I’ve always been the kind of person who hates to do domestic chores – folding laundry, doing the dishes, and especially vacuuming the floor. Don’t ask me why. Maybe it’s because I believe there should be a better way of automating these time-consuming tasks while I work on better and more-interesting things. It’s not that I don’t know where the problems are. I can clearly see when the carpets are dirty or when the sink is full of dishes. I don’t have an issue gathering the data. I have an issue taking action to fix the problem – no time, no interest, and I’m just not that good at it.
Recently, we bought a Roomba at home. We’re quite a team, the Roomba and I. I’ve set it to vacuum the floors every other day at 1:00 pm, and if (through my powers of observation) I determine that the carpets need a little bit more attention, I take a few seconds to tweak the Roomba’s schedule or run it in a particular area. Together, we’ve managed to solve the problem of a dirty house. I collect data and adjust the Roomba to meet my needs, and the Roomba does the heavy lifting: the part I really don’t like to do.
Whether we know it or not, the Roomba Principle applies in so many areas of our everyday lives. In nearly every facet of life, tools have been developed to help us solve problems and free up our time to be more productive.
How To Apply Roomba Principle?
Given that basic reality, I’ve been somewhat surprised by a recent trend in the traffic industry that promotes investment in observation only. Over the last two years, tools that have been designed to automate data analysis and adjustment of traffic signal timing have been eclipsed by a demand for more and better data-collection systems. Don’t misunderstand: high-resolution data is an incredibly powerful ally in the traffic-signal optimization battle. With it, you can observe efficiencies and inefficiencies down to the smallest details and moments in time. Arrivals on red, arrivals on green, the delay of individual movements – all important data when it comes to better understanding how you can improve your signal timing. But data alone is a half-measure that does not provide the solution.
It’d be like observing my carpets with a microscope. If I collected data that showed 18 dirt particles per square inch within a two-and-a-half foot radius around the front door as compared to eight per square inch in front of the couch, I may know where I need to focus my cleaning efforts. But without my Roomba to follow up, I’m armed with a lot of really interesting yet useless data. My house guests would be less than impressed with reams of data about how dirty my floors are if I didn’t actually clean them.
Since becoming a part of the traffic industry, I’ve heard from hundreds of agencies about the lack of time, lack of staff and lack of resources when it comes to actually fixing signal timing challenges. Those challenges continue, and vendors in the traffic industry owe it to our agency partners to provide consulting, products, and services that matter and actually address their challenges. All the data in the world doesn’t make a corridor flow more efficiently. It won’t ease a frustrated motorist’s mind to know that you’ve got detail to the Nth degree on how poorly your signals are timed. An overabundance of information won’t reduce emissions or improve safety. Data in and of itself is a half-measure, and without the time / staff / resources to take action on the data, traffic industry vendors are doing a disservice to our agency partners by promoting the sizzle and forgetting the steak.
Data is important. We shouldn’t forget it. But we have an obligation to also provide you with the best tools in the industry to fix what the data shows is broken. Keep the solution in mind when deploying your next round of technology upgrades, because knowing your floors are dirty alone won’t solve the problem of cleaning them.
