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仝照民(博士生)、刘耀林的论文在JOURNAL OF TRANSPORT GEOGRAPHY刊出
发布时间:2024-06-27     发布者:易真         审核者:任福     浏览次数:

标题: Detecting anomalous commuting patterns: Mismatch between urban land attractiveness and commuting activities

作者: Tong, ZM (Tong, Zhaomin); Zhang, ZY (Zhang, Ziyi); An, R (An, Rui); Liu, YL (Liu, Yaolin); Chen, HT (Chen, Huiting); Xu, JW (Xu, Jiwei); Fu, SH (Fu, Shihang)

来源出版物: JOURNAL OF TRANSPORT GEOGRAPHY : 116 文献号: 103867 DOI: 10.1016/j.jtrangeo.2024.103867 Published Date: 2024 APR

摘要: Rapid urbanization has dramatically changed the urban spatial structures, causing a mismatch between residents' commuting activities and the optimal status of the current urban facility configuration. However, limited attention has been paid to detecting these mismatched commuting patterns and their associations with built environmental characteristics. To maximize the effectiveness of urban facility allocation and improve commuting efficiency, this paper developed a framework to identify anomalous commuting interaction patterns. A weighted bipartite network considering urban land attractiveness was first constructed to analyze the commuting flows between urban units. Then a modified Hungarian algorithm was proposed to obtain the optimal commuting interaction fluxes. By comparing real and optimal interaction fluxes, two types of commuting anomalies were detected. Finally, the machine learning model was used to explore the non-linear relationships between built environment and anomalous commuting patterns. Results show the spatial distribution of areas with significant anomalous interactions and the difference between overload- and underload- related anomalous commuting patterns. Potential urban sub-centers were identified to adjust the urban spatial layouts. Besides, the nonlinear and threshold effects of the built environment on the two anomalous commuting patterns were confirmed, which can provide references for urban spatial renewal and commuting flow allocation.

作者关键词: Anomalous commuting patterns; Bipartite network; Built environment; Machine learning model; Non-linear effects

地址: [Tong, Zhaomin; An, Rui; Liu, Yaolin; Chen, Huiting; Xu, Jiwei] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Zhang, Ziyi] East China Univ Technol, Fac Geomatics, 418 Guanglan Rd, Nanchang 330013, Peoples R China.

[Fu, Shihang] Zhejiang Acad Surveying & Mapping, 2 Dixin Rd, Hangzhou 310023, Peoples R China.

通讯作者地址: Liu, YL (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

电子邮件地址: yaolin610@yeah.net

影响因子:5.7