Study on Occupancy Behaviors of Passengers in the Subway Cabin: An Observation in Chengdu, China

Abstract

Insufficient attention has been paid to how subway cabins are used by passengers and especially the distribution of passengers and occupancy of facilities. In this study, passengers were observed in 133 sections from the beginning to the end of the early peak of Chengdu Metro in the working days. The differences in occupancy behaviors of passengers to different areas, seats, and standing auxiliary facilities in the cabin were analyzed by the nonparametric test. The occupancy curve was fitted by the least square method from the minimum to the maximum load factor, and the prediction and explanatory model for the use of cabin was established. As expected, the distribution of passengers in the cabin is uneven. The highest occupancy rate has been maintained at the cabin end. Female passengers accounted for the largest proportion in the door area, while male passengers accounted for a larger proportion at the end of the cabin. There is no difference in the use of different seat types by passengers. There are more seats in female passengers, and females are more likely to get the remaining seats when the seat load is nearly saturated. For the auxiliary standing facilities, there are always passengers who do not use the facilities and the proportion is increasing. The facilities that can be relied on account for a greater median proportion of the passengers with facilities, but service capacity is limited. In response to these conclusions, measures to improve the design of the cabin are proposed.

The Effect of Seat Layout on the Interaction of Passengers Inside the Train Carriage: An Experimental Approach for Urban Services

“…Recent studies have shown the distribution of passengers and their interaction based on variables such as queue length at the door, number of people inside the carriage, travel distance, and comfort of passengers [31]. The space of passengers and the occupation inside the train are reported in [32,33], which are essential factors that affect the interaction of passengers boarding or alighting. The characteristics of passengers are also studied in [34] utilizing laboratory experiments.…”

Intelligent Systems Integrating BIM and VR for Urban Subway Microenvironmental Health Risks Management

“…Urban subways, renowned for their speed and capacity, are prevalent across 79 countries, including Japan, the United States, Korea, China, Brazil, Chile, and Colombia [1,2]. Globally, subway networks spanned approximately 36,854.20 km in 2021, facilitating nearly 39.67 billion passenger journeys [3].…”