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Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets’ functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal street activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic street activities on streets, which complements existing street classification frameworks, which focus on the static or transportation characteristics of the street segments. Our results show that a street classification framework based on temporal street 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. In particular, we find that our classification distinguishes better those streets where crime is more prevalent than current functional or contextual classifications of streets.