基于ETC收费数据的特大城市高速公路通勤流识别方法研究

doi:10.6040/j.issn.1672-3961.0.2024.210

摘要/Abstract

摘要： 为科学分析特大城市高速公路通勤流特征,从“客流特征-用地结构”双维度出发,利用高速公路ETC收费数据及收费站周边5 km用地数据开展分析:用区位熵和模糊C-均值法划分收费站点类型;结合车辆工作日通行特点,用改进Canopy-K-means算法识别通勤流;以济南市高速公路通勤流为例进行验证并分析其时空分布特征。结果表明:收费站点分就业主导型、错位偏就业型等6类;通勤流分早晚高峰时段均选择高速公路通行的常规型和仅早高峰时段选择高速公路通勤的特殊型2类;通勤流以“县城-中心城区”车流为主。此研究为高速公路道路规划、差异化收费等提供数据与理论支撑。

关键词: 道路交通, ETC数据, 特征聚类, 通勤识别, 站点类型

Abstract: To scientifically analyze the characteristics of highway commuting flows in megacities, a dual-dimensional approach focusing on “passenger flow characteristics-land use structure”was adopted in this study. Highway ETC toll data and land use data within a 5 km radius of toll stations were utilized for analysis. The station types were classified using location entropy and the fuzzy C-means algorithm. Based on vehicles' weekday travel patterns, an improved Canopy-K-means algorithm was applied to identify commuting flows. The highway commuting flows in Jinan were used as a case study for validation and analysis of their spatiotemporal distribution characteristics. The results showed that toll stations could be categorized into six types, including employment-dominated and employment-biased mismatch types. Commuting flows were divided into two categories: conventional commuters who used highways during both morning and evening peak hours, and special commuters who only used highways during the morning peak hour. The commuting flows were primarily composed of traffic from counties to the central urban area. This research provided data and theoretical support for highway planning and differentiated toll policies.

Key words: road traffic, ETC data, feature clustering, commuting identification, station type

中图分类号:

TP311.13

代洪娜,李忠锐,冯海霞,赵欢欢,范宏贤. 基于ETC收费数据的特大城市高速公路通勤流识别方法研究[J]. 山东大学学报 (工学版), 2026, 56(5): 40-51.

Dai Hongna, Li Zhongrui, Feng Haixia, Zhao Huanhuan, Fan Hongxian. The study of commuter flow identification methods on expressways in megacities based on ETC toll data[J]. Journal of Shandong University(Engineering Science), 2026, 56(5): 40-51.

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