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.

通过地图搜索日志模拟环保交通模式选择的有效性，并将交通模式选择视为组合优化问题，考虑 CO2 和平均旅行时间的约束条件，优化结果表明，CO2 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％。