Today, we’re bringing predictive travel time – one of the most powerful features from our consumer Google Maps experience – to the Google Maps APIs so businesses and developers can make their location-based applications even more relevant for their users.
Predictive travel time uses historical time-of-day and day-of-week traffic data to estimate travel times at a future date. This makes it easier than ever to predict how long it will take to get somewhere and suggest the best route even when the departure time is far in the future.
Since traffic conditions in the future will vary greatly, we give companies the ability to set an optional traffic_model parameter to choose whether an optimistic, pessimistic or best_guess estimate of traffic conditions is most appropriate for their application. Some examples:
- If your application is used for scheduling deliveries, and you want to ensure you’ve allowed enough time between deliveries so your drivers won’t be late, you might want to use the pessimistic travel time estimates.
- On the other hand, if you’re building a thermostat app, and you want the house to be warm by the time your user arrives home from work, you might want to use the optimistic travel time estimate to calculate when the user is likely to arrive.
- If you want to give your user an estimate of the most likely travel time to their destination, the default best_guess traffic model will give you the most likely travel time considering both current traffic conditions and historical averages.
To get started with the predictive travel time, visit our documentation on the Directions and Distance Matrix API and try it out by signing up online for the Standard Plan or reach out to your account manager. To become a Premium Plan Customer, please reach out here.