldwide. Selecting an environmentally friendly transport mode can be an effective approach of individuals to reduce air pollution in daily life. In this study, we propose a method to simulate the effectiveness of an eco-friendly transport mode selection for reducing air pollution by using map search logs. We formulate the transport mode selection as a combinatorial optimization problem with the constraints regarding the total amount of CO2 emissions as an example of air pollution and the average travel time. The optimization results show that the total amount of CO2 emissions can be reduced by 9.23%, whereas the average travel time can in fact be reduced by 9.96%. Our research proposal won first prize in Regular Machine Learning Competition Track Task 2 at KDD Cup 2019.
Comments: KDD Cup 2019 Regular ML Track Task 2, 1st Prize this https URL
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.04831 [cs.LG]
  (or arXiv:2109.04831v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.04831

Submission history

From: Keiichi Ochiai [view email]
[v1] Fri, 10 Sep 2021 12:30:32 UTC (1,722 KB)
[v2] Tue, 14 Sep 2021 00:16:32 UTC (1,722 KB)
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