Traffic Prediction with Advanced Graph Neural Networks
Google Map's traffic prediction has always led me to a very curious question:<p>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.<p>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?<p>Should Google Maps route several cars through a suboptimal route, if it results in traffic as a whole becoming better?<p>If Google Maps is "greedy" for every driver, can that make a traffic problem worse?<p>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?<p>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)?<p><a href="https://en.wikipedia.org/wiki/Braess%27s_paradox" rel="nofollow">https://en.wikipedia.org/wiki/Braess%27s_paradox</a>