Traffic Prediction with Advanced Graph Neural Networks
Google Map's traffic prediction has always led me to a very curious question:Clearly Google Maps has the ability to turn into a feedback loop. Traffic exists -> people use Google Maps to find better routes -> traffic is modified due to people taking alternate routes -> new traffic emerges.So my question is: what is Google Maps traffic optimizing for? The best traffic experience for User 3982274, or the best traffic experience for the conglomerate of all cars on the road?Should Google Maps route several cars through a suboptimal route, if it results in traffic as a whole becoming better?If Google Maps is "greedy" for every driver, can that make a traffic problem worse?In reality, I guess this problem is more hypothetical than real, at least today.  But imagine this: in 30 years, if all cars are self-driving and self-navigating via systems like Google Maps, what is the system optimizing for?edit: there's also Braess's paradox. I'm not sure if it applies here, but perhaps it does -- could "sending some users down a new route during heavy traffic" be identical to  "adding a road to a network", which can therefore result in the paradox (worse network conditions for everyone)?https://en.wikipedia.org/wiki/Braess%27s_paradox