Опубликована: Янв. 1, 2024
Язык: Английский
Building Simulation, Год журнала: 2025, Номер unknown
Опубликована: Янв. 25, 2025
Язык: Английский
Процитировано
2
The Science of The Total Environment, Год журнала: 2025, Номер 965, С. 178578 - 178578
Опубликована: Янв. 31, 2025
Язык: Английский
Процитировано
0
Journal of Hazardous Materials, Год журнала: 2025, Номер 488, С. 137475 - 137475
Опубликована: Фев. 2, 2025
Язык: Английский
Процитировано
0
Applied Sciences, Год журнала: 2025, Номер 15(10), С. 5367 - 5367
Опубликована: Май 12, 2025
This study presents an artificial intelligence-based approach for the pose detection of passengers’ skeletons when boarding and alighting from urban bus in Valparaíso, Chile. Using AlphaPose estimator activity recognition model based on Random Forest, video data were processed to analyze poses activities passengers. The results obtained allow evaluation safety ergonomics public transportation, providing valuable information improving design accessibility buses. not only enhances understanding passenger behavior but also contributes optimization systems accommodate diverse needs, ensuring a safer more comfortable environment all users. accurately estimates posture passengers, offering insights into their movements interacting with bus. In addition, Forest recognizes variety activities, walking sitting, helping how passengers interact space. analysis helps identify areas where improvements can be made terms accessibility, comfort, safety, contributing overall transport systems. opens up new possibilities AI-driven transportation serve as foundation future planning.
Язык: Английский
Процитировано
0
Sustainability, Год журнала: 2024, Номер 16(17), С. 7400 - 7400
Опубликована: Авг. 28, 2024
The process of urbanization has facilitated the exponential growth in demand for road traffic, consequently leading to substantial emissions CO2 and pollutants. However, with development expansion network, distribution emission characteristics pollutant are still unclear. In this study, a bottom-up approach was initially employed develop high-resolution inventories (NOx, CO, HC) from primary, secondary, trunk, tertiary roads rapidly urbanizing regions China based on localized factor data. Subsequently, standard length method utilized analyze spatiotemporal across different networks while exploring their heterogeneity. Finally, influence elevation surface vegetation cover traffic-related taken into consideration. results indicated that CO2, HC, NOx increased significantly 2020 compared those 2017 trunk roads, Fuzhou uneven; 2017, areas high were predominantly concentrated central low coverage levels topography but expanded 2020. This study enhances our comprehension variations carbon resulting regional network expansion, offering valuable insights case studies worldwide undergoing similar infrastructure development.
Язык: Английский
Процитировано
1
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0