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Neural Network-Based Prediction Model for Passenger Flow in a Large Passenger Station: An Exploratory Study

机译：大型客运站乘客流量的基于神经网络的预测模型：探索性研究

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摘要

As the hub and carrier to transfer the passengers, the railway station is an important factor that affects the rail passenger transportation because the normal operation of the station without load redundancy is determined by the moderate passenger flow. It means reasonable and accurate prediction of passengers entering and leaving the station can provide the basis and guarantee for the station security, the resources allocation and the personnel deployment. Since the neural network model is good at processing the common regular data changes through training the network and adjusting the weight value based on a large number of training samples, the neural network model is used in processing the short-term irregular data to predict the passenger flow at the railway station which is susceptible to the constantly changing external factors. In this paper, the neural network is used to predict the passenger flow. First, the key factors affecting the change of the passenger flow are selected and analyzed as the input of the neural network. Second, the learning and the rate updating of variable step size are adopted to estimate the number people entering the station during a certain time interval, which is then weighted with the historical data to derive the prediction of the passenger flow during the next time interval. The simulation results show that the experiment results show that the method proposed in this paper can better track and predict the sudden changes in the passenger flow caused by emergencies. Meanwhile, it can be found that in the process of forecasting abnormal passenger flow, the most critical link is to summarize and summarize the characteristics of railway station passenger flow, clarify the type and time distribution of passenger flow at each station, and analyze the factors that cause abnormal changes in passenger flow.
机译：作为转移乘客的集线器和载体，火车站是影响铁路乘客运输的重要因素，因为没有负载冗余的站的正常运行由中等客流决定。这意味着对进入和离开该站的乘客的合理和准确的预测可以为站安全，资源分配和人员部署提供基础和保证。由于神经网络模型良好地处理公共常规数据通过训练网络和基于大量训练样本来调整权重值，因此神经网络模型用于处理短期不规则数据以预测乘客在火车站流动，易于不断变化的外部因素。在本文中，神经网络用于预测乘客流量。首先，选择影响乘客流程变化的关键因素，并分析为神经网络的输入。其次，采用了学习和速率更新的可变步长的速率来估计在一定时间间隔期间进入站的数量人员，然后随后用历史数据加权，以导出在下次间隔期间的乘客流预测。仿真结果表明，实验结果表明，本文提出的方法可以更好地追踪并预测紧急情况造成的乘客流动的突然变化。同时，可以发现，在预测异常客流的过程中，最关键的联系是总结和总结了火车站客流的特点，阐明了每个站的乘客流量的类型和时间分布，并分析了因素这导致客运流的异常变化。

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