Spatio-Temporal Recursive Method for Traffic Flow Interpolation Article Swipe
Gang Wang
,
Yuhao Mao
,
Xu Liu
,
Haohan Liang
,
Keqiang Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/sym17091577
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/sym17091577
Traffic data sequence imputation plays a crucial role in maintaining the integrity and reliability of transportation analytics and decision-making systems. With the proliferation of sensor technologies and IoT devices, traffic data often contain missing values due to sensor failures, communication issues, or data processing errors. It is necessary to effectively interpolate these missing parts to ensure the correctness of downstream work. Compared with other data, the monitoring data of traffic flow shows significant temporal and spatial correlations. However, most methods have not fully integrated the correlations of these types. In this work, we introduce the Temporal–Spatial Fusion Neural Network (TSFNN), a framework designed to address missing data recovery in transportation monitoring by jointly modeling spatial and temporal patterns. The architecture incorporates a temporal component, implemented with a Recurrent Neural Network (RNN), to learn sequential dependencies, alongside a spatial component, implemented with a Multilayer Perceptron (MLP), to learn spatial correlations. For performance validation, the model was benchmarked against several established methods. Using real-world datasets with varying missing-data ratios, TSFNN consistently delivered more accurate interpolations than all baseline approaches, highlighting the advantage of combining temporal and spatial learning within a single framework.
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- https://www.mdpi.com/2073-8994/17/9/1577/pdf?version=1758435351
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- 23
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https://doi.org/10.3390/sym17091577Digital Object Identifier
- Title
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Spatio-Temporal Recursive Method for Traffic Flow InterpolationWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-21Full publication date if available
- Authors
-
Gang Wang, Yuhao Mao, Xu Liu, Haohan Liang, Keqiang LiList of authors in order
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goldOpen access status per OpenAlex
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0Total citation count in OpenAlex
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23Number of works referenced by this work
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| abstract_inverted_index.highlighting | 177 |
| abstract_inverted_index.incorporates | 120 |
| abstract_inverted_index.missing-data | 165 |
| abstract_inverted_index.technologies | 25 |
| abstract_inverted_index.communication | 39 |
| abstract_inverted_index.correlations. | 76, 148 |
| abstract_inverted_index.dependencies, | 134 |
| abstract_inverted_index.proliferation | 22 |
| abstract_inverted_index.interpolations | 172 |
| abstract_inverted_index.transportation | 15, 109 |
| abstract_inverted_index.decision-making | 18 |
| abstract_inverted_index.Temporal–Spatial | 95 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5101368869 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I82880672 |
| citation_normalized_percentile.value | 0.50630043 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |