Artificial Intelligence for Roadway Maintenance
Augustus Caesar, the first Roman Emperor, thought good roads so important that he retained the title of Curator Viarum or ‘Commissioner of Roads.’ For Augustus, road maintenance and a strong defense were synonymous, and one of the prime duties of government. Road inspection in that day consisted of a chariot driver accompanied by one or two Lictors, or ‘Road Inspectors,’ who visually inspected the superficiem via and miliarium’, ‘road way’ and ‘road signs’ respectively, for overall condition. Along the way these inspectors were careful to make notes about what maintenance was needed, where and when, then share it with the local road crews.
While we use a variety of impressive advanced materials and technologies today to preserve our multimillion mile global network of paved road, surprisingly little has changed from Roman methods, save for substituting a Ford F150 for a chariot. Fortunately, advances in AI and machine learning are allowing our civil engineers to rethink when, where, why, and how we maintain roadways, and these changes could not happen soon enough.
The US road network of more than four million paved miles, built up over a century, and with an estimated replacement cost of $6.5T, is now showing its age. According to TRIP, a national transportation research group, 28 percent of major US roads are rated “poor” or in need of a complete rebuild, which translates into about $1.25 million per mile to re-mill and resurface a four-lane road. When, again according to TRIP, you add the burden of a $515 annual per vehicle cost for operations and maintenance upkeep of the US fleet of 260 million passenger cars then improved road quality is even more imperative. However, for most in the know about US roads the question is not if but how.
Yet the advent of a variety of clever, new, and tried and true technologies is causing a massive rethink of the way we manage and maintain roads. First, a growing web of road sensors, in the form of inductive loops, non-intrusive traffic detection devices, and video cameras on or along highways and urban streets are collecting vast amounts of data. Second, a far larger tsunami of roadway data is accumulating that will make the data generated data by Facebook, Amazon, and Google seem paltry by comparison: this will come from autonomous vehicles.
According to Brian Krzanich, Intel CEO and a leader in the emerging autonomous vehicle space, “Data is truly the new currency of the automotive world.” He added, “In an autonomous car we have to factor in cameras, radar, sonar, GPS and LIDAR … Run those numbers, and each autonomous vehicle will be generating approximately 4,000 GB – or 4 terabytes – of data a day.” If in the next few years only ten percent of the current US passenger fleet became self-driving then those 26 million vehicles would generate an astounding 38.4 zettabytes of data annually. To put that number in perspective, one year’s data production in this scenario is over eight times the volume of all the world’s current data.
That is a lot of data and, in fact, so much so that no single organization of any size on the planet currently has the capacity to manage and exploit it all. Nevertheless, some have started down this path. For example, Ford is investing $200 million in a new data center in Flat Rock, Michigan to support its own autonomous vehicle efforts and they expect their data storage requirements to grow from 13 petabytes now to over 200 petabytes by 2021.
Others are taking a collaborative approach to the massive data challenge similar to the Star, Oneworld, and SkyTeam airline alliances, where competing airlines share complex and expensive infrastructure to lower operating and capital costs which, in turn, lowers ticket prices for all consumers. A wide variety of autonomous vehicle industry players, including automakers, tech companies, equipment manufacturers, governments, civil engineering firms, to name a few, are working together in innovative ways to capture, fuse, and use the data that each is collecting separately. A prime example is the mapping company, which is owned in part by a consortium of the automotive giants Audi, BMW, and Daimler, as well as Intel. One likely and important outcome of this effort will be better roads for everyone.
One obvious beneficiary of all of this data will be the roadways themselves, which is not surprising given that roads and vehicles retain a symbiotic relationship. According to Andrew Ng, one of the world’s leading machine learning experts, one of the most important qualities of a roadway – for human and non-human driver alike – is predictability. Dr. Ng is adamant that most of the world’s roadways simply don’t make the grade. “The problem with poorly maintained roads is not only that they’re harder to navigate,” he asserted in a recent Wired article, “Self-Driving Cars Won’t Work Until We Change Our Roads,” “but that computers and humans are no longer able to accurately anticipate where others will drive, thus reducing predictability.”
The growing autonomous vehicle fleet, together with countless truck and passenger vehicle fleets on the road now, will be instrumental in passively – read inexpensively – gathering timely, precise, and local data that is so essential to better roads. With success, the centuries old process of manual inspection will be replaced with a more cost-effective methods for monitoring roads. While there are admittedly more technical solutions available for assessing road surfaces, including inspection vehicles that use combinations of RADAR, high-definition cameras, and LiDAR, these methods often come at a steep cost in terms of money and labor, a cost that dramatically limits the frequency of use and, for smaller municipalities, the affordability.
RoadBotics takes the view that still-better-than-good-enough data fidelity, extreme ease of use, vanishingly small implementation cost, makes for a powerful tool for roadway managers to use in maintaining a high road surface and roadway quality. “It’s cutting-edge technology. This has brought us up to the next level,” Richard Albert, Director of Public Works North Huntingdon, Pennsylvania, said. “We’re getting a lot of accolades for being part of this.”
The RoadBotics approach takes advantage of what is readily available, which includes a smartphone, a smartphone app, and a windshield to collect the data. Once the data is collected and sent to the cloud, the data is analyzed using advanced AI technology. RoadBotics then outputs the resulting information on the location, size, and type of damage for any defect identified and is reported to a city on an overhead map, using color-coded markers to superficially present the presence and degree of road damage.
All RoadBotics customers can drill further into the data, represented by map markers, to better understand their numeric evaluation of the defect, view photographic evidence of it, and even override ratings, on occasion and as necessary.
AI technology is all around us, including along the road, and as advances and the familiarity with autonomous vehicles grows, any number of opportunities to improve our roadways will emerge. We need only look at roadways as Caesar Augustus did, as one of our most precious assets, worthy of our greatest efforts.
Leave your comment below, or reply to others.
Read more from the Meeting of the Minds Blog
Spotlighting innovations in urban sustainability and connected technology
Behavior Change Case Study: Cleveland Neighborhood Progress – The Racial Equity & Inclusion Initiative
The approach taken by Erika and the Cleveland Neighborhood Progress team to develop and drive a racial equity and inclusion initiative is as multifaceted as it is inspiring. In developing and nurturing the projects and partnerships that comprised the Year of Awareness Building, the Cleveland Neighborhood Progress team appears to have drawn from many aspects of the EAST and MINDSPACE frameworks.
Instead of looking around and thinking to one’s self about what could be done to make a certain place better, our program urges folks to share their ideas and begin building support from the ground-up. That’s how a group of neighbors and volunteers in Brightmoor, Michigan, succeeded in transforming their neighborhood’s food desert back in 2015.
We are firm believers in putting nudge theory to work within organizations. Luum is a data-driven commute benefits software solution that runs end-to-end employer commute programs and gives them deep insight into how their employees commute. We’ve seen the ripple effect that even the slightest positive behavior changes around the commute can have for an entire organization and, subsequently, its city. Over the past five years, our hometown of Seattle has seen its transit ridership grow (one of two cities in the country!) and boasts a downtown drive-alone rate that hovers around 25 percent.