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Research on Classification of Highway Service Areas Based on Multifactor Clustering

Abstract The current classification of highway service areas is primarily based on qualitative analysis, which lacks consistent standards and fails to meet the requirements for operation and planning. This article proposes a classification method for highway service areas based on land use features and characteristics specific to these areas. POI (point of interest) data is obtained from a mapping platform, and surveys provide information on the area of highway service areas, distance from city centers, regional economic conditions, and population data. The approach involves utilizing Principal Component Analysis (PCA) to extract feature factors from the POI data, which are combined with the highway service area characteristics to form classification indicators. The clustering tendencies are analyzed using the Hopkins statistic, the number of clusters is determined using the elbow method, and the advantages and disadvantages of K-Means, Fuzzy C-means (FCM), and hierarchical clustering algorithms are assessed using the Calinski-Harabasz (CH) coefficient, Silhouette Coefficient (SC), and Davies-Bouldin (DB) index. Using data from 95 highway service areas in Shaanxi Province as an example, the article applies the K-Means, FCM, and hierarchical clustering algorithms to classify the types of highway service areas and conducts feature analysis for each type. The research findings indicate that the K-Means clustering algorithm outperforms the FCM and hierarchical clustering algorithms according to all three evaluation indicators. Therefore, the article employs the K-Means clustering algorithm to classify the 95 highway service areas in Shaanxi Province into three categories. The classification results obtained from this study provide a basis for the comprehensive development of highway service areas and the surrounding land. By offering a more refined and consistent classification method, this research addresses the current issues and supports improved operation and planning of highway service areas.

Research Square Platform LLC

Cao zhen Sun yuhui Geng juna Tian zhun

2023

Title: Research on Classification of Highway Service Areas Based on Multifactor Clustering

Description:

This article proposes a classification method for highway service areas based on land use features and characteristics specific to these areas.

POI (point of interest) data is obtained from a mapping platform, and surveys provide information on the area of highway service areas, distance from city centers, regional economic conditions, and population data.

The approach involves utilizing Principal Component Analysis (PCA) to extract feature factors from the POI data, which are combined with the highway service area characteristics to form classification indicators.

The clustering tendencies are analyzed using the Hopkins statistic, the number of clusters is determined using the elbow method, and the advantages and disadvantages of K-Means, Fuzzy C-means (FCM), and hierarchical clustering algorithms are assessed using the Calinski-Harabasz (CH) coefficient, Silhouette Coefficient (SC), and Davies-Bouldin (DB) index.

Using data from 95 highway service areas in Shaanxi Province as an example, the article applies the K-Means, FCM, and hierarchical clustering algorithms to classify the types of highway service areas and conducts feature analysis for each type.

The research findings indicate that the K-Means clustering algorithm outperforms the FCM and hierarchical clustering algorithms according to all three evaluation indicators.

Therefore, the article employs the K-Means clustering algorithm to classify the 95 highway service areas in Shaanxi Province into three categories.

The classification results obtained from this study provide a basis for the comprehensive development of highway service areas and the surrounding land.

By offering a more refined and consistent classification method, this research addresses the current issues and supports improved operation and planning of highway service areas.

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