Google Maps is designed to improve your public transportation experience by providing crowded forecasts for public transportation. This means that just as you use Google Maps to see real-time traffic on your route, you will now be able to understand the congestion of the bus, train or subway you are about to capture. In addition, Google Maps is expanding the bus’s real-time traffic delays to help you understand how long it takes for the next bus to arrive at your site. The ability to use machine learning models on top of real-time traffic data was first introduced in India earlier this month.
As transportation congestion forecasts increase, Google Maps will be the ultimate solution to see how crowded your next public journey will be. This will help you make an informed decision about whether you should take an already crowded bus, train or subway, or wait for a seated vehicle.
Transmission congestion prediction is available through optional feedback directly from Google Maps users. In addition, you can receive a notification on the Maps app asking you how many buses, train or subway congestion you have when navigating in transit mode.
In addition to providing you with crowded forecasts, Google Maps adds real-time traffic delays to buses to help you know if your bus will be late or delayed. The delay will be included based on real-time traffic conditions on your route. In addition, Google is using its machine learning model to predict bus delays in real time.
“In addition to road traffic delays, we will also consider the detailed information about the bus route and the signal about the location and time of the trip when training our model. Even in a small area, the model needs to convert the speed prediction to a different bus. The speed of the street is different,” Google explained its other blog post.
It’s worth noting that Google Maps first introduced this feature to predict bus delays in India earlier this month. However, it is now available on Android and iOS in around 200 cities worldwide – as well as the newly developed transport congestion prediction feature.