Using publicly available data for predicting socioeconomic values in urban context
Abstract Urban transportation networks are recognized for their pivotal role in forecasting city indicators and facilitating efficient planning and management. However, despite the increase of methodologies and models harnessing machine learning advancement to forecast these values, challenges persi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Computational Urban Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43762-025-00192-y |
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