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Former Google engineer explains how Google Maps determines your ETA

Published Dec 27th, 2013 5:15PM EST
Google Maps ETA Calculation

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The feature in most navigation apps that offers at estimated time of arrival is a nice add-on that gives drivers a general idea of when they might arrive at their destination. Of course, some services are better than others at estimating your ETA and as is often the case when it comes to anything involving crunching large amounts of data with numerous variables, Google is better than most. While the exact science Google uses to calculate its projected arrival times remains a mystery to those outside the Maps team, a former Google engineer recently offered a good general explanation of how Google Maps calculates your ETA.

“Like in similar products, Google maps ETAs are based on a variety of things, depending on the data available in a particular area,” former Google engineer Richard Russell wrote while answering a question on Quora earlier this year. “These things range from official speed limits and recommended speeds, likely speeds derived from road types, historical average speed data over certain time periods (sometimes just averages, sometimes at particular times of day), actual travel times from previous users, and real-time traffic information. They mix data from whichever sources they have, and come up with the best prediction they can make.”

Russell went on to explain that navigation providers constantly compare their estimates to actual historical travel time in various traffic conditions to fine-tune their algorithms. As a result, “the companies who have access to the best usage data (ie those who are best able to compare their predictions against reality, which means those who have the most usage) are likely to end up with the best predictions in the medium to long term.”

While ETA projections often serve as good general guidelines, the engineer notes that accuracy isn’t likely to improve in the near future.

“Calculating ETAs is a future-prediction problem, and traffic, while it follows certain patterns, is inherently unpredictable,” Russell wrote. “Even if you had complete knowledge of current traffic conditions and known changes (eg roadworks starting or a football match finishing), there’s nothing that can predict a crash or a slow truck changing route.”

Zach Epstein
Zach Epstein Executive Editor

Zach Epstein has been the Executive Editor at BGR for more than 10 years. He manages BGR’s editorial team and ensures that best practices are adhered to. He also oversees the Ecommerce team and directs the daily flow of all content. Zach first joined BGR in 2007 as a Staff Writer covering business, technology, and entertainment.

His work has been quoted by countless top news organizations, and he was recently named one of the world's top 10 “power mobile influencers” by Forbes. Prior to BGR, Zach worked as an executive in marketing and business development with two private telcos.