Research on Deep Learning - Empowered Intelligent Passenger Flow Control for Fully Automatic Metro Operation Systems |
| Xinzhen Feng Rui Gu Lianxia Wang Duolong Wang Zeying Li |
| 1. Tianjin Municipal Engineering School, Tianjin 300252, China；2. Tianjin Line 1 Rail Transit Operation Co., Ltd., Nankai District, Tianjin 300110, China；3. China Classification Society Quality Assurance Co., Ltd. Tianjin Branch, Tianjin 300457, China |
| Abstract:With the widespread application of Fully Automatic Operation (FAO) systems in urban rail transit, passenger flow control has become a key factor in ensuring their efficient and safe operation. This paper delves into the challenges of passenger flow control in FAO systems by leveraging deep learning technologies. It constructs deep learning models, integrates multi - source data, designs intelligent control strategies, and proposes an efficient intelligent passenger flow control scheme. The aim is to help improve the service quality and operational efficiency of the system and promote the sustainable development of urban rail transit. |
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Keywords：Fully Automatic Operation System; Passenger Flow Control; Deep Learning; Multi - Source Data Fusion;
Intelligent Control Strategy
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