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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree
Longzhu Xiao, Siuming Lo, Jixiang Liu, et al.
Sustainable Cities and Society (2021) Vol. 72, pp. 103063-103063
Closed Access | Times Cited: 141

Showing 1-25 of 141 citing articles:

Variations in outdoor thermal comfort in an urban park in the hot-summer and cold-winter region of China
Dong Wei, Linchuan Yang, Zhikang Bao, et al.
Sustainable Cities and Society (2021) Vol. 77, pp. 103535-103535
Closed Access | Times Cited: 136
Exploring the spatiotemporal patterns and correlates of urban vitality: Temporal and spatial heterogeneity
Yang Chen, Bingjie Yu, Bo Shu, et al.
Sustainable Cities and Society (2023) Vol. 91, pp. 104440-104440
Closed Access | Times Cited: 74
Using street view images to examine the association between human perceptions of locale and urban vitality in Shenzhen, China
Chao Wu, Yu Ye, Fanzong Gao, et al.
Sustainable Cities and Society (2022) Vol. 88, pp. 104291-104291
Closed Access | Times Cited: 73
Exploring non-linear and synergistic effects of green spaces on active travel using crowdsourced data and interpretable machine learning
Linchuan Yang, Haosen Yang, Bingjie Yu, et al.
Travel Behaviour and Society (2023) Vol. 34, pp. 100673-100673
Closed Access | Times Cited: 60
Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data
Wei Yang, Yingpeng Li, Yong Liu, et al.
Landscape and Urban Planning (2023) Vol. 243, pp. 104969-104969
Closed Access | Times Cited: 57
Exploring nonlinear effects of built environment on jogging behavior using random forest
Yong Liu, Yingpeng Li, Wei Yang, et al.
Applied Geography (2023) Vol. 156, pp. 102990-102990
Closed Access | Times Cited: 53
Nonlinear relationships and interaction effects of an urban environment on crime incidence: Application of urban big data and an interpretable machine learning method
Sunjae Kim, Sugie Lee
Sustainable Cities and Society (2023) Vol. 91, pp. 104419-104419
Closed Access | Times Cited: 44
Unraveling nonlinear and interaction effects of multilevel built environment features on outdoor jogging with explainable machine learning
Wei Yang, Jun Fei, Yingpeng Li, et al.
Cities (2024) Vol. 147, pp. 104813-104813
Closed Access | Times Cited: 24
Simulating influences of land use/land cover composition and configuration on urban heat island using machine learning
Yong Liu, Zihao An, Yujia Ming
Sustainable Cities and Society (2024) Vol. 108, pp. 105482-105482
Closed Access | Times Cited: 20
Examining active travel behavior through explainable machine learning: Insights from Beijing, China
Ganmin Yin, Zhou Huang, Chen Fu, et al.
Transportation Research Part D Transport and Environment (2024) Vol. 127, pp. 104038-104038
Closed Access | Times Cited: 16
Nonlinear and threshold effects of the built environment, road vehicles and air pollution on urban vitality
Quang Cuong Doan, Jun Ma, Shuting Chen, et al.
Landscape and Urban Planning (2024) Vol. 253, pp. 105204-105204
Closed Access | Times Cited: 16
Revitalizing historic districts: Identifying built environment predictors for street vibrancy based on urban sensor data
Miaoyi Li, Liu Ji-xiang, Yifei Lin, et al.
Cities (2021) Vol. 117, pp. 103305-103305
Closed Access | Times Cited: 58
Gender differences in active travel among older adults: Non-linear built environment insights
Yongjiang Yang, Kuniaki Sasaki, Long Cheng, et al.
Transportation Research Part D Transport and Environment (2022) Vol. 110, pp. 103405-103405
Open Access | Times Cited: 56
Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning
Shiliang Su, Chong Zhao, Hao Zhou, et al.
Journal of Transport Geography (2022) Vol. 100, pp. 103308-103308
Closed Access | Times Cited: 55
Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level
Caigang Zhuang, Shaoying Li, Zhangzhi Tan, et al.
Journal of Transport Geography (2022) Vol. 102, pp. 103375-103375
Closed Access | Times Cited: 51
Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications
Shiliang Su, Zhuolun Wang, Bozhao Li, et al.
Journal of Transport Geography (2022) Vol. 104, pp. 103455-103455
Closed Access | Times Cited: 40
Using machine-learning models to understand nonlinear relationships between land use and travel
Xinyu Cao, Tao Tao
Transportation Research Part D Transport and Environment (2023) Vol. 123, pp. 103930-103930
Closed Access | Times Cited: 39
Vital triangle: A new concept to evaluate urban vitality
Haimeng Liu, Peng Gou, Jieyang Xiong
Computers Environment and Urban Systems (2022) Vol. 98, pp. 101886-101886
Closed Access | Times Cited: 38
Nonlinear effects of built environment features on metro ridership: An integrated exploration with machine learning considering spatial heterogeneity
Mengyang Liu, Yuxuan Liu, Yu Ye
Sustainable Cities and Society (2023) Vol. 95, pp. 104613-104613
Closed Access | Times Cited: 29
Portraying business district vibrancy with mobile phone data and optimal parameters-based geographical detector model
Feng Gao, Xingdong Deng, Shunyi Liao, et al.
Sustainable Cities and Society (2023) Vol. 96, pp. 104635-104635
Closed Access | Times Cited: 29
Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models
Songhua Hu, Chenfeng Xiong, Peng Chen, et al.
Transportation Research Part A Policy and Practice (2023) Vol. 174, pp. 103743-103743
Closed Access | Times Cited: 27
Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: A systematic review
Mahdi Aghaabbasi, Saksith Chalermpong
Travel Behaviour and Society (2023) Vol. 33, pp. 100640-100640
Closed Access | Times Cited: 25
Pedestrian movement with large-scale GPS records and transit-oriented development attributes
Sunyong Eom, Hongjik Kim, Daisuke Hasegawa, et al.
Sustainable Cities and Society (2024) Vol. 102, pp. 105223-105223
Closed Access | Times Cited: 11
Identification of 71 factors influencing urban vitality and examination of their spatial dependence: A comprehensive validation applying multiple machine-learning models
Zhenyu Wang, Xinzhu Wang, Yida Liu, et al.
Sustainable Cities and Society (2024) Vol. 108, pp. 105491-105491
Closed Access | Times Cited: 11
Unraveling nonlinear and spatial non-stationary effects of urban form on surface urban heat islands using explainable spatial machine learning
Yujia Ming, Yong Liu, Yingpeng Li, et al.
Computers Environment and Urban Systems (2024) Vol. 114, pp. 102200-102200
Closed Access | Times Cited: 10
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