Neural network based temporal feature models for short-term railway passenger demand forecasting
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Tsai et al. (2009) has shown that Neural Networks can be used successfully in short term forecasting.
“Tsai et al. (2009) has shown that Neural Networks can be used successfully in short term forecasting.”
From Passenger demand forecasting in scheduled transportation (2020) · cites this paper as Tsai et al. (2009)
Cites
Phoneme recognition using time-delay neural networks
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.21701 · Cited by 2,643 · see citing works
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A comparative study of machine learning classifiers for modeling travel mode choice
Hagenauer, J., & Helbich, M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.057 · Cited by 424 · see citing works
A real-time passenger flow estimation and prediction method for urban bus transit systems
Zhang, J., Shen, D., Tu, L., Zhang, F., Xu, C., Wang, Y., Tian, C., Li, X., Huang, B., & Li, Z. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2686877 · Cited by 130 · see citing works
Passenger demand forecasting in scheduled transportation
Banerjee, N., Morton, A., & Akartunalı, K. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.10.032 · Cited by 106 · see citing works
Real time transit demand prediction capturing station interactions and impact of special events
Noursalehi, P., Koutsopoulos, H. N., & Zhao, J. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.10.023 · Cited by 71 · see citing works
Bai, Y., Hu, Q., Ho, T. K., Guo, H., & Mao, B. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2954895 · Cited by 33 · see citing works
A two‐stage method for bus passenger load prediction using automatic passenger counting data
Wang, P., Chen, X., Chen, J., Hua, M., & Pu, Z. (2021) · IET Intelligent Transport Systems · doi:10.1049/itr2.12018 · Cited by 28 · see citing works
- Authors
- Tsai, T.-H., Lee, C.-K., & Wei, C.-H.
- Affiliations
- Cornell University (US)
- Southern Taiwan University of Science and Technology (TW)
- National Cheng Kung University (TW)
- Corresponding author
- Tsai, Tsung-Hsien
- ORCID
- Tsai, Tsung-Hsien
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 36
- Issue
- 2
- Pages
- 3728-3736
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1995687640
- Cited by
- 211
- References
- 1
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 8 · 2023: 9 · 2022: 20 · 2021: 29 · 2020: 23 · 2019: 26 · 2018: 21 · 2017: 10 · 2016: 11 · 2015: 12 · 2014: 7 · 2013: 5 · 2012: 4
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Keywords
- short-term forecasting, ensemble forecasting, revenue management, multilayer perceptron comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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