So far, we have talked about the secretary problem, or more generally, about optimal stopping, and how it can be useful to model when to sell a house, or how to look for a romantic partner. There are many more applications for optimal stopping and today I would like to talk about one of them, when to stop looking for another spot and park. Parking, for anyone in an urban environment, can be a painful experience. To address this problem, Brian Christian and Tom Griffiths went to Donald Shoup, the man described by Los Angeles Times as "the parking rock star" and UCLA's Distinguished Professor of Urban Planning.
Shoup argues that while there are many factors that affect parking, the single most important one is the occupancy rate. The occupancy rate is a number representing how many spots are taken percentually in a specific area. Furthermore, he states that many of the headaches of parking are consequences of cities adopting policies that result in extremely high occupancy rates. For example, let's say that there is an event downtown and a lot of people are attending. If the cost of parking right next door to the event is the same as parking further away, then everyone will want to park there instead of parking further away and walking a little bit. This only makes people waste more time, since they need to go see for themselves that spaces right next to the event are already taken. Additionally, people burn a lot of fossil fuel while they cruise looking for a spot.
Then, what is the solution? According to Shoup, a possible solution is implementing dynamic prices in the meters so that occupancy rates are held stable at around 85%. Therefore, the higher the occupancy rate in a specific area, the higher the price to pay for a spot there. A version of this approach is already being applied in downtown San Francisco. The reason Shoup would want to keep occupancy rate at around 85% is because the time and fuel spent looking for a spot increment exponentially as occupancy rates go up. For example, when occupancy rates go from 90% to 95%, it translates into cars taking twice as much time to find a spot. Another way of saying this is as follows: With an 85% occupancy rate, you would have to start looking for a spot around two blocks before your destination. On the other hand, with an occupancy rate of 99%, you would have to start looking for a spot about a quarter mile before reaching your destination.
Of course, this is a simplified solution for a very complicated problem. When presenting this solution, other variables are being ignored such as population density in cities as well as how many cars are owned per person on average. Maybe that is why when asked what was his secret weapon to confront the parking problem, Shoup answered: "I ride my bike".
No matter what parameters are taken into account in the model, the more vacant spots, the easier life gets. Nevertheless, policy makers seem to have failed in understanding that parking is not a simple resources and maximizing of utilization problem. Parking is a continuous process. A process that consumes attention, fuel, time and one that generates pollution and congestion. Even though it may seem counter-intuitive, empty spots on a highly desirable block may indicate that things are working properly.
Sources:
If you want to check out other thoughts that this awesome book has evoked, click on these past posts:
- Algorithms To Live By #1: Expectations, Authors, And Algorithms
- Algorithms To Live By #2: Optimal Stopping - How Long To Look For And When To Stop To Find The Best (The 37% Rule)
- Algorithms To Live By #3: The 37% Rule Applied To Partner Searching
- Algorithms To Live By #4: How Does The Secretary Problem Changes With More Information?
Best,
