Model of Passenger Comfort Level in Railway Passenger Stations Based on Fusion of PMV Physical Equation and Attention-LSTM Neural Network
The comfort level of railway passenger stations plays a crucial role in enhancing the overall travel experience and satisfaction of passengers.Liaochengxi Railway Station is selected as the object of this study,with three models—PMV physical equation,Attention-LSTM neural network model,and PMV&Attention-LSTM fusion model—being adopted to carry out comprehensive evaluation and analysis on passenger comfort level in railway stations.In the process of modelling,techniques such as standardization processing,dataset partitioning,and grid search cross-validation are used to find the optimal hyperparameters,and the loss function and mean square error in the training process are recorded.The model prediction takes into account a variety of environmental factors,including temperature,humidity,wind speed,air quality,carbon dioxide levels,lighting conditions,and noise levels.Comparing the three prediction methods,the results show that the fusion model provides a more precise reflection of passenger comfort levels,particularly when accounting for multi-dimensional environmental data.This suggest that the fusion model is better suited for the complex environmental conditions found in railway passenger stations and can offer more reliable reference for enhancing comfort in passenger waiting areas.
railway passenger stationpassenger comfortPMVAttention-LSTM neural networkfusion modelliaocheng west railway station
NETL
NSTL
万方数据
