%0 Journal Article
%T 基于k-means算法的高速公路服务区分类研究<br>Research on Classification of Expressway Service Area Based on k-means Algorithm
%A 毛俊飞
%A 张冲
%A 高超
%A 刘兴国
%A 王光亚
%J Open Journal of Transportation Technologies
%P 73-80
%@ 2326-344X
%D 2024
%I Hans Publishing
%R 10.12677/OJTT.2024.132009
%X 截至2023年6月，我国高速公路的建设规模已达到17.73万公里，位居全球之首。根据国家公路网的长远规划预计到2035年，我国的高速公路总长度将达到16.2万公里，现如今已经提前完成规划，基本形成一个现代化、高质量的国家公路网络。这不仅在新的发展格局中发挥着重要作用，而且对推动经济发展具有关键性的支持作用。在这个背景下，高速公路服务区的分类成为了一个研究重点。它对于理解不同类型服务区周边的土地利用、客流变化规律、发展趋势和建设规划具有重要意义。为了深入分析这一课题，本文采用了聚类分析方法。通过收集服务区附近的兴趣点(POI)、交通枢纽等关键设施的数据，作为研究的主要变量。在对数据进行Z-Score标准化处理后，本研究运用了主成分分析和k-means聚类方法对高速公路服务区进行了细致的分类。根据我们的分析结果，这些服务区可以分为四类：综合型服务区、融合型生产服务区、旅游型服务区以及工业型生产服务区。这一分类不仅为我们后续的深入研究奠定了基础，而且为服务区未来的发展方向提供了参考。<br />
As of June 2023, China has achieved a remarkable milestone in infrastructure development by constructing 177,300 kilometers of highways, the largest network globally. According to the long-term plan for the national highway network, the total length of highways in China is pro-jected to reach 162,000 kilometers by 2035, the planning has been completed ahead of schedule, and a modern, high-quality national highway network has basically been formed. This significant development plays a crucial role in shaping the new development pattern and serves as a pivotal support for economic growth. In this context, the classification of highway service areas has be-come a research focus. It is of significant importance for understanding the land use around dif-ferent types of service areas, the patterns of passenger flow changes, development trends, and construction planning. To deeply analyze this topic, this paper adopts the method of cluster analysis. By collecting data on points of interest (POI), transportation hubs, and other key facilities near the service area, as the main variables for the study. After the data was standardized using the Z-Score method, this study applied principal component analysis and k-means clustering methods to perform a detailed classification of highway service areas. According to our analysis results, these service areas can be divided into four categories: comprehensive service areas, integrated production service areas, tourism-oriented service areas, and industrial production service areas. This classification not only lays the foundation for our subsequent in-depth research but also provides a reference for the future development direction of service areas.
%K 聚类分析，主成分分析，k-means聚类，高速公路服务区<br>Cluster Analysis
%K Principal Component Analysis
%K k-means Clustering
%K Motorway Service Area
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=82428