Have a personal or library account? Click to login
Effect of Spatio-Temporal Granularity on Demand Prediction for Deep Learning Models Cover

Effect of Spatio-Temporal Granularity on Demand Prediction for Deep Learning Models

Open Access
|Feb 2023

References

  1. Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 1–74.10.1186/s40537-021-00444-8801050633816053
  2. Gers, F. A., Eck, D., & Schmidhuber, J. (2002) Applying LSTM to time series predictable through time-window approaches. In: Neural Nets WIRN Vietri-01, 193–200. Springer.10.1007/978-1-4471-0219-9_20
  3. Lara-Benítez, P., Carranza-García, M., & Riquelme, J. C. (2021) An experimental review on deep learning architectures for time series forecasting. International Journal of Neural Systems, 31(03), 2130001.10.1142/S012906572130001133588711
  4. LeCun, Y., Bengio, Y., & Hinton, G. (2015) Deep learning. nature, 521(7553), 436-444. Google Scholar Google Scholar Cross Ref Cross Ref.10.1038/nature14539
  5. Lee, D., Jung, S., Cheon, Y., Kim, D., & You, S. (2019) Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding. ArXiv Preprint ArXiv:1905.10709.
  6. Liu, K., Chen, Z., Yamamoto, T., & Tuo, L. (2022) Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing. ArXiv Preprint ArXiv:2203.10301.
  7. Okutani, I., & Stephanedes, Y. J. (1984) Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological, 18(1), 1–11.10.1016/0191-2615(84)90002-X
  8. Pan, B., Demiryurek, U., & Shahabi, C. (2012) Utilizing real-world transportation data for accurate traffic prediction. In: IEEE 12th International Conference on Data Mining, 595–604.10.1109/ICDM.2012.52
  9. Qi, Y., & Ishak, S. (2014) A Hidden Markov Model for short term prediction of traffic conditions on freeways. Transportation Research Part C: Emerging Technologies, 43, 95–111.10.1016/j.trc.2014.02.007
  10. Schimbinschi, F., Nguyen, X. V., Bailey, J., Leckie, C., Vu, H., & Kotagiri, R. (2015) Traffic forecasting in complex urban networks: Leveraging big data and machine learning. In: 2015 IEEE International Conference on Big Data (Big Data), 1019–1024.10.1109/BigData.2015.7363854
  11. Williams, B. M., & Hoel, L. A. (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering, 129(6), 664–672.10.1061/(ASCE)0733-947X(2003)129:6(664)
  12. Wu, F., Wang, H., & Li, Z. (2016) Interpreting traffic dynamics using ubiquitous urban data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 1–4.10.1145/2996913.2996962
  13. Yao, H., Tang, X., Wei, H., Zheng, G., & Li, Z. (2019) Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5668–5675.10.1609/aaai.v33i01.33015668
DOI: https://doi.org/10.2478/ttj-2023-0003 | Journal eISSN: 1407-6179 | Journal ISSN: 1407-6160
Language: English
Page range: 22 - 32
Published on: Feb 28, 2023
Published by: Transport and Telecommunication Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Ken Koshy Varghese, Sajjad Mahdaviabbasabad, Guido Gentile, Mohamed Eldafrawi, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.