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.
Please note that this comment section is for thoughtful, on-topic discussions. Admin approval is required for all comments. Your comment may be edited if it contains grammatical errors. Low effort, self-promotional, or impolite comments will be deleted.
Read more from the Meeting of the Minds Blog
Spotlighting innovations in urban sustainability and connected technology
AI has enormous potential to improve the lives of billions of people living in cities and facing a multitude of challenges. However, a blind focus on the technological issues is not sufficient. We are already starting to see a moderation of the technocentric view of algorithmic salvation in New York City, which is the first city in the world to appoint a chief algorithm officer.
There are 7 primary forces determining the success of AI, of which technology is just one. Cities must realize that AI is not the quick technological fix that vendors sell. Not everything will be improved by creating more algorithms and technical prowess. We need to develop a more holistic approach to implementing AI in cities in order to harness the immense potential. We need to create a way to consider each of the seven forces when cities plan for the use of AI.
In New Zealand, persistent, concentrated advocacy and legal cases advanced by Māori people are inspiring biocentric policies; that is, those which recognize that people and nature, including living and non-living elements, are part of an interconnected whole. Along the way, tribal leaders and advocates are successfully making the case that nature; whole systems of rivers, lakes, forests, mountains, and more, deserves legal standing to ensure its protection. An early legislative “win” granted personhood status to the Te Urewera forest in 2014, which codified into law these moving lines:
“Te Urewera is ancient and enduring, a fortress of nature, alive with history; its scenery is abundant with mystery, adventure, and remote beauty … Te Urewera has an identity in and of itself, inspiring people to commit to its care.”
The Te Urewera Act of 2014 did more than redefine how a forest would be managed, it pushed forward the practical expression of a new policy paradigm.
Can U.S. cities transform to overcome extreme car dependency?
In summer 2019, two values driven agencies came together to see if they could incentivize change in five cities with the Made to Move Grant program. This innovative, unique, and inspirational partnership between Degree and Blue Zones is awarding $100,000 dollars to each city to redesign their neighborhoods and city-centers for active, healthy lives. The program aims to create model practices and projects that gain the attention of other cities and inspire evolutionary changes to once again focus on places for people, and design accordingly.