FEATURE
The Next Line: |
MARK ESTRADA, |
Where Technology Meets the Trade |
“ CAPTN STRIPER” |
T wenty years ago, when I stepped into the pavement marking industry, the future looked a lot different. We measured parking lots by hand. We stretched string lines, snapped chalk, read plans, counted spaces and relied heavily on the experience of the person standing on the pavement. A good striper learned to see a parking lot before the first line ever hit the ground. Today, I can stand on that same pavement and watch a robot perform layout work while artificial intelligence helps analyze information that once took hours to process. That raises an obvious question: Where does the human fit into the future of striping? My answer is simple. Right in the middle of it.
Automation Is Already Here
When people hear“ automation,” they sometimes imagine machines replacing entire crews. I don’ t believe that’ s the most useful way to look at what is happening. The better question is: What parts of our work can technology make safer, faster and more consistent? Layout is a perfect example. Traditionally, laying out a large parking lot can require significant time spent measuring, marking and verifying dimensions before production begins. Robotic layout technology has the potential to automate portions of that process and give crews a highly accurate starting point. I’ ve had the opportunity to work with robotic layout technology in real-world conditions, and when you watch a machine establish points and connect a layout, it’ s difficult not to recognize where the industry is headed. But I’ ve also learned something equally important. A robot doesn’ t understand the parking lot the way an experienced striper does. It doesn’ t know that the plans don’ t quite match the existing conditions. It doesn’ t automatically understand why a particular transition will look wrong to the customer. It doesn’ t have years of experience telling it when something on paper simply doesn’ t make sense on pavement. That still takes a person.
AI Will Move Beyond the Paint Machine
The biggest changes may actually happen before the striping machine ever leaves the trailer.
Think about how much information moves through a pavement maintenance company every day: estimates, measurements, photographs, proposals, schedules, material quantities, production reports, safety documents, customer communications and job-cost information. Artificial intelligence can help contractors organize and interpret that information much faster. An estimator could use AI-assisted systems to analyze measurements and historical production data. Operations teams could identify scheduling conflicts earlier. Project managers could compare estimated quantities with actual material usage. Training programs could become interactive instead of relying solely on manuals sitting in a truck or office. Eventually, our equipment will likely become part of that same information loop. Imagine a striping machine that doesn’ t simply pump paint but also helps track application rates, material consumption, environmental conditions and production. That’ s when paint equipment begins to become something more than equipment. It becomes information.
Paint Has to Evolve, Too
Innovation cannot stop with robots and software. The material coming through the gun matters just as much. I’ ve spent time working around traffic paint development and studying how coatings perform under actual field conditions. Contractors deal with challenges that don’ t always show up on a product data sheet: premature tracking, color retention, drying conditions, surface contamination, heavy forklift traffic, tire wear and changing pavement conditions. The next generation of pavement markings should not focus only on durability. We should continue pushing for materials that provide better performance, greater visibility and safer surface characteristics while remaining practical for contractors to apply.
Eventually, smarter equipment and better materials may work together. Machines could help operators recognize when application conditions change. Production data could tell us how a coating actually performs over thousands of installations instead of relying solely on controlled testing. That feedback could help manufacturers build better paint and help contractors apply it correctly.
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