A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction-Reference-Cited by-同舟云学术

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A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction

Published:2020 Issue: Volume: Page:100-116
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Quy Tai Le,Nejdl Wolfgang,Spiliopoulou Myra,Ntoutsi Eirini

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-38081-6_8

Reference28 articles.

1. Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961–971 (2016)

2. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)

3. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

4. Fafalios, P., Iosifidis, V., Stefanidis, K., Ntoutsi, E.: Tracking the history and evolution of entities: entity-centric temporal analysis of large social media archives. Int. J. Digital Libr. 1–13 (2018)

5. Flores, B.E.: A pragmatic view of accuracy measurement in forecasting. Omega 14(2), 93–98 (1986)

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3. Taxi‐demand forecasting using dynamic spatiotemporal analysis;ETRI Journal;2022-07-21

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