Artificial Intelligence for Roadway Maintenance
Who will you meet?
Cities are innovating, companies are pivoting, and start-ups are growing. Like you, every urban practitioner has a remarkable story of insight and challenge from the past year.
Meet these peers and discuss the future of cities in the new Meeting of the Minds Executive Cohort Program. Replace boring virtual summits with facilitated, online, small-group discussions where you can make real connections with extraordinary, like-minded people.
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 MeetingoftheMinds.org
Spotlighting innovations in urban sustainability and connected technology
In my business, we’d rather not be right. What gets a climate change expert out of bed in the morning is the desire to provide decision-makers with the best available science, and at the end of the day we go to bed hoping things won’t actually get as bad as our science tells us. That’s true whether you’re a physical or a social scientist.
Well, I’m one of the latter and Meeting of the Minds thought it would be valuable to republish an article I penned in January 2020. In that ancient past, only the most studious of news observers had heard of a virus in Wuhan, China, that was causing a lethal disease. Two months later we were in lockdown, all over the world, and while things have improved a lot in the US since November 2020, in many cities and nations around the world this is not the case. India is living through a COVID nightmare of untold proportions as we speak, and many nations have gone through wave after wave of this pandemic. The end is not in sight. It is not over. Not by a longshot.
And while the pandemic is raging, sea level continues to rise, heatwaves are killing people in one hemisphere or the other, droughts have devastated farmers, floods sent people fleeing to disaster shelters that are not the save havens we once thought them to be, wildfires consumed forests and all too many homes, and emissions dipped temporarily only to shoot up again as we try to go “back to normal.”
So, I’ll say another one of those things I wish I’ll be wrong about, but probably won’t: there is no “back to normal.” Not with climate change in an interdependent world.
I caught up with Steph Stoppenhagen from Black & Veatch the other day about their work on critical infrastructure in Las Vegas. In particular, we talked about the new Bleutech Park project which touts itself as an eco-entertainment park. They are deploying new technologies and materials to integrate water, energy, mobility, housing, and climate-smart solutions as they anticipate full-time residents and park visitors. Hear more from Steph about this new $7.5B high-tech biome in the desert.
Planning for new, shared modes of transit that will rival private vehicles in access and convenience requires a paradigm shift in the planning process. Rather than using traditional methods, we need to capture individual behavior while interacting with the systems in questions. An increasing number of studies show that combining agent-based simulation with activity-based travel demand modeling is a good approach. This approach creates a digital twin of the population of the city, with similar characteristics as their real-world counterparts. These synthetic individuals have activities to perform through the course of the day, and need to make mobility decisions to travel between activity locations. The entire transportation infrastructure of the city is replicated on a virtual platform that simulates real life scenarios. If individual behavior and the governing laws of the digital reality are accurately reproduced, large-scale mobility demand emerges from the bottom-up, reflecting the real-world incidences.