Google Maps helps users navigate in part by providing information about traffic congestion. By now, plenty of drivers frequently open the app to check potential routes, avoiding stretches that appear yellow or red on Google Maps whenever possible. That information, gathered by aggregating data from users currently on the road who are using the app, lets users know what current traffic conditions look like.
However, Google also selects routes by predicting future traffic patterns. Essentially, a few years before AI became something akin to a marketing term, Google Maps was already leveraging AI tech to forecast traffica sophisticated, using a process that combines current conditions with historical data.
Most days, traffic follows predictable patterns. Think about a popular commuting route. There are likely times during the day when it's consistently busier than others. But outliers can disrupt these typical patterns. If there's an accident, drivers might use other routes, resulting in traffic patterns that don't conform to expectations. That's why Google Maps can't rely on historical data alone to make traffic predictions. The app uses AI to analyze both current information and overall trends in trying to ensure accuracy.
BGR
Google Maps helps users navigate in part by providing information about traffic congestion. By now, plenty of drivers frequently open the app to check potential routes, avoiding stretches that appear yellow or red on Google Maps whenever possible. That information, gathered by aggregating data from users currently on the road who are using the app, lets users know what current traffic conditions look like.
However, Google also selects routes by predicting future traffic patterns. Essentially, a few years before AI became something akin to a marketing term, Google Maps was already leveraging AI tech to forecast traffica sophisticated, using a process that combines current conditions with historical data.
Most days, traffic follows predictable patterns. Think about a popular commuting route. There are likely times during the day when it's consistently busier than others. But outliers can disrupt these typical patterns. If there's an accident, drivers might use other routes, resulting in traffic patterns that don't conform to expectations. That's why Google Maps can't rely on historical data alone to make traffic predictions. The app uses AI to analyze both current information and overall trends in trying to ensure accuracy.
BGR
Google Maps helps users navigate in part by providing information about traffic congestion. By now, plenty of drivers frequently open the app to check potential routes, avoiding stretches that appear yellow or red on Google Maps whenever possible. That information, gathered by aggregating data from users currently on the road who are using the app, lets users know what current traffic conditions look like.
However, Google also selects routes by predicting future traffic patterns. Essentially, a few years before AI became something akin to a marketing term, Google Maps was already leveraging AI tech to forecast traffica sophisticated, using a process that combines current conditions with historical data.
Most days, traffic follows predictable patterns. Think about a popular commuting route. There are likely times during the day when it's consistently busier than others. But outliers can disrupt these typical patterns. If there's an accident, drivers might use other routes, resulting in traffic patterns that don't conform to expectations. That's why Google Maps can't rely on historical data alone to make traffic predictions. The app uses AI to analyze both current information and overall trends in trying to ensure accuracy.
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