A Study on Short-Term Passenger Flow Forecasting for Urban Rail Transit
DOI:
https://doi.org/10.54097/grp4s909Keywords:
Forecasting, urban rail transit, short-term passenger traffic.Abstract
Short-term forecasts of light rail traffic are an indispensable basis for adjusting timetables in real time, improving operational efficiency and providing passengers with informed travel recommendations. At the same time, the subway is the most convenient and widely used mode of transport for people today. The purpose of this paper is to forecast passenger traffic in the subway. To solve the problem of predicting short-term passenger flow at the entrances and exits of metro stations with nonlinear and stochastic characteristics, this paper uses a genetic algorithm (GA) based on ordered long-term and short-term memory (SLSTM) and builds a GA-SLSTM forecasting model to predict future short-term passenger flow by studying the patterns and characteristics of passenger flow in historical data. The model consists of five stages: data preprocessing, model training, model evaluation, and forecast application. Through these steps, the GA-SLSTM model is able to predict the future situation of short-term passenger traffic based on the characteristics and patterns in historical data, providing a reference base for planning and optimizing urban transport.
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References
Zhong C, Batty M, Manley E, et al. Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data. PLoS ONE, 2016, 11(2): e0149222.
Niu H, Zhou X. Urban railway timetable optimisation under time-varying demand and congestion.Transportation Research Part C: Emerging Technologies, 2013, (36): 212-230.
Wu J, Liu M, Sun H, et al. Fairness-based schedule synchronisation optimisation in urban metro networks. Transport research part C: new technologies, 2015, (51): 1-18.
TENG Teng, LIU Zhengqi, WANG Xiaomin. Short-term forecasting of urban railway passenger traffic based on GA-SLSTM model. Railway Computer Software, 2022, 31(08): 7-12.
He Xudong. Analysis of urban railway passenger traffic forecasting methods using big data. Science and Technology Information, 2021, 19(30): 11-13.
QIN Linan, DONG Luxi. Research on urban railway passenger traffic forecasting algorithm. Transport Engineering, 2021, 21(01): 40-47.
Keyu Wen, Guotang Zhao, Bisheng He, et al. An information transfer decomposition-based forecasting method for short-distance railway holiday passenger traffic. Expert Systems with Applications, 2022, 189 (3).
Chen S J, Xue Y. Passenger flow prediction for short-distance bus journeys based on an improved convolutional neural network. Computer Science, 2019, 46: 175-184.
Wang Qiuwen, Chen Yanru, Liu Yuanchun. Short-term passenger flow prediction of urban railway travel based on a convolutional neural network with long and short-term memory. Control and Decision Making, 2021, 36: 2760-2770.
Liu Yu et al. Deep learning-based architecture for underground passenger flow prediction. Transportation Research Part C: Emerging Technologies, 2019, (101): 18-34.
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