A team of MIT researchers think that navigation systems have a parking problem. They’re capable of telling us about traffic congestion and offering us alternate routes, but once we’ve arrived at our destination, we’re on our own when it comes to finding a place to park—and hunting for a parking space can increase emissions, congestion and the effective travel time. As the Fast Company article notes, a third of New York street traffic involves drivers looking for a parking spot. So the researchers modeled a system to address it.
To solve the parking problem, the researchers developed a probability-aware approach that considers all possible public parking lots near a destination, the distance to drive there from a point of origin, the distance to walk from each lot to the destination, and the likelihood of parking success.
The approach, based on dynamic programming, works backward from good outcomes to calculate the best route for the user.
Their method also considers the case where a user arrives at the ideal parking lot but can’t find a space. It takes into the account the distance to other parking lots and the probability of success of parking at each.
The caveat is that this system relies on data, whether directly from the parking lot companies or through crowdsourcing (Waze, but for parking), and that sort of data hasn’t, to my knowledge, been systematized yet.


