Coupled Impact of Points of Interest and Thermal Environment on Outdoor Human Behavior Using Visual Intelligence DOI Creative Commons

Shiliang Wang,

Qun Zhang, Peng Gao

и другие.

Buildings, Год журнала: 2024, Номер 14(9), С. 2978 - 2978

Опубликована: Сен. 20, 2024

Although it is well established that thermal environments significantly influence travel behavior, the synergistic effects of points interest (POI) and on behavior remain unclear. This study developed a vision-based outdoor evaluation model aimed at uncovering driving factors behind human in spaces. First, Yolo v5 questionnaires were employed to obtain crowd activity intensity preference levels. Subsequently, target detection clustering algorithms used derive variables such as POI attractiveness distance, while validated environmental simulator was utilized simulate comfort distributions across different times. Finally, multiple classification models compared establish mapping relationships between POI, environment variables, preferences, with SHAP analysis examine contribution each variable. The results indicate XGBoost achieved best predictive performance (accuracy = 0.95), shadow proportion (|SHAP| 0.24) distance 0.12) identified most significant influencing preferences. By extrapolation, this can provide valuable insights for optimizing community enhancing vitality areas similar climatic cultural contexts.

Язык: Английский

Dynamic multi-scale effect of urban spatial form on PM 2.5 concentrations in different climate zones of China DOI

Yuqiu Jia,

Meixia Lin,

Huishi Du

и другие.

International Journal of Sustainable Development & World Ecology, Год журнала: 2025, Номер unknown, С. 1 - 20

Опубликована: Май 7, 2025

Язык: Английский

Процитировано

0

Tabular Prior-Data Fitted Network for Urban Air Temperature Inference and High Temperature Risk Assessment DOI
Zhongqi Yu, Rong Yu,

Xinyi Ge

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106484 - 106484

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Comparison of mediating effects of air pollutants on urban morphology and urban heat Island intensity at block scale DOI Creative Commons
Jiayu Fan, Xuegang Chen, Weihong Zhang

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 26, 2025

Язык: Английский

Процитировано

0

Global vs. Local Heat Patches: toward a more comprehensive perspective on managing urban overheating—A Shanghai case study DOI
Jiaxing Ye, Xiong Yao, Xing Shi

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106503 - 106503

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

A 3D perspective for understanding the mechanisms of urban heat island and urban morphology using multi-modal geospatial data and interpretable machine learning DOI
Ting Han, Chenxi Du,

Yijia Xie

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 113184 - 113184

Опубликована: Июнь 1, 2025

Язык: Английский

Процитировано

0

Assessment of the impact of urban block morphological factors on carbon emissions introducing the different context of local climate zones DOI
Yuchen Qin, Jian Kang,

Haizhu Zhou

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер unknown, С. 106073 - 106073

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

2

Coupled Impact of Points of Interest and Thermal Environment on Outdoor Human Behavior Using Visual Intelligence DOI Creative Commons

Shiliang Wang,

Qun Zhang, Peng Gao

и другие.

Buildings, Год журнала: 2024, Номер 14(9), С. 2978 - 2978

Опубликована: Сен. 20, 2024

Although it is well established that thermal environments significantly influence travel behavior, the synergistic effects of points interest (POI) and on behavior remain unclear. This study developed a vision-based outdoor evaluation model aimed at uncovering driving factors behind human in spaces. First, Yolo v5 questionnaires were employed to obtain crowd activity intensity preference levels. Subsequently, target detection clustering algorithms used derive variables such as POI attractiveness distance, while validated environmental simulator was utilized simulate comfort distributions across different times. Finally, multiple classification models compared establish mapping relationships between POI, environment variables, preferences, with SHAP analysis examine contribution each variable. The results indicate XGBoost achieved best predictive performance (accuracy = 0.95), shadow proportion (|SHAP| 0.24) distance 0.12) identified most significant influencing preferences. By extrapolation, this can provide valuable insights for optimizing community enhancing vitality areas similar climatic cultural contexts.

Язык: Английский

Процитировано

1