That used the existing traffic system as much as possible, specifically the existing segmentation of road-networks and the associated real-time data pipeline. Our initial proof of concept began with a straight-forward approach How do we represent dynamically sized examples of connected segments with arbitrary accuracy in such a way that a single model can achieve success? ![]() To estimate travel times using Supersegments is an architectural one. The biggest challenge to solve when creating a machine learning system On the road to novel machine learning architectures for traffic prediction We divided road networks into “ Supersegments” consisting of multiple adjacent segments of road that share significant traffic volume.Ĭurrently, the Google Maps traffic prediction system consists of theįollowing components: (1) a route analyzer that processes terabytes of traffic information to construct Supersegments and (2) a novel Graph Neural Network model, which is optimized with multiple objectives and predicts the travel time for each Supersegment. Here’s how it works: Dividing the world’s roads into Supersegments To do this at a global scale, we used a generalized machine learning architecture called Graph Neural Networks that allows us to conduct spatiotemporal reasoning by incorporating relational learning biases to model the connectivity structure of real-world road networks. Team to minimise the remaining inaccuracies even further - sometimes by more than 50% in cities like Taichung. While Google Maps’ predictive ETAs haveīeen consistently accurate for over 97% of trips, we worked with the Additional factors like road quality, speed limits, accidents, and closures can also add to the complexity of the prediction model.ĭeepMind partnered with Google Maps to help improve the accuracy of For example - even though rush-hour inevitably happens every morning and evening, the exact time of rush hour can vary significantly from day to day and month to month. This process is complex for a number of reasons. To accurately predict future traffic, Google Maps uses machine learning to combine live traffic conditions with historical traffic patterns for roads worldwide. Traffic a driver can expect to see 10, 20, or even 50 minutes into their drive. While this data gives Google Maps an accurate picture of current traffic, it doesn’t account for the To calculate ETAs, Google Maps analyzes live traffic data for road by using advanced machine learning techniques including Graph Neural Networks, as the graphic below shows: Researchers at DeepMind have partnered with the Google Maps team to improve the accuracy of real time ETAs by up to 50% in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C. These features are also useful for businesses such as rideshare companies, which use Google Maps Platform to power their services with information about pickup and dropoff times, along with estimated prices based on trip duration. ![]() You need to leave in time to attend an important meeting. You need to notify friends and family that you’re running late, or if These are critical tools that areĮspecially useful when you need to be routed around a traffic jam, if People rely on Google Maps for accurate traffic predictions andĮstimated times of arrival (ETAs). Today we’re delighted to share the results of our latest partnership, delivering a truly global impact for the more than one billion people that use Google Maps. ![]() ![]() We can apply breakthrough research to immediate real-world problems at a Google scale. Speech-impaired user with his original voice, to helping users discover personalized apps, Traffic Prediction with Advanced Graph Neural Networksīy partnering with Google, DeepMind is able to bring the benefits ofĪI to billions of people all over the world. Traffic prediction with advanced Graph Neural Networks.
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