地铁换乘客流实时监测模型研究

摘要

Metro transfer station is an important node for urban rail transit network. Its transfer efficiency directly affects the transport capacity. With the metro interchange passenger flow characterized by high randomness and short-term impact as the focus, the structure of subway network was analyzed in this paper, and a time-diminishing and real-time monitoring model was proposed. The model was solved by BP neural network algorithm. A typical subway station of Nanjing metro was selected to verify the model, and the simulation results showed that the model is reliable.%针对地铁换乘客流随机性强及短时冲击性等特点,在分析地铁线网结构的基础上,提出基于时序倒推的地铁换乘客流实时监测模型,并采用BP神经网络算法进行模型求解.选取南京地铁典型换乘站对所述方法进行分析验证,进一步说明模型的合理性和可行性.