It is our goal for Labs to be the place where the future of smart city innovation is conceived, researched and eventually brought to market. It’s made up of technologists and product strategists focused on the future of traffic technology for the smart city.

So why now? A couple of reasons.

First, cities are feeling enormous pressure from traffic congestion and infrastructure strain. They struggle with moving people and goods around their urban centers. That pressure will only intensify in the decades ahead: The United Nations projects that two-thirds of the world’s population will live in cities by 2050. Cities will have no choice but to advance the way they manage their transportation infrastructure with social, economic and environmental considerations in mind.

The second driver behind Miovision Labs relates to the tremendous growth we’ve experienced as a company. Miovision has now reached a size and scale that makes internal research and development (R&D) a massive reinvestment area for the company. Despite this effort, there is a need for a more open, collaborative approach to smart city R&D, combining the best technologists across many organizations. As the ‘open’ movement sweeps across almost every aspect of modern computing, we believe the same is true in smart city innovation.

Miovision Labs epitomizes this more open approach to innovation, an approach that scales beyond the traditional limitations of proprietary R&D. These limitations were first brought to my attention when I read Clayton Christensen’s seminal work, The Innovator’s Dilemma.  Christensen’s thesis—that disruptive technologies from upstart companies ultimately lead incumbents to fail—is rooted in a fundamental organizational reality: Disruption from within is extremely difficult. What led companies to succeed in the first place—their core tech, how they deliver it to customers—is what the organization is built to sustain. Any truly disruptive innovation from within is reasonably suppressed in the name of fiduciary responsibility.

So with Miovision Labs, our technologists will be free to disrupt away. Break existing models. And forge new pathways to innovation. In doing so, this team will help cities make sense of the vast amounts of data that will become available in the coming years and use this data to fuel smart city applications in traffic and beyond.

The Path to Miovision Labs

Miovision was launched 11 years ago to solve the urban transportation problem. Back then—and much of this is still true today—cities struggled to access data needed to improve transportation and traffic in cities. Data was difficult to unlock from older infrastructure. Legacy data collection methods were expensive, inaccurate, and lacked the detail engineers needed to properly plan and operate roadways. Since our launch, we have helped over 13,000 municipalities connect, monitor, and study their traffic infrastructure to make roadways safer and more efficient.

In the next 10 years, the problem will shift for many cities from accessing the data to interpreting and applying it. Cities will have almost unlimited access to data that details how their infrastructure is performing and how their citizens are using roadways. The challenge of future traffic teams will be to understand and put this data to use in their cities.

Miovision Labs’ mission is to lay the groundwork for a next generation of traffic technology, with the goal of ensuring that rapidly escalating volumes of data remain an asset for a smart city, and don’t become an unwieldy liability. Reaching this state requires specialized skills and IP including computer vision, deep learning, big data analytics and embedded device design—skills that Miovision Labs brings to the table.

We’re Hitting the Ground Running

Out of the gate, Miovision Labs already has key partnerships in place. Our initial work will focus on the following transportation projects in collaboration with academic researchers and non-government organizations (NGOs):

  1. Freight flow in cities. In partnership with freight specialist firm CPCS, Miovison Labs will study how traffic data from passive sensors, video cameras, GPS, and other sources can be used to understand and improve how freight moves through cities. The findings will inform planners and policymakers in the public sector about how to better collaborate with private firms in the collection and use of new data types for streamlining urban freight flows. The project is sponsored by the U.S. Transportation Research Board’s National Cooperative Freight Research Program (NCFRP 49).
  2. Road incident prevention. In partnership with the University of Toronto, Miovision Labs is pioneering computer vision (CV) for use in Conflict Analysis. This discipline has historically required human observation to detect and rank the severity of road incidents, a labor-intensive luxury most cities can’t afford. Post-accident analysis is much more common. This study is using real-world historical data, rather than simulations, to identify high-risk intersections to help resolve issues and guide safer infrastructure decisions in the future.
  3. Open traffic data. Miovision Labs is working with the World Bank-led Open Transport Partnership to encourage more open, two-way data sharing between companies and transportation agencies. Access to private sector innovation will help resource-constrained agencies develop evidence-based solutions to traffic and road safety challenges. Miovision will share traffic data, as it is made open by its customers, to support this work.

The research being conducted through these partnerships represent important steps toward smart cities. They combine new data sources and new analytical techniques that will eventually become core pieces of city operations and planning.

Some companies talk about a top-down approach to smart cities. But that has never worked for a variety of reasons, the main one being exorbitant costs. Getting to the future won’t happen overnight, and these types of projects are critical to that progress. In our view, Miovision Labs is critical to that progress.