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Exploring the Lagged Effect of Rainfall on Urban Rail Transit Passenger Flow: A Case Study of Guangzhou, China

https://doi.org/10.2139/ssrn.5044935
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A hybrid deep learning based traffic flow prediction method and its understanding
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DEASeq2Seq: An attention based sequence to sequence model for short-term metro passenger flow prediction within decomposition-ensemble strategy
inferredcites →https://doi.org/10.1061/jtepbs.0000383openalex-snapshot
Prediction of Urban Rail Transit Ridership under Rainfall Weather Conditions
inferredcites →https://doi.org/10.1080/08839514.2017.1296682openalex-snapshot
Passenger Flow Prediction Based on Newly Adopted Algorithms
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Predicting incident duration using random forests
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Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks
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A Novel Car-Following Control Model Combining Machine Learning and Kinematics Models for Automated Vehicles
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Subway Passenger Flow Prediction for Special Events Using Smart Card Data
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Estimation of Influence Distance of Bus Stops Using Bus GPS Data and Bus Stop Properties
inferredcites →https://doi.org/10.1109/tits.2020.3004254openalex-snapshot
Automatic Feature Engineering for Bus Passenger Flow Prediction Based on Modular Convolutional Neural Network
inferredcites →https://doi.org/10.1371/journal.pone.0222365openalex-snapshot
Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow
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A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions
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