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Short-term traffic forecasting: Where we are and where we’re going
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“So capturing these multi-step temporal relationships is critical to ETA prediction (Vlahogianni et al., 2014).”
From Real-time bus arrival prediction and bus bunching prevention using a multi-agent LSTM approach (2026) · cites this paper as Vlahogianni et al., 2014
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“Transit vehicle trajectories have sequential patterns: the time to the next stop is dependent not only on the current location and speed but also on the past vehicle momentum-acceleration patterns through past stop services, traffic signal cycles, and dwell time carryovers.”
From Real-time bus arrival prediction and bus bunching prevention using a multi-agent LSTM approach (2026) · cites this paper as Vlahogianni et al., 2014
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Data fusion in intelligent transportation systems: Progress and challenges – A survey
Faouzi, N.-E. E., Leung, H., & Kurian, A. (2011) · Information Fusion · doi:10.1016/j.inffus.2010.06.001 · Cited by 444
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Metadata
- Authors
- Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 43
- Pages
- 3-19
- ISSN
- 0968-090X
- Cited by
- 1,132
- References
- 1
- Language
- en
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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.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.