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I would like to create a sttistical model to estimate the number of people passing by a street each day/time of the day. I have been looking at some certain parameters given by google maps API. I have also looked at Fermi estimation but for vehicles. I want to look at pedestrians. Any ideas? Any interesting papers on the matter? books? Theory?
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Look into object tracking, it's probably what you will end up needing.
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Some cities have engineering data on intersections for traffic lights and you can get a ballpark probably from those tiers. Never done this before. Chicago or some top 3 city in CA might have this data…

Chicago has traffic violations data… not sure this helps you tho. Big take away from that data during an interview I had that impressed the crowd is that most of those major violation areas are within 3 blocks of interstates or right off the interstates I- 90/290. Not all but most
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Find google maps busyness estimates for a Starbucks nearby. That’s the shape of your distribution.

Now you only need to estimate magnitude. Easiest option is prob to assume some function of Starbucks foot traffic (1% of people stop in). They should be all over most cities, so could get an interesting view of dif patterns in dif places.

What are you trying to solve for? Just curious?
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Are you in the US.  My state publishes average annual traffic numbers.   Look for the database attached to your department of transportation website.

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