Google Maps Predictive Travel Time: What it is and how it works

Google Maps Predictive Travel Time: What it is and how it works

Predictive Travel Time Google

This week Google Maps announced new product functionality for its API products – Predictive Travel Time.

This new feature is part of the Google Maps solutions and will enable users to plan ahead of time to choose the best route and times based on Google’s historical analysis of road-traffic data. Predictive Travel Time uses historical time-of-day and day-of-week traffic data to estimate travel times at a future date.

What is Predictive Travel Time?

Predictive Travel Time is an enhancement to Google Maps Directions and Distance Matrix APIs that returns estimated travel time based on the day of the week and the time of day.

The user can choose any transport mode: car, public transport, walking, bicycle… Although default mode is driving (car), it also includes walking and bicycling (where available), and public transport (that requests distance calculation via public transit routes, where available). 

All current Google Maps Directions and Distance Matrix APIs customers can access this feature.

How can it be used?

This feature can be used in many cases, such as “What properties are 20 minutes away by tube from my workplace?” or “What time should I leave home if I have a meeting at Paddington office at 11am?”.

Predictive Travel Time can be essential for many companies in the Travel and Leisure, Transportation and Real Estate industries. But also if you manage a fleet of delivery drivers, it can be used to improve your operations and help you operate more efficiently with access to real-time traffic information.

Best practices - examples

When working with a few customers, we have identified these following use cases they have developed to help their users choose a property, a hotel or a mode of transportation:

Let’s imagine the user is looking to buy a house or flat, that is 15 minutes away by public transport from their office in Paddington, Central London.

  • Zoopla uses a Travel Time filtering system that allows the user to choose the preferred time and transport mode to and from a location. 
  • Zoopla allows the user to also see those properties in a map (which indicates the area that is within 15 minutes of the user’s selected location). 
  • The user can also see the nearest amenities such as schools, gyms, restaurants or supermarkets.
Zoopla Predictive Travel TIme
Travelodge logo

UK Hotel brand Travelodge also includes the new Google API Travel Time when showing the location of an specific hotel on a map. 

The user can see the nearest points of interest but also search for any place and get the travel time by different transport modes (car, public transport, walking and cycling).

When clicking into “Get directions”, the user gets how to get to that destination, without having to leave the Travelodge website, which helps obtain more bookings and improves the user engagement to the service provided.

Travelodge Predictive Travel Time

The award-winning transport app, Citymapper, includes Travel Time predictor on their maps to show the users suggested travel routes and their times to the selected destination.It includes walking, tube, bicycle, taxi and bus. 

Citymapper now covers up to 68 cities around the world such as London, Tokyo, Madrid, Sydney, Lisbon, Moscow, Miami, Mexico City, among others.

The app is currently available in both Android and iOS app stores.


CityMapper Predictive Travel Time

Based on the use of Google Maps of each customer, Predictive Travel Time can be implemented in several ways.
In Snowdrop, we take into account the best practices in the market to advise our clients on how to improve their services, as well as to be able to optimize the use of Google Maps.

If you’d like to know more about how Predictive Travel Time can fit your business, our Google Maps optimisation plans or Google Maps API pricing, you can reach out to us by filling the form below. We will be happy to assist!

* This post has been edited with more up-to-date information as of 15 December 2020.