As urban centers grow, cities need to evolve their urban planning to be inclusive of all modes of transportation, including pedestrians, bikes, buses, cars, and more. With Miovision, you can access insights you need to improve mobility, increase safety, and reduce congestion on city roadways.
In-ground loops can only do so much. They sense motion, but can’t distinguish between bikes, cars, buses, pedestrians, and other modes of transportation. That makes it difficult to plan efficient traffic flow at different times of the day, to accommodate all citizens.
Some streets are busy with heavy vehicle traffic, some are congested at specific times of the day, and others might be used by pedestrians or bikes most of the time. Without the right technology, you can’t assess which modes of transportation are being used most often to customize signal times.
You make the best decisions with the data you have. Unfortunately, some technology doesn’t collect continuous data, which means that important insights can be missed. With granular information, you can identify trends that will have an impact on planning, timing, and safety considerations.
With so many different modes of transportation on the road, collisions are bound to happen. And when they do, it’s difficult to get the full picture by relying solely on witnesses. It’s even more difficult to understand the circumstances around the intersection that could have contributed to the problem without auditable data.
Miovision uses machine learning to teach our technology to identify different modes of transportation and react accordingly. The longer you use it, the smarter it gets. Make the right decisions based on the most accurate, reliable, and auditable data.
“We started with the goal of improving how we monitor our traffic signals, and now we’re working with Miovision to explore how to improve safety for pedestrians and help first responders get to emergencies more quickly.” Mark de la Vergne, Chief of Mobility Innovation, Detroit