Estimating road speed classes: Integrating OpenStreetMap and Street View imagery for missing data imputation
Traffic speed is a significant indicator for evaluating road network performance and supporting intelligent transportation systems, as it informs congestion management, routing, and operational decisions. Although traffic information is available from commercial platforms and sensor-based monitoring...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2026
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| In: |
Computers, environment and urban systems
Year: 2026, Volume: 125, Pages: 1-12 |
| DOI: | 10.1016/j.compenvurbsys.2025.102392 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.compenvurbsys.2025.102392 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0198971525001450 
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| Author Notes: | Shiyu Tang, Sukanya Randhawa, Jin Rui, Christina Ludwig, Steffen Knoblauch, Charles Hatfield, Alexander Zipf |
| Summary: | Traffic speed is a significant indicator for evaluating road network performance and supporting intelligent transportation systems, as it informs congestion management, routing, and operational decisions. Although traffic information is available from commercial platforms and sensor-based monitoring systems, such data are often costly, proprietary, or spatially limited, which restricts their broader usability. To overcome these limitations, we designed a spatial prediction model based on the Graph Sample and Aggregation (GraphSAGE) to infer traffic speeds in unobserved areas. Instead of predicting continuous speed values, we classified traffic into speed classes, which enhanced model robustness in the absence of historical observations and better reflected long-term typical traffic patterns relevant to downstream applications such as routing, emission assessment, and traffic management. Taking Berlin as a case study, the model incorporated multi-source features, including topological features, OpenStreetMap-based road features, and semantic Street View imagery indicators. Uber Movement average speed data were used as supervised learning labels. Results showed that the multi-source feature fusion improved the prediction performance, with the F1 score increasing from 0.6228 to 0.6917. Feature analysis revealed that OSM contextual features contributed the most under limited label coverage, while Street View imagery added complementary information to facilitate model discrimination. Despite only 28 % of road segments being covered by Uber observations, similar feature patterns between labeled and unlabeled areas enabled the model to generalize and infer missing speed data citywide. The framework makes scalable and low-cost speed class inference available for urban traffic monitoring and modeling. |
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| Item Description: | Online veröffentlicht: 17 December 2025, Gesehen am 29.05.2026 |
| Physical Description: | Online Resource |
| DOI: | 10.1016/j.compenvurbsys.2025.102392 |