As the living fabric that connects urban places, streets play an important role in driving urban development, providing essential access and fostering human interactions.
Understanding pedestrian activities and how these activities vary in different streets is critical to the design of efficient as well as liveable streets. However, current street classification frameworks focus primarily on the functions of streets in transportation networks rather than actual activity patterns. And that results in coarse classifications.
This research proposes an activity-based street classification framework to categorize street segments based on their temporal pedestrian activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. The results show that a street classification framework based on temporal pedestrian activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning.
Icon image: Piqsels- Pedestrian