Since 2010 the Saturday after Thanksgiving we are celebrating and supporting small businesses. This was a way to counter Black Friday and Cyber Monday that mainly feature big-box retailers and huge e-commerce stores (well, mainly Amazon). Many local governments promote and support (as they should do) small local businesses. In Pittsburgh (and in many other places across the country) one of the ways the local government promotes small business Saturday is by offering free curb parking across the city.

The intentions are certainly good; no one argues about that. The line of thought for offering free curb parking, is that by removing the “barrier” of curb parking pricing, more customers will decide to drive and come out to support local businesses by shopping. Sounds reasonable, but it is not the case. Sure people love free parking (even though nothing is free in life; we pay for it in other ways), but remember the objective here is to support local businesses. What do local businesses need? Infrastructure to support the – hopefully large – incoming flow of customers. Parking is one such infrastructure, that allows customers to temporarily store their cars, and run their errands, including shopping. Therefore, local businesses ideally want this infrastructure to be able to support as many cars as possible. Given that the number of curb parking spots are fixed, this means that the turnover of each parking spot should be high. The turnover of a spot is simply the number of cars parked in that spot during the day. A higher turnover, means that more cars parked in one spot, which can potentially translates to more customers.

What does free parking do? Free parking actually reduces the turnover of a parking spot! When parking is free, drivers do not have parking ticket anxiety anymore, and turnover plummets. Simply put, someone that might park for 30 minutes, run their errands and leave, might now stay 60 minutes and take their time in completing their errands. So this spot will be occupied for an additional 30 minutes, possibly not allowing another potential customer to park. Will this second person leave the area and not shop? Chances are not, but what will happen is that they will circle around to find a spot, increasing traffic, polution, and we all know what this does to our mood. Not to mention, that they might think of it twice before going back next time.

While we cannot know how exactly people will react when they cannot find a spot (i.e., leave or keep circling), we can get an estimate of the turnover of a spot through simulations. Assuming a simple Poisson model we can simulate our parking spot’s state. Starting with the – realistic for business areas – assumption that there is high demand for parking (which is actually another side-effect of free parking, that is, induced demand), we can simulate the turnover of a parking spot based on the changes in the average service rate of a parking spot. For example, if the average occupancy time of a parking spot is 30 minutes under normal pricing, we can expect this time to increase when we move to free parking. We do not know by how much but we can simulate a variety of cases ranging from conservative (e.g., a 5 minute increase on average) to more pessimistic ones (e.g., doubling the occupancy time). The following figure shows the simulated distributions for the turnover of a parking spot under different scenarios.

As we can see even under conservative assumption (average occupancy time increasing by 5 minutes), the spot turnover is reduced by approximately 14% (from an average turnover of 21 to an average turnover of 18). Businesses need spot turnover. Free parking achieves the opposite. So if we want to support local businesses, we should come up with other ways and not free parking. Borrowing from the core idea of PittSmartLiving of bringing together public transit, commuters and local businesses, free bus rides or free bike rides to specific business destinations could be a good start. This can help local businesses, can help dwellers particularly from underserved communities, while at the same time promoting public transit as a viable option for moving around the city. Equally important, is the signal sent by offering it for free during a small business celebration that public transit is a crucial link between people and businesses.

For anyone interested in the economics of parking (which are very different from what a superficial view of the topic might imply), the book “The High Cost of Free Parking” by Donald Shoup is a must-read.

At PittSmartLiving we have been trying to find ways to avoid over-crowded buses through win-win-win settings for all involved stakeholders (port authorities, riders and local businesses), improve riding conditions and in general “flatten the curve” for public transport.  As part of our efforts we have been analyzing crowding data in buses and businesses. The latter is important since it means that we could use the same methods to study changes in businesses’ foot traffic during the coronavirus pandemic, to understand how people react to recommendations for social distancing (which in the author’s personal opinion it should be termed as physical distancing, since we are still socializing using the technological advancements of our era).

We have been using Google’s Place API to collect crowding data. You might have interacted with this information available from this API when you tried to search for your favorite cafe and got back a bunch of information for it, including the bar chart below:

This chart provides information on how “busy” the place is expected to be based on historical data from people’s cell phones that use Google’s services, as well as, how busy the place is right now (red-ish bar). These numbers are expressed as a fraction of the most popular time of the week for the venue. For example, if the red bar on the figure above corresponds to 40, and this place is expected to be at its weekly busiest on a Sunday at noon, then currently the place is 40% as busy as during a Sunday at noon. Now, of course, the current value can be larger than 100 as well, which means that the place is busier than the expected weekly busiest time. Google’s estimates for the expected levels of crowdedness are updated in a rolling window fashion (with details not being fully known to the public).

It should be evident that we can use these data to get an estimate of how people are distancing these days. We started collecting data for this purpose from a select number of venues in Pittsburgh on March 13th and we found some interesting patterns. People, in general, were following recommendations (the order for sheltering in place was announced on March 19th and enforced on March 23rd in Pennsylvania). During that week traffic in retail stores and malls was down (approximately 45%), traffic in restaurants was down (approximately 30%), traffic in transportation hubs/stations was down (approximately 65%). Following, are some representative time-series examples of venues that experienced a reduction in traffic.

One significant exception was bars during March 14th that were busier than normal, with Pittsburghers celebrating St. Patrick’s day as it can be seen by the following time-series:

Now the only type of business that did not see any significant decline during the first week of data collection in Pittsburgh was grocery stores. They saw a small decline of 4%, but since that week things have changed, with larger declines observed. For example, the following figure shows average daily changes from a busy grocery store:

These results seem to indicate that people stocked up and distanced themselves even from grocery stores. Among the 30 groceries that we have been monitoring currently, the last 10 days there has been an average reduction of 30% in the crowdedness levels in these businesses. Now it is always good to understand the data. Part of this decline can be policies put by various grocery stores on how many people can be within their premises at any given time. This certainly will have an impact on the volumes reported by Google and other providers. So it is always good to keep in mind these things when trying to understand the data and make conclusions.

Recently, I also came across another dataset from Foursquare that captures foot traffic in venues. Now I was a bit skeptical initially since people rarely check-in to places they go, but digging a bit deeper in the data, these are not based on check-ins but rather on passive sensing of user locations (i.e., similar to what Google does). I was particularly interested in residential venues (that we cannot get information about from the Google API) and how foot traffic has changed there. First I took a look at the US as a whole and following are some interesting figures:

We can see the natural progression here through the month of March across the whole country with foot traffic in residential places being significantly reduced by the end of the month (as compared to the month of February on a similar day)! Now again we have to understand what the data measure. Someone might be confused saying that this does not make sense since we are staying at home more. This is true, but these Foursquare data measure the foot traffic, i.e., how many people are in a building/venue. This means that by the end of March there were fewer people in a residential building than expected (as compared to a baseline from February). This points to people physically distancing from their close friends and family as well, staying home with their close/immediate family only. Simply put, they do not have people over. Following is the time series of these changes for Allegheny County, which tells a similar story.

Overall, people seem to be taking this seriously (as they should) but there is still more that we can do! Stay far from each other; it saves lives!

Note: Google published a similar analysis in the beginning of April that provides a similar analysis for several countries and you can access these reports here.