CNN-LSTM and clustering-based spatial–temporal demand forecasting for on-demand ride services
Author:
Funder
Erciyes University Technology Transfer Office
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07681-9.pdf
Reference37 articles.
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2. Li X, Pan G, Wu Z, Qi G, Li S, Zhang D, Zhang W, Wang Z (2012) Prediction of urban human mobility using large-scale taxi traces and its applications. Front Comput Sci China 6(1):111–121. https://doi.org/10.1007/s11704-011-1192-6
3. Moreira-Matias L, Gama J, Ferreira M, Mendes-Moreira J, Damas L (2013) Predicting taxi–passenger demand using streaming data. IEEE Trans Intell Transp Syst 14:1393–1402. https://doi.org/10.1109/TITS.2013.2262376
4. Faghih S, Shah A, Wang Z, Safikhani A, Kamga C (2020) Taxi and mobility: modeling taxi demand using ARMA and linear regression. Procedia Comput Sci 177:186–195. https://doi.org/10.1016/j.procs.2020.10.027
5. Faghih SS, Safikhani A, Moghimi B, Kamga C (2019) Predicting short-term Uber demand in New York city using spatiotemporal modeling. J Comput Civ Eng 33:05019002. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000825
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