The Roads Management Insights data models for Travel Time and Speed Reading are built by combining different information sources:
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Aggregated maps data:The most critical source is aggregated, anonymized data from Google Maps, which allows Google Maps to calculate the real-time speed of vehicles on roads around the world.
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Historical traffic data:Over time, the aggregated user data is used to build historical traffic patterns, which help the system understand the "normal" traffic for a specific road at any given time and day of the week.
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Supplemental data:Historical data is combined with other data, including third-party information from partners like local Departments of Transportation, as well as real-time user feedback from Maps users reporting incidents like crashes or construction.
AI combines these information sources together to understand current conditions with real-time data, and to provide baseline predictions with historical data. This fusion is key for how routes are predicted, for example:
- Short routes depend largely on current, real-timeinformation
- Longer routes use advanced AI modeling, where nearby segments are predicted using real-time data, while more-distant segments rely more heavily on historical patterns.
- Roads with limited real-time signals rely more heavily on its historical datato predict slowdowns.
Further reading
You can learn more about Google's road information in the following Google blog posts:
