Through a novel classification approach we added a new feature of HOV routing and ETAs.

The shift to sustainable travel modes like electric vehicles (EVs), carpooling, and public transit, has made travel times more varied. This is largely due to the availability of dedicated lanes, such as carpool lanes, also called high-occupancy vehicle (HOV) lanes, which are reserved for vehicles with multiple passengers and are designed to move traffic more efficiently during peak hours. As a result, HOV lanes are typically faster than general lanes during rush hour. For example, in Utah’s Salt Lake Valley, at 68.18 mph compared to 58.60 mph in general lanes, a difference of about 16%.

Accurate estimated time of arrival (ETA) predictions and optimized routing are key to improving the commuting experience. With precise ETAs, travelers can make better decisions, save time, and even contribute to reducing congestion and emissions. With this in mind, Google Maps recently introduced a feature that lets drivers select routes that include HOV lanes and see that route’s ETA. In this blog post, we explain how we developed this feature and developed a classification system to determine HOV trips from non-HOV trips, which led to the launch of HOV specific ETAs in Google Maps.

Developing HOV-specific ETAs

To estimate HOV travel times, we first infer past HOV travel times by analyzing aggregated and anonymized traffic trends. We then use these inferred times to train our ETA prediction models specifically for HOV lanes.

However, identifying HOV trips isn’t straightforward. Simple data, like speed, can be similar for both HOV and non-HOV users, especially when traffic is light. Yet HOV travel patterns also have several distinct and useful constraints, including limitations on availability based on location, time of day, and exceptional events.