Interactive Project · Street Equity & Inclusiveness
iWho gets to use the street? · Haidian, Beijing
What if a city could see itself through the eyes of the people who move through it all day? EgoCity reads Beijing’s streets from cameras carried by food-delivery riders — turning 220 hours of everyday rides into a fine-grained portrait of who actually uses public space, and who is left out.
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The question
Streets are the most ubiquitous public space we have — yet we rarely measure whether they actually serve everyone. EgoCity asks two things of every block: is it fully and diversely used (utilization), and does it serve all age groups fairly (inclusiveness)? A vibrant street that only adults occupy is busy, but not just.
Interactive · In progress
The animated EgoCity routes — eight riders sweeping the district over three weeks — will play here.
The method
Eight food-delivery riders carried portable cameras across Haidian over three weeks — yielding 220+ hours of video and 118,240 anonymized street frames from a pedestrian’s-eye view.
A hybrid vision-language model detects each person and labels them child / adult / elderly plus six activities — at 0.96 F1, outperforming GPT-4o and Gemini-1.5-Pro.
On 100 m grids, we measure Utilization (volume × activity diversity) and Inclusiveness (how evenly age groups appear, relative to who lives there).
Individual level
Of 91,524 people read from the frames, the street skews overwhelmingly adult. Set against who actually lives in Haidian, the gap is stark: the elderly are nearly one in five residents but barely one in forty people on the street.
What people do
Across the six recognized activities, walking dominates and lingering is common; talking is occasional and phone-calling almost absent. Bars show the share of street grids where each activity appears. (Mobile browsing and dog-walking sit between talking and calling.)
What we found
96% of detected pedestrians were adults; children and the elderly were strikingly underrepresented relative to their share of residents.
Pedestrian flow and residential population were spatially mismatched — dense residential streets stayed quiet, while moderate-density areas like Zhongguancun buzzed.
High utilization clustered in a few cores (Zhongguancun–Haidian, Balizhuang), while peripheries such as Ganjiakou and Lugouqiao stayed persistently low.
Very few grids were both highly used and highly inclusive — the liveliest streets were often the least balanced across age groups.
Why it matters
By riding along with workers already moving through the city, EgoCity captures fine-grained, pedestrian-eye data at a fraction of the usual cost — and gives planners a way to spot streets that are busy but exclusionary, and to revive under-used residential blocks, toward streets that hold children and elders, not just adults.
Project
EgoCity reads Beijing’s public streets from the cameras of food-delivery riders. Pairing a crowdsourced, pedestrian-eye video dataset with a custom vision-language model, it measures who actually uses the street — and how fairly space is shared across children, adults, and the elderly.
Team
A Nature AI Lab project.
Publication
Zhang, X., Chen, M., Huang, Y. (2025). Who gets to use the street? Evaluate the utilization and inclusiveness using crowdsourced videos and vision-language models. Sustainable Cities and Society.
Use & Credit
The visualizations on this page may be reused in any publication provided that:
For more information, mingze.chen@ubc.ca