Applying Transformer-Based Dynamic-Sequence Techniques to Transit Data Analysis Article Swipe
Byoung-Sik Choo
,
Dong‐Kyu Kim
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/engproc2025102012
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/engproc2025102012
Transit systems play a vital role in urban mobility, yet predicting individual travel behavior within these systems remains a complex challenge. Traditional machine learning approaches struggle with transit trip data because each trip may consist of a variable number of transit legs, leading to missing data and inconsistencies when using fixed-length tabular representations. To address this issue, we propose a transformer-based dynamic-sequence approach that models transit trips as variable-length sequences, allowing for flexible representation while leveraging the power of attention mechanisms. Our methodology constructs trip sequences by encoding each transit leg as a token, incorporating travel time, mode of transport, and a 2D positional encoding based on grid-based spatial coordinates. By dynamically skipping missing legs instead of imputing artificial values, our approach maintains data integrity and prevents bias. The transformer model then processes these sequences using self-attention, effectively capturing relationships across different trip segments and spatial patterns. To evaluate the effectiveness of our approach, we train the model on a dataset of urban transit trips and predict first-mile and last-mile travel times. We assess performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Experimental results demonstrate that our dynamic-sequence method yields up to a 30.96% improvement in accuracy compared to non-dynamic methods while preserving the underlying structure of transit trips. This study contributes to intelligent transportation systems by presenting a robust, adaptable framework for modeling real-world transit data. Our findings highlight the advantages of self-attention-based architectures for handling irregular trip structures, offering a novel perspective on a data-driven understanding of individual travel behavior.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/engproc2025102012
- https://www.mdpi.com/2673-4591/102/1/12/pdf?version=1754563706
- OA Status
- gold
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413039483
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413039483Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/engproc2025102012Digital Object Identifier
- Title
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Applying Transformer-Based Dynamic-Sequence Techniques to Transit Data AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-07Full publication date if available
- Authors
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Byoung-Sik Choo, Dong‐Kyu KimList of authors in order
- Landing page
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https://doi.org/10.3390/engproc2025102012Publisher landing page
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https://www.mdpi.com/2673-4591/102/1/12/pdf?version=1754563706Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-4591/102/1/12/pdf?version=1754563706Direct OA link when available
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Computer science, Transformer, Sequence (biology), Engineering, Voltage, Electrical engineering, Genetics, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.transformer-based | 60 |
| abstract_inverted_index.self-attention-based | 237 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100377978 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I139264467 |
| citation_normalized_percentile.value | 0.30688457 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |