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Urban planning has focused on reallocating road space from automobile to more sustainable transport modes in many cities worldwide. Mostly in urban areas, road space (from façade to façade) is highly disputed by different urban activities and functions. Nonetheless, there are varying demand periods during the day in which road space is underutilized due to its static design. Underutilized spaces could be used for other mobility or access purposes to improve efficiency. Sensing road space, using big data and transport demand management tools, may characterize different demand patterns, adapt the road space dynamically and, ultimately, promote efficiency in using a scarce resource, such as urban road space. This approach also reinforces short-term flexibility in urban planning, allowing for better responses to unpredictable events. This paper defines the concept of dynamic road space allocation by discussing the previous literature on dynamic allocation of space. We propose a methodological framework and discuss the technological solutions as well as the many challenges of implementing dynamic road space allocation.
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Stored only when both papers are in the corpus — 27 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.