Development and Comparison of Deep Learning and Statistical Models to Predict Bus Passenger Flow
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4852-5_25
Reference20 articles.
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3. Tang J, Zuo A, Liu J, Li T (2022) Seasonal decomposition and combination model for short-term forecasting of subway ridership. Int J Mach Learn Cybern 13(1):145–162. https://doi.org/10.1007/s13042-021-01377-7
4. Zhang J, Chen F, Shen Q (2019) Cluster-based LSTM network for short-term passenger flow forecasting in urban rail transit. IEEE Access 7:147653–147671. https://doi.org/10.1109/ACCESS.2019.2941987
5. Lin Z, Feng J, Lu Z, Li Y, Jin D. DeepSTN+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis [online]. Available www.aaai.org
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