Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (5): 40-51.doi: 10.6040/j.issn.1672-3961.0.2024.210

• Machine Learning & Data Mining • Previous Articles    

The study of commuter flow identification methods on expressways in megacities based on ETC toll data

Dai Hongna1, Li Zhongrui2*, Feng Haixia2, Zhao Huanhuan2, Fan Hongxian2   

  1. Dai Hongna1, Li Zhongrui2*, Feng Haixia2, Zhao Huanhuan2, Fan Hongxian2(1. Don College, Shandong Jiaotong University, Jinan 250357, Shandong, China;
    2. School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, Shandong, China
  • Published:2026-10-19

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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

CLC Number: 

  • TP311.13
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