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Small Cities. Big Results.

The promise of smart cities is moving from hype to reality. And it seems that a lot of the attention is going to the big cities. Cities like New York, Dubai and Singapore and are getting the bulk of the attention from smart city vendors and the media.

All Cities Are Important

But what about smaller cities and towns? In America, more than one-half of the nation’s population live in cities and towns with fewer than 25,000 people. If we are to truly transform the world’s cities into efficient, data-driven, smart cities, we need to start paying attention to smaller cities.

Small Cities Doing Big Things

Maybe more attention should be paid to the small city, because it looks like they are delivering some big results. At Miovision, we’ve implemented Spectrum, our smart traffic signal technology, in small cities across North America. Cities like Waterloo Ontario, Northampton Massachusetts and Brossard Quebec have implemented smart traffic solutions, and they’re generating big results in a short period of time.  We’re talking about immediate cost savings, improved road capacity and reduced greenhouse gas emissions. But it’s not just our projects in small cities that delivering results. Other cities and projects are delivering big results too.

Waterloo, Ontario

Waterloo, Ontario

Santander, Spain with a population of about 180,000 people is a model for effective smart city deployment. They’ve implemented over 10,000 sensors across their city. Cherry Hill, New Jersey has implemented a sustainability dashboard to track how much it costs to heat, light and operate Township facilities, down to the square foot. And in Perth, a small city in Scotland, they are making major investments in smart waste, intelligent street lighting and open data initiatives.

The Small City Advantage

Why are smaller cities delivering better results. When implementing smart city initiatives, we’ve found that small cities actually have some key advantages over larger cities.

Smaller Projects, Quicker Results

Small cities typically have bounded problems. Problems like, we need to reduce congestion at these 6 intersections. Or, we need to track water usage for 50,000 people. Larger cities will typically provide a whole host of issues that require sophisticated planning and tracking. Small problems mean quicker implementation and faster results. Faster results translate into momentum and budget for more projects. Getting results in 6 months vs. 6 years means means cities can better justify current and future smart city initiatives.

Less Resources, Bigger Impact

Small cities have bigger constraints. They don’t have the luxury of a substantial traffic operations team. Constraints mean that benefits like automation, remote monitoring and analytics deliver bigger impact. It also means saving a big chunk of money relative to their budget.

Build on local assets

Small towns and rural communities are looking for ways to use local assets to strengthen their economies and provide better quality of life. By seamlessly overlaying smart sensors, software and cloud technology on existing infrastructure, cities can adopt data-driven management systems that will grow and adjust as their city does. No need to rip out and replace what’s already there – this infrastructure provides the foundation for a smarter city!

Join Our Webinar to Learn More

Still think smart traffic signals and smart city projects are only for the big boys? Join our next webinar and we’ll provide you some key examples that will make you think otherwise.

Introducing Miovision Labs

This week, we launched Miovision Labs. 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.

How Seattle Transformed a Dangerous Intersection Through Data

There’s an intersection in Seattle located in the Madison Park lakeside neighborhood where a ½ mile hill leads right into a populated business district. There sits Seattle’s busiest Starbucks location and a Wells Fargo Bank. This arterial connection is both the neighborhood’s busiest intersection for pedestrians and a city-designated school crossing location. Due to the rampant speeding and sight-line problems with this location, people walking often have difficulty crossing the street without trepidation.

Busy intersection at East Madison Street and McGilvra Boulevard East in Seattle

It took a serious collision between a cyclist and a pedestrian to force the city to try and fix the problems with the intersection. That’s where Bob Edmiston, his team from Seattle Neighborhood Greenways, and volunteers from Tableau saw an opportunity to make difference.

Their success would all depend on the data.

The Long and Winding Road to Funding

The plan for Seattle Neighborhood Greenways was to conceptualize and implement a safer intersection strategy for pedestrians. First, the team secured a $90,000 grant through Madison Park Community Council to enable the Seattle Department of Transportation (SDOT) to redesign the intersection.

However, to secure the addition $390,000 necessary to implement the changes, they would need to prove that the redesigned intersection would actually solve a problem. On top of that, there is a competitive pitching process for allocating grants in Seattle divided by district. Edmiston and his team were competing for the top spot against 15 other grant projects from the area. After initially failing to convince the decision makers of the value of the project by using an emotional appeal, a more persuasive approach was desperately needed.

If the team didn’t win the grant right now, their project would be dead.

Answering the Call with Data

Seattle Neighborhood Greenways sensed that they needed quantifiable proof of the improved safety of their solution. To collect the evidence necessary for a persuasive argument, Edmiston built a traffic counter that could record gaps in traffic with millisecond precision and conducted a gap analysis of the intersection. Seattle Neighborhood Greenways volunteer Troy Heerwagen worked with Edmiston to visualize the data using Tableau Public for ease of understanding.

Edmiston made some key observations:

  • During the critical 15-minute period before the morning school bell, there were only two opportunities with gaps long enough to walk across the street.
  • Crossing distance reductions provided by the curb extensions would reduce the crossing time enough to triple the number of safe crossing opportunities for pedestrians during the critical 30 minutes before the morning school bell, without requiring any changes to driver behavior or roadway function.
Intersection Video Data

Bob’s Visualized Data

After presenting the new data and logic to the East District Neighborhood Council, the people responsible for funding decisions were convinced that the project would, in fact, produce the safety outcomes it promised. They reversed their earlier decision to not fund the project and chose to make it their top priority for 2017 funding.

Don’t Underestimate the Data

Edmiston reminds us to not underestimate the data, when he says,

“data matters, counts matter, gap analysis matters. We would have been dead in the water without it. But it’s about being able to show data in a way people can understand and relate to. That’s an equally important part of the problem.”

If it weren’t for the data collected, Seattle’s busiest intersection would still be dangerous for pedestrians. More so, it was the way team presented the data through visualization that made it digestible and accessible to everyone.

Miovision is passionate about enabling other change-minded individuals to use data to justify their road safety solutions.

Want to learn more how we can help you leverage meaningful data? Contact us today.