Google uses AI to reduce stop-and-go traffic on your route

The idea is cool, but if you are in a US context, outside of a core city, please understand that your being delayed is actually part of the feature set of a stop light. Delaying you and other drivers allows gaps in traffic that let people enter relatively high-speed, unlimited-access roads that cover the US. It takes a lot of open space to safely enter a fast road.

The alternative would be to narrow roads and lower speed limits as traffic intensifies, mitigating the need for stop lights. Unfortunately, design speed is the priority: we do whatever it takes to make sure that cars, when they are not stopped by lights or other traffic, can safely(ish) travel at the design speed.

Cities don't install enough sensors. They are probably the best way to improve traffic outside busy times.

The coolest implementation I've seen was in Los Alamos. The sensor was way ahead of the intersection so by the time I got to it, without slowing down, the light changed. This was more than 25 years ago.

That was the subject of Google Hash Code 2021: https://www.kaggle.com/competitions/hashcode-2021-oqr-extens...

This format was really fun, it's a shame Google discontinued all its code competitions.

I find traffic infrastructure kind of inherently broken in North America. There's a serious over-reliance on signalization, and to boot, the signals and signs are often less than ideal for circumstances.

You can have speed limit signs, but nothing controls speed better than physical infrastructure -- in city cores, things like pedestrian islands, bulb-outs, raised crosswalks; throughout, roundabouts in place of four-way stop and light-controlled intersections.

There's an absurd amount of stop signs in the US and Canada -- practically every intersection has at least one. They're mostly superfluous, and could be done away with, replaced with proper education for drivers, and yield signs where priority is ambiguous. Stop signs can serve a purpose where the view is obscured, and the driver genuinely needs to come to a complete stop to evaluate if they can proceed safely. When they're planted everywhere, they mean nothing.

Add on top of this a fine system that's proportional to the driver's net worth and the kinetic energy of the vehicle at time of infraction, and we could be getting somewhere.

I honestly believe driving should be strongly discouraged, made more painful and more expensive, to push people away from it as a primary mode of transport. Decades ago car companies sold us a nightmare of a "freedom dream" and destroyed so much in the name of profits, it's unforgivable.

On the one hand this is genuinely cool: take existing Google Maps navigation log data, use it to identify ways traffic lights could be reprogrammed to reduce stop and starts, build dashboards of those recommendations and share them with cities. Love that.

But "Google uses AI to..." is such a frustrating way to frame it!

Sure, there are likely some traditional ML models and techniques involved in this work. It first launched in 2021 so it's unlikely there were any generative models (which, let's face it, is what many people in 2024 assume when they see "AI" mentioned).

It's not inaccurate to call it AI, but it adds about as much information to the story as saying "Google uses algorithms/computer science/data analysis to..."

The more interesting component here isn't the "AI", it's the vast amount of road usage data. Google has been able to collect from prople running Google Maps for navigation. Though maybe their PE team don't want to emphasize that as much!

I'm sure they believe they're doing that, but as other posts have mentioned, it is extremely difficult to give google feedback.

I happen to know a few googlers socially from my days in SFLUG and BayLUG, so I can sometimes get a note to the appropriate product manager or engineer through the friends-of-a-friend network. But going through the front door has never worked.

I believe that Google Maps is perfectly fine in most of the bay area. It's generally acceptable for roads that a google street view car has driven down. But pretty much a lost cause for other roads.

Corner me at a conference sometime and I'll tell you about how google maps sent us four-wheeling through eastern california fire roads (a dirt road that collapsed after we turned around at a critical junction) or told me to get on and off the highway the first time I drove from Tacoma to Seattle (what should have been a simple 45 min drive with traffic turned out to be a 2 hour slog fest because I didn't have enough local knowledge to realize maps was full of itself.)

While it's great they no-doubt are providing an internship for two or three PhD candidates, I think they may want to fix their data before thinking AI will improve the experience.

The AI does not know the data you're training it with is garbage. If yoy do it right, you may be able to spot anomolies in the data or auto-cluster bits of data, but if you train any sort of CNN on garbage data, you're going to get garbage out.

So while this may be great for people commuting from the GooglePlex in Mountain View to the Google facilities in San Francisco, and it //may// help people traveling along I-5 in California, I fear the garbage aspect of their geo data will not be magically solved by adding AI.

Used individually, it is an amazing tool. Used collectively, it is an insane amount of power for a private company to possess. They can manipulate traffic flows almost anywhere on earth, at any time, instantaneously.

Why do we need Google to do this?

I remember learning 10+ years ago in college that (I think) Paris was using AI to dynamically optimize traffic lights to manage congestion. It was called machine learning back then, of course.

The oldest paper that Google lets me find today is from 2017 (search has a recency bias): https://ieeexplore.ieee.org/document/8122189

edit: found one from 2014 – https://www.dot.ny.gov/divisions/engineering/technical-servi...

Edit: Found better sources!

Here's a paper from 1990 about real-time modelling and monitoring of traffic patterns in Paris https://www.researchgate.net/publication/317769123_Modelling...

And a paper from 2015 that explicitly mentions using cameras for traffic detection: https://hal.science/hal-01491597/document

If you are using Ai to do it, you are not going to be net negative. This is another example of a solution seeking a problem.

A bit tangential, but can anyone suggest interesting resources for understanding Google Maps' algorithms and architecture?

I am familiar with Peter Sanders' 2009 talk on Fast Route Planning (https://www.youtube.com/watch?v=-0ErpE8tQbw).

I really liked the 'AI-generated summary' the page offers. I hope to see more of that on the wider web (because, my attention span doesn't go beyond paragraph no. 3).

Does anyone know if the google maps team offers a simulation platform to city planners?

Seems like it might be useful for testing hypotheses about how proposed road projects might impact traffic flow.

Cool but I didn’t really see AI specifics

it seems to me google maps is causing worse traffic by re-routing to side streets

https://spectrum.ieee.org/amp/your-navigation-app-is-making-...

City planners – let's build major thoroughfares so we can keep traffic away from residential neighborhoods.

Google Maps – this tiny residential street looks empty, let's send a million cars through it.

Google needs to stop re-routing mid-navigation full stop.

The number of times that I've had to miss exits or turns because google re-routed without sufficient lead time has caused me to stop using it for driving navigation.

It's no wonder you see crazy people swerving across 3 lanes to make an exit these days. I blame google maps navigation for a lot of unsafe driving I see on the road today.

> Everyday cars are stuck in traffic

Nope, they are traffic!

I understand the intention here of reducing stop time at stoplights for cars, but I think this is a dangerous project.

The word pedestrian, or bicycle does not exist in this article whatsoever. Traffic lights are a flawed, but useful tool for traffic calming. If we optimize for reducing the amount of red lights cars will run into, this will fundamentally increase speeds on roads, and increased speeds, equals increase pedestrian, cycle, and car deaths.

This is all conjecture, but it seems like the key indicator that Google is providing two cities is reduction in stop time. If that is their key metric, while also not looking at other things like bicycle stop time, or pedestrian wait time; we will be optimizing for average car speed indirectly. That is a bad thing inside of cities.

I guess this is more of a press release / PR blog, there's no technical explanation of anything here.

>Today, Green Light is live in over 70 intersections

I'm not even sure if this is a good production result to know what has been accomplished.

How about Google maps uses traffic congestion predictions for routes and travel times? It does not, which is ridiculous.

It seems like google maps routing will route you right through the maximum number of stop lights when it picks a "green" route.

It's almost like its weighing a low average speed with green driving and a long string of stop lights will lower the average speed.

When I see projects like this one, my mind mostly goes to, "how will this be funded and maintained?"

This requires a lot of specialized infrastructure to be run. I really like the idea of cities investing in measures that make small positive improvements for many people. However, for Google this seems like a side project, so I have my doubts that it will be around in 5 years. Not exactly the kind of thing I want to back with long term infrastructure dollars.

So google is tracking your google maps data everywhere you go? any way to opt out of the surveillance?