We also look at a number of other factors, like road quality. If we predict that traffic is likely to become heavy in one direction, we’ll automatically find you a lower-traffic alternative. Our predictive traffic models are also a key part of how Google Maps determines driving routes. To account for this sudden change, we’ve recently updated our models to become more agile-automatically prioritizing historical traffic patterns from the last two to four weeks, and deprioritizing patterns from any time before that. Since then, parts of the world have reopened gradually, while others maintain restrictions. We saw up to a 50 percent decrease in worldwide traffic when lockdowns started in early 2020. Since the start of the COVID-19 pandemic, traffic patterns around the globe have shifted dramatically. This technique is what enables Google Maps to better predict whether or not you’ll be affected by a slowdown that may not have even started yet!įor most of the 13 years that Google Maps has provided traffic data, historical traffic patterns have been reliable indicators of what your conditions on the road could look like-but that's not always the case. By partnering with DeepMind, we’ve been able to cut the percentage of inaccurate ETAs even further by using a machine learning architecture known as Graph Neural Networks–with significant improvements in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C. Our ETA predictions already have a very high accuracy bar–in fact, we see that our predictions have been consistently accurate for over 97% of trips. Recently, we partnered with DeepMind, an Alphabet AI research lab, to improve the accuracy of our traffic prediction capabilities. We then combine this database of historical traffic patterns with live traffic conditions, using machine learning to generate predictions based on both sets of data. For example, one pattern may show that the 280 freeway in Northern California typically has vehicles traveling at a speed of 65mph between 6-7am, but only at 15-20mph in the late afternoon. To predict what traffic will look like in the near future, Google Maps analyzes historical traffic patterns for roads over time. Predicting traffic with advanced machine learning techniques, and a little bit of history This is where technology really comes into play. But while this information helps you find current traffic estimates -whether or not a traffic jam will affect your drive right now-it doesn’t account for what traffic will look like 10, 20, or even 50 minutes into your journey. When people navigate with Google Maps, aggregate location data can be used to understand traffic conditions on roads all over the world.
FUTURE TRAFFIC GOOGLE MAPS DRIVERS
Live traffic, powered by drivers all around the world If you’ve ever wondered just how Google Maps knows when there’s a massive traffic jam or how we determine the best route for a trip, read on. Today, we’ll break down one of our favorite topics: traffic and routing. While all of this appears simple, there’s a ton going on behind the scenes to deliver this information in a matter of seconds. When you hop in your car or on your motorbike and start navigating, you’re instantly shown a few things: which way to go, whether the traffic along your route is heavy or light, an estimated travel time, and an estimated time of arrival (ETA).
Every day, over 1 billion kilometers are driven with Google Maps in more than 220 countries and territories around the world.
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