
Traffic Prediction with Advanced Graph Neural Networks - beefman
https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks
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2bitencryption
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](https://en.wikipedia.org/wiki/Braess%27s_paradox)

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paxys
When User 3982274 is on a busy road using the app, Google optimizes for that
user's experience. If every user on that road is using the app at the same
time, these algorithms should theoretically result in the optimal condition
you described above.

For example, if there are two roads leading up to the destination, one at 100%
capacity and the other at 0%. The app will start routing people from road 1 to
road 2. When the two balance out and the app will stop the suggestion. Even
though it helped only some individual users, the end result is a 50/50 split,
so good for everyone.

~~~
frogblast
I live in an area strongly impacted by Google making ‘individually optimal’
decisions for each driver, and actually leaving those drivers in a
dramatically worse situation.

I live in a rural area, between a major population center and a major resort
area, with one major highway and a few small back roads that provide alternate
paths for part of the highways route. Every summer weekend the highway becomes
highly congested.

Google quickly starts routing people down the back roads because of a 30
minute delay on the highway. A sudden crush of cars hits these back roads, and
they end up gridlocked for 3-4 _hours_. Google then realizes traffic is
literally stopped on these roads, and stops sending new traffic down those
routes. But the people already on them are still stuck for hours.

It gets smelly when a bunch of drivers take a shit on the side of the road
because they can’t go anywhere else, and leave it there.

All because Google simultaneously made an ‘individually optimal’ decision for
a whole bunch of individual drivers at once.

——

Another example in the same area actually causes a backup on the highway
itself. Google started suggesting one back road that required an unprotected
left turn across oncoming traffic on the highway, to avoid a 10-15 minute
delay further on the highway. Drivers dutifully followed directions by getting
into the left turn lane.

The drain rate of the left turn rate is slow because oncoming traffic is also
high. The left turn lane fills up, and one driver with directions to turn then
stops in the traffic lanes to wait for room to get into the turn lane. And
suddenly the highway is now encountering 2-3 hour delays that don’t clear for
most of the day.

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HALtheWise
In theory, the learning based approach in this blog post could solve this
without any architectural changes. They mention predictions spilling to nearby
roads, learning the pattern that "if the highway is backed up now, the little
road will be too in a few minutes". Once that is baked into the model, Google
Maps won't direct (as many) people onto the site road because they would get
stuck in the predicted traffic there. The nice thing about this is it also
handles the case where other mapping applications direct drivers there, and
the "smartest" mapping application will still get you there fastest.

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jastingo
What I've always wondered is to what extent A/B testing is conducted after any
such algorithm is implemented.

For example, does Google Maps send some users deliberately down a route that
it thinks is suboptimal so that it can better learn traffic patterns over a
wider range of roads? My instincts as a data scientist tell me this would be a
great way to gather more data and to create a better system as a whole, but at
the expense of some users having longer drive times for some routes.

Putting my tin foil hat on, I've long suspected that Waze is used as the
experimentation platform for Google Maps in this way. Where I live, Waze
presents some highly unusual routes that I know are not optimal having lived
here forever, whereas Google Maps is more on point.

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symplee
Is it just me, or is it a little disingenuous to write the claim "...improve
the accuracy of real time ETAs by up to 50% in places like Berlin, Jakarta,
São Paulo, Sydney, Tokyo, and Washington D.C."

When the actual numbers listed for those cities are:

    
    
      Berlin - 21%
      Jakarta - 22%
      São Paulo - 23%
      Sydney - 43%
      Tokyo - not listed
      Washington D.C. - 29%

~~~
datameta
It gets even more muddled when you consider they mention the following: "While
Google Maps’ predictive ETAs have been consistently accurate for over 97% of
trips, we worked with the team to minimise the remaining inaccuracies even
further - sometimes by more than 50% in cities like Taichung."

Are they essentially saying that they lowered 3% inaccuracy to ~1.5% in
Taichung? (And nevermind the fact that 51% is described as "more than 50%"...)

Of course this type of work is fascinating. Getting from 97 to 98.5% accuracy
is far far more difficult than getting from 95.5 to 97%. But I don't enjoy the
fudging of the perception of results.

~~~
boloust
It could mean that 97% of the trips had an predictive error of, say, 2
minutes, while the remaining 3% had an error of, say, 10 minutes, and they
reduced that error to 5 minutes.

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GistNoesis
I'm not sure this is advancing in the right direction for the users.

Optimizing traffic is probably doing more harm than good. It's one kind of
premature optimization. I see it as a way of masking potential underlying
problems like not enough investment in infrastructure, or too many cars for
the capacity.

Squeezing out the performance from existing infrastructure, is probably
harming the longer game of having a good transporting system for the users.
And that's a game that should be played by the owner of the infrastructure,
not some third party which just exploit the externalities by sending heavy
road traffic through calm neighborhoods every-time there is a traffic jam,
often increasing the risks of further accidents and grid-locking everything,
because those roads were not designed for those spikes.

The worse is that more often than not, those routing apps are not even making
the user win some time. But it makes the user happy because he believes he
took the right direction by following the app direction.

Often it's net negative for everybody.

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Barrin92
related question, is there actual open source data available for traffic or
other kinds of urban movement for people to toy around with?

~~~
MR4D
Houston has some.

I’m on my iPhone so I can’t browse the data, but XML and JSON can be found
here.

[https://traffic.houstontranstar.org/datafeed/datafeed_info.a...](https://traffic.houstontranstar.org/datafeed/datafeed_info.aspx)

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ericls
I think in addition to predict traffic jam for individuals, this can be used
to adaptively control traffic lights and open/close lanes to reduce traffic
jam for the whole system.

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thx4dafish
Just have to point out that there is absolutely a difference between user and
system optimal, even outside of Braess Paradox situations. If you’ve never
noticed it, it’s because traffic engineers worked hard to mitigate it.

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WalterBright
They can't even time the traffic lights correctly around here.

