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Exploration of Travel Patterns of Urban Rail Transit Commuters Based on Gaussian Mixture Model (GMM): A Case Study of Changsha, China
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Jierong DENG1, Chenhui LIU1, 2, 3
Urban Rapid Rail Transit | 2025, 38(3) : 48 - 53
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Urban Rapid Rail Transit | 2025, 38(3): 48-53
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Exploration of Travel Patterns of Urban Rail Transit Commuters Based on Gaussian Mixture Model (GMM): A Case Study of Changsha, China
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Jierong DENG1, Chenhui LIU1, 2, 3
Affiliations
  • 1 College of Civil Engineering Hunan University Changsha 410082
  • 2 Transportation Research Center Hunan University Changsha 410082
  • 3 National Key Laboratory of Bridge Safety and Resilience Hunan University Changsha 410082
Published: 2025-06-01 doi: 10.3969/j.issn.1672-6073.2025.03.007
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This study analyzes commuting patterns in urban rail transit and investigates the characteristics and regularities of different commuter types. The analysis is based on Changsha Metro AFC data, covering two consecutive weeks of working days from March 21 to April 3, 2023. A Gaussian Mixture Model (GMM) is constructed using three variables: morning boarding time, evening boarding time, and the average commuting duration. This model classifies passengers into three categories: the classic commuting pattern, the offpeak commuting pattern, and the longdistance commuting pattern. The results show that classic commuters primarily commute during regular peak hours (7:30–8:30 and 17:30–18:30), while longdistance commuters tend to start slightly earlier, and offpeak commuters avoid peak periods, with their morning boarding times spanning from 7:00 to 12:00 and evening boarding times concentrated between 20:00 and 22:00. In terms of commuting duration, classic and offpeak commuters typically travel for 15 to 30 minutes, whereas longdistance commuters predominantly travel for over 30 minutes. Additionally, the residential and workplace station distributions highlight that longdistance commuters are more likely to reside in peripheral urban areas, and the workplace stations of offpeak commuters are more concentrated than those of the other two groups, predominantly located in areas bustling with dining and entertainment activities.

urban rail transit  /  commuter  /  travel pattern  /  Gaussian mixture model  /  clustering analysis  /  AFC data  /  job-housing separation
Jierong DENG, Chenhui LIU. Exploration of Travel Patterns of Urban Rail Transit Commuters Based on Gaussian Mixture Model (GMM): A Case Study of Changsha, China[J]. Urban Rapid Rail Transit, 2025 , 38 (3) : 48 -53 . DOI: 10.3969/j.issn.1672-6073.2025.03.007
Year 2025 volume 38 Issue 3
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Article Info
doi: 10.3969/j.issn.1672-6073.2025.03.007
  • Receive Date:2024-09-02
  • Online Date:2025-07-09
  • Published:2025-06-01
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History
  • Received:2024-09-02
  • Revised:2024-12-18
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Affiliations
    1 College of Civil Engineering Hunan University Changsha 410082
    2 Transportation Research Center Hunan University Changsha 410082
    3 National Key Laboratory of Bridge Safety and Resilience Hunan University Changsha 410082
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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