In order to study the formation mechanism of residents' low-carbon awareness
and provide an important basis for traffic managers to guide urban residents to
choose low-carbon travel mode, this paper proposes a low-carbon energy-saving
travel data feature analysis and mining based on machine learning algorithm.
This paper uses data mining technology to analyze the data of low-carbon travel
questionnaire, and regards the 15-dimensional problem under the framework of
planned behavior theory as the internal cause variable that characterizes
residents' low-carbon travel willingness. The author uses K-means clustering
algorithm to classify the intensity of residents' low-carbon travel
willingness, and applies the results as the explanatory variables to the random
forest model to explore the mechanism of residents' social attribute
characteristics, travel characteristics, etc. on their low-carbon travel
willingness. The experimental results show that based on the Silhouette index
test and t-SNE dimensionality reduction, residents' low-carbon travel
willingness can be divided into three categories: strong, neutral, and not
strong; Based on the importance index, the four most significant factors are
the occupation, residence, family composition and commuting time of residents.
Conclusion: This method provides policy recommendations for the development and
management of urban traffic low-carbon from multiple perspectives.

通过机器学习算法对低碳出行数据进行特征分析和挖掘，本研究旨在研究居民低碳意识形成机制，为交通管理者引导城市居民选择低碳出行方式提供重要依据。实验结果表明，居民低碳出行意愿可分为三类：强烈、中立和不强烈；而职业、居住地、家庭结构和通勤时间是最重要的因素之一。该方法从多个方面为城市交通低碳的发展和管理提供政策建议。