The Value of Single-Space Sensing
A recent study found that, in the United States, drivers on average lost 99 hours in 2019 due to congestion—two hours more than in 2017, and the highest annual amount measured to date. Urban growth trends further complicate the picture—currently over 55% of the world’s population lives in urban areas, and by 2050, that number will exceed 68%.
Simply put, our cities are becoming more populous, and our city streets more congested.
Add to this the complications created by the COVID-19 pandemic; as many cities “reopen” and encourage renewed commerce, evidence strongly suggests that more people than ever will eskew public transportation and opt to drive themselves, choosing the now-familiar social distance that personal vehicles afford. Curbside management, too, has become a pressing challenge, due to the proliferation of delivery vehicles and couriers of all types.
These intersecting circumstances clearly indicate that congested roads, persistent traffic and scarce parking are issues that demand attention now, for cities and drivers alike. At a time when the vast majority of cities find their budgets dwindling and are looking to cut services and costs rather than incorporate new solutions, these issues can seem daunting. A smart city transportation plan, however, is the rare technology that can mitigate traffic issues while creating new revenue streams that, in essence, pay for themselves and more.
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 parking 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—giving the most accurate view of a city’s parking ecosystem while eliminating guesswork.
While the value of this real-time data is undeniable, many incorrectly assume that it is expensive and/or difficult to obtain
While the value of this real-time data is undeniable, many incorrectly assume that it is expensive and/or 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 to maximize revenue while providing optimal value to the community—particularly in a time when there are more delivery vehicles and drivers competing for available spaces.
A vast majority of mobility companies using AI rely heavily on data gathered from smartphones, or other tangential data. The challenges with some of these systems is twofold: First, latency of the data becomes an issue. Mobility is an area where the timeliness of data is critical. This then leads to the second issue—accuracy. If the data isn’t fresh, it is often inaccurate. In the case of parking, data can lose its relevance and value in as little as a few seconds.
If the data isn’t fresh, it is often inaccurate. In the case of parking, data can lose its relevance and value in as little as a few seconds.
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 can build a vastly inaccurate picture of space occupancy. This leads 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.
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, parking enforcement personnel waste time circling, with statistics showing that as many as 93% 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 payment apps can eliminate the high cost of purchasing, installing, and maintaining meters altogether. Many developing nations are bypassing meters altogether and relying solely on mobile payment apps and “sensing”—eliminating meters in much the way these same nations skipped over landlines and went directly to mobile phones.
Putting sensors in every space should be viewed as a cost-saving measure, and as both a revenue and 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. Arriving at the right balance between pricing and parking enforcement 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. Add to that the fact that single-space sensors are equipped with meter resetting abilities—they can zero out time left on a meter when a person leaves a space—and real-time demand-based pricing, both measures designed to maximize efficiency and revenue. Ticketing frequency can be enriched further by a small increase in the cost of parking itself; as little as a nickel per space hourly can add up to a considerable windfall. These measures, and the resultant cash flow they enable, makes it possible for cities to pay for a single space sensor system in as little as a year and generate ongoing positive revenue for their community—helping cities with budget deficits or to fund other smart city initiatives down the line.
Cities can pay for a single space sensor system in as little as a year and generate ongoing positive revenue for their community.
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. In fact, a recent INRIX study indicated that the one technology drivers want most is the ability to access turn-by-turn directions to available parking in real time—a technology that single-space sensing places well within reach.
With traffic at its peak, and more cars taking to the road as communities ease back into full operation, mitigating congestion, managing curbsides, and smartly-leveraging existing parking inventory is vital. The environmental upside and safety benefits of single-space sensing are difficult to ignore—less pollution, a lower carbon footprint, and truly touchless operation that removes the need for physical contact with parking meters—all add to the appeal. The fact that parking sensor technology can accomplish all these things and more while becoming an incremental source of revenue makes it all but essential. A smart city transportation plan is only as good as the data it collects, and single-space sensors are the most accurate, the most convenient, most responsible, and the most cost-effective technology available.
A less-congested, more-profitable, smarter tomorrow awaits those cities willing to invest today.
The Columbus Division of Parking Services embraces the spirit of innovation and the use of technology to create an optimal on-street parking experience in its urban neighborhoods.
Fybr proudly announces Linnell Gordon will be assuming the position of Senior Vice President, Software Engineering. Beginning with Fybr as a consultant in 2014, Gordon was initially brought on board to support the Java-based infrastructure in place at the time.
Camera-based image recognition systems are built on a simple premise- they are electronic “eyes” capable of recognizing unique items and capturing data. The unique item can be everything from a license plate number to a human face. Once recognized and captured, the resultant data can be applied to a wide variety of use cases, such as parking, traffic, or law enforcement. This simple premise is easy to grasp, and its outcome is a highly-desirable one. But as with many technologies, what works in theory under controlled conditions and what works in practice in real-world situations aren’t always the same.