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.
翻译：减少空气污染,如CO2和PM2.5排放,是全世界许多国家最重要的问题之一。选择环境友好型运输方式可能是个人减少日常生活中空气污染的有效办法。在本研究中,我们提出一种方法,模拟生态友好型运输方式选择的有效性,通过地图搜索日志减少空气污染。我们把运输模式选择作为一种组合优化问题,把二氧化碳排放总量的限制作为空气污染和平均旅行时间的一个例子。优化结果显示二氧化碳排放总量可以减少9.23%,而平均旅行时间实际上可以减少9.96%。我们的研究提案在2019年KDD杯普通机器学习竞赛轨道2中获得了第一奖。
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