The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Seq2Seq RNNs for Bus Arrival Time Prediction
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.07.001 · Cited by 2,661
How long to wait?: predicting bus arrival time with mobile phone based participatory sensing
Zhou, P., Zheng, Y., & Li, M. (2012) · Proceedings of the 10th international conference on Mobile systems, applications, and services · doi:10.1145/2307636.2307671 · Cited by 348
Travel time prediction with LSTM neural network
Duan, Y., L.V., Y., & Wang, F.-Y. (2016) · 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795686 · Cited by 336
Multi-output bus travel time prediction with convolutional LSTM neural network
Petersen, N. C., Rodrigues, F., & Pereira, F. C. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.028 · Cited by 288
Bus Arrival Time Prediction Using Support Vector Machines
Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2006) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450600981009 · Cited by 268
ETA Prediction with Graph Neural Networks in Google Maps
Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., et al. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3481916 · Cited by 208
Vanajakshi, L., Subramanian, S. C., & Sivanandan, R. (2009) · IET Intelligent Transport Systems · doi:10.1049/iet-its:20080013 · Cited by 178
Experimental Study of Real-Time Bus Arrival Time Prediction with GPS Data
Lin, W.-H., & Zeng, J. (1999) · Transportation Research Record: Journal of the Transportation Research Board · doi:10.3141/1666-12 · Cited by 170
An LSTM-Based Method with Attention Mechanism for Travel Time Prediction
Ran, X., Shan, Z., Fang, Y., & Lin, C. (2019) · Sensors · doi:10.3390/s19040861 · Cited by 148
Cited by
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Metadata
- Authors
- Bhutani, N., Pachal, S., & Achar, A.
- Corresponding author
- Achar, Avinash
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Singapore
- Published
- 2024-11-16
- Pages
- 266-275
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4404399904
- Semantic Scholar
- 4b562f536e54a332a59c597912c32eff4d658ab8
- Cited by
- 0
- References
- 24
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Railway Systems and Energy Efficiency
- Keywords
- bus arrival time prediction, public transport, encoder-decoder architecture, spatio-temporal correlation, traffic congestion, developing countries, real-time prediction
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- CoChat. (2026). Citation check: Seq2Seq RNNs for Bus Arrival Time Prediction. https://app.cochat.ai/papers/10.1007/978-981-96-0026-7_22
- The paper
- Bhutani, N., Pachal, S., & Achar, A. (2025). Seq2Seq RNNs for Bus Arrival Time Prediction. Lecture Notes in Computer Science. https://doi.org/10.1007/978-981-96-0026-7_22
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
