Abstract:Traffic simulations, essential for planning urban transit infrastructure interventions, require vehicle-category-specific origin-destination (OD) data. Existing data sources are imperfect: sparse tollbooth sensors provide accurate vehicle counts by category, while extensive mobility data from cellular network activity captures aggregated crowd movement, but lack modal disaggregation and have systematic biases. This study develops a machine learning framework to correct and disaggregate cellular network data using sparse tollbooth counts as ground truth. The model uses temporal and spatial features to learn the complex relationship between aggregated mobility data and vehicular data. The framework infers destinations from transit routes and implements routing logic to distribute corrected flows between OD pairs. This approach is applied to a bus depot expansion in Trondheim, Norway, generating hourly OD matrices by vehicle length category. The results show how limited but accurate sensor measurements can correct extensive but aggregated mobility data to produce grounded estimates of background vehicular traffic flows. These macro-scale estimates can be refined for micro-scale analysis at desired locations. The framework provides a generalisable approach for generating origin-destination data from cellular network data. This enables downstream tasks, like detailed traffic simulations for infrastructure planning in data-scarce contexts, supporting urban planners in making informed decisions.
| Comments: | 8 pages, 7 figures |
| Subjects: | Machine Learning (cs.LG); Physics and Society (physics.soc-ph) |
| Cite as: | arXiv:2604.15782 [cs.LG] |
| (or arXiv:2604.15782v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.15782 arXiv-issued DOI via DataCite (pending registration) |
From: Oluwaleke Yusuf [view email]
[v1]
Fri, 17 Apr 2026 07:40:10 UTC (3,860 KB)
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