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DeepTRANS: A Deep Learning System for Public Bus Travel Time Estimation using Traffic Forecasting

Summary: DeepTRANS extends a DL bus ETA model with predicted traffic forecasts to improve travel-time estimation. By fusing forecasted congestion as exogenous features, it yields about 21% accuracy gains over prior DL ETA baselines, highlighting forecast-informed data fusion for transport analytics. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12362
Venue
VLDB
Year
2020
Pagerank
7.1406582e-05
Overall Rank
3,768 | 74.15%
DOI
10.14778/3415478.3415518

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Authors

BibTeX Citation

@article{tran_vldb20,
        title = {{DeepTRANS: A Deep Learning System for Public Bus Travel Time Estimation using Traffic Forecasting}},
        author = {Tran, Luan and Mun, Min Y. and Lim, Matthew and Yamato, Jonah and Huh, Nathan and Shahabi, Cyrus},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {2957--2960},
        doi = {10.14778/3415478.3415518},
        url = {https://doi.org/10.14778/3415478.3415518},
        year = {2020}
}

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