Many cities today are growing rapidly as the world becomes more urban. A 2014 World Health Organization study estimated that urban areas account for 54% of the global population, up from 34% in 1960 and by 2050, 66% of the World’s population will live in an urban area. It is this accelerated growth that places an ever-increasing strain on city infrastructure and services. Arguably, one of the most visible strains is on the areas of transportation, traffic congestion, and parking.

Congestion and travel times within urban areas are two key factors behind the Department of Transportation’s recent Smart City Challenge, the winner of which, Columbus, Ohio was awarded $50 million in funding, combined with another $90 million in private backing.

With transportation paving the way for the development of numerous smart city plans, there is a great deal of debate over which components of the transportation infrastructure should be addressed first. Several studies show that in congested US urban areas, up to 30% of traffic is caused by drivers looking for parking. Given the lack of easily accessible parking, one of the main goals should be to get cars into parking spaces faster and with less effort on the part of the driver. However, quite often there is a debate over the appropriate method for achieving these results and how the necessary funding to roll out the appropriate plans will be secured.

At Fybr, we believe that to achieve a true smart city transportation plan, it is essential to have accurate, reliable, and real-time data. By putting a sensor in every space – including multi-use lanes and restricted parking zones such as those in front of fire hydrants and loading zones – occupancy can be monitored in real-time. This gives the most accurate snapshot of a city’s parking ecosystem and eliminates guesswork. While the value of this real-time data is undeniable, many incorrectly assume that it is expensive and difficult to obtain – leading to the rise of concepts such as indicative sensing (only monitoring a random handful of spaces) or predictive analysis (using historical and ancillary data to estimate which spots will be occupied). While both of these concepts have the ability to provide some beneficial data, they fall short on delivering the kind of real-time, accurate, and actionable information necessary.

For example, Sidewalk Labs, an Alphabet (Google) company, is using predictive analysis by gathering consumer data from Google Maps and search requests to attempt to connect drivers with open spaces. This relies heavily on consumers to “push” that data, meaning the platform is dependent on consumer use and adoption. While this information may be beneficial for things like traffic flow, it falls short when it comes to determining parking occupancy at the single space level.

Another method of predictive analysis relies on parking meter payment information to determine the occupancy state of a space. The logic that a space that is paid for is occupied and a space that is expired is vacant builds a far from accurate picture of space occupancy, leading to inaccurate wayfinding, while leaving many of the potential benefits of a smart parking system out of reach. For example, a driver may pay for an hour, but only park for 45 minutes. A platform that only uses parking payment information would then inaccurately report the state of that space for 25% of the hour. Conversely, a space that is expired but still occupied would report false availability.

We also believe that using sensors to change and update enforcement methods is equally important in developing smarter parking ecosystems that can positively impact traffic congestion. Without data on occupancy, officers waste time circling, while statistics show that as many as 97% of violations are missed in most cities. With this much inefficiency, drivers are willing to “risk” a ticket, thereby taking advantage of time limits and no parking zones – costing cities valuable uncaptured meter revenue. However, with single space sensing, enforcement officers have access to real-time occupancy data – helping them be fair and efficient. Greater efficiency in addressing violations leads to greater compliance and an increase in both revenue and space availability and turnover.

Plus, with the growing popularity of digital devices, the combination of sensors and mobile payments apps can eliminate the high cost of purchasing, installing, and maintaining meters altogether.  In fact, many developing nations are bypassing meters altogether and relying solely on mobile payment apps and “sensing”, eliminating meters altogether in much the way these same nations skipped over land lines and went directly to mobile phones.

Sensors Are More Than Affordable

Putting sensors in every space should be viewed as a cost savings and revenue/service enhancement initiative. In most cases, the single space sensor system will pay for itself in a relatively short amount of time.  In places where parking demand is high and virtually insensitive to price, sensors can help the city build and manage a precise demand-based pricing model and review the impact of price changes in real time.  Getting meter pricing and enforcement in the right balance can virtually eliminate areas of congestion while creating higher space turnover that will benefit merchants and consumers.  In areas where demand is lower, it may make sense to measure the impact of reducing prices to achieve the best overall result.

A modest 2-3% increase of ticketed violations through directed enforcement alone could cover the cost of system implementation. Couple that with the fact that Fybr sensors have the ability to do meter resetting – zeroing out time left on a meter when a person leaves a space – and real-time demand-based pricing, and cities can pay for a single space sensor system in as little as a year and generate ongoing positive cash flow for a community – helping fund other smart city initiatives.

Drivers see increased value from sensing as well. With single space sensing, parking occupancy data becomes exponentially more accurate with almost no latency, making wayfinding applications much more reliable and leading to a more efficient transportation experience.  In areas where there is a high demand for parking, drivers want real-time, accurate information on occupancy versus estimates, probabilities, and predictive analytics.

Breaking Down the Costs


Consider this sample scenario:

Fybr’s fully-inclusive cost to City A for 1,000 sensors is approximately $225 per sensor installed and $9 per month for a five-year contract to provide parking data and various applications. This works out to $153 per year per sensored space or $12.75 per space per month, 42.5 cents per day, etc.

With a parking rate of $3.00 per hour to park for 8 hours per day for 310 days per year (365 minus Sundays and holidays), City A’s maximum revenue per meter per year is $7,440. Following average city collection rates of 60% of the maximum possible revenue, annual meter revenue per meter would be $4,464 annually. For enforcement, a typical city has parking ticket revenue that is almost equal to paid meter revenue on a per meter basis (this, by the way, is achieved by ticketing less than 10% of parking offenses).  This would increase City A’s per meter revenue to $8,928.

To break even against the $153 cost of a single space sensor, City A could:

  • Write 3.4 more parking tickets per year per meter (assumes $45 ticket).
  • See an increase in paid compliance of 3.4% from people who normally don’t pay.
  • Increase the rate per hour by about 5 cents.
  • Some combination of the above


What’s Really Smart

The bottom line is, the fully loaded cost of the Fybr sensor system really is fairly minor and it is one of the easiest solutions to implement. It doesn’t take much system-wide improvement to recover the cost and start generating additional revenue from normal operations. Plus, the high-value benefits of significantly reduced congestion, enhanced safety, lower carbon footprint, and the impact of a smart parking system on a city’s environmental sustainability and livability can’t be beat. Fybr’s complete system is very affordable and the real hard-dollar ROI might be among the highest of smart city investments. Paired with predictive analytics from companies like Sidewalk Labs and Apple, a broader and more accurate picture of activity within a community is possible, bringing the true vision of a Smart City to life.