Safety accidents occure frequently during metro construction, which are mainly caused by human factors, and the incidence of accidents can be increased due to the overlap of human factors and physical factors. The hum...Safety accidents occure frequently during metro construction, which are mainly caused by human factors, and the incidence of accidents can be increased due to the overlap of human factors and physical factors. The human factors are taken as breakthrough to make early warning for the human insecurity factors in the metro construction, which can effectively reduce the occurrence of safety accidents. This paper proposes the principle of total monitoring and early-warning management. The unsafe behaviors in metro construction when approaching the hazardous area and non-standard safety prevention measures are analyzed to design and model the early warning process of unsafe behaviors in metro construction. Finally, the model is analyzed and verified using actual examples.展开更多
This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfac...This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.展开更多
基金Supported by the National Natural Science Foundation of China(71603284)the Humanity and Social Science Research Foundation of Ministry of Education PRC(16YJC630068)the China Postdoctoral Science Foundation(2019T120718,2018M630918).
文摘Safety accidents occure frequently during metro construction, which are mainly caused by human factors, and the incidence of accidents can be increased due to the overlap of human factors and physical factors. The human factors are taken as breakthrough to make early warning for the human insecurity factors in the metro construction, which can effectively reduce the occurrence of safety accidents. This paper proposes the principle of total monitoring and early-warning management. The unsafe behaviors in metro construction when approaching the hazardous area and non-standard safety prevention measures are analyzed to design and model the early warning process of unsafe behaviors in metro construction. Finally, the model is analyzed and verified using actual examples.
文摘This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.
