RoadBotics data is objective. That’s because our pavement assessments are done by our deep learning model, not humans.
Don’t get me wrong, I like humans. In fact, some of my best friends are humans. However, some of the things that make humans so great – our sociability, for example – also makes us less-than-ideal pavement assessors.
Humans are subjective
Road managers need to communicate why they’re doing what they’re doing. Here’s an exchange that might be familiar to them:
Constituent or Elected Official: (with a hint of contempt) Why are you repairing that road and not my road?
Road Manager: You see, we’ve put together a plan based on the condition of the roads that will keep them in the best condition while minimizing costs.
Constituent or Elected Official: (contempt rising) Yeah, but who determined the condition of the roads? You?
Even the most selfless civil servants cannot avoid suspicion from certain quarters that their road assessment was skewed in some politically-advantageous manner. The solution, of course, is to have a neutral third-party like RoadBotics do the assessment.
Windshield assessments are ineffective too
There are a lot of companies that will do a neutral assessment, but most municipalities are relying on manual windshield assessments. A windshield assessment is an exercise in concentration and judgement.
But, who among us is as sharp at the end of a workday as at the beginning? Imagine trying to maintain the same level of vigilance throughout an entire day of road rating. You’d have to be a machine.
Then there is the issue of what the assessor is seeing. They’d want to sit up front to get a good view. But are they looking down every few seconds to input data or keeping their coffee from spilling?
And what if they’re rolling by too fast to catch all of the distresses on a road? Do they see more distresses at stop lights because they have more time to take it all in?
A video record of the assessment would certainly help with this.
RoadBotics Operations Technicians drive around the entire road network while recording the data collection using a smartphone camera. The recorded video is then broken down into thousands of images of each 10-foot section. Then, each of the road images are analyzed by RoadBotics’ AI to receive a rating of 1 to 5.
Manual assessments are inconsistent
“Ride smoothness” ratings are often an integral part of manual assessments. But then how do you compare an assessment that was done in a tank-like SUV (say) to the experience of a driver in an old micro-compact? And does the driver dodge or swerve towards potholes?
RoadBotics has seriously considered these questions. Our conclusion was that the only way that anyone could provide objective data on this point would be to drive the same stretch of road tens or hundreds of times along slightly different paths. This wouldn’t be cost-effective for our customers.
One of the greatest responsibilities of a data analyst is to be transparent about the questions that their analysis cannot answer. A RoadBotics assessment does not try to incorporate any ride smoothness rating because we cannot provide direct, objective data on that point.
Find the solution just right for you
Companies that use LIDAR, a surveying method using laser, may provide objective and granular details of the road conditions. However, they are undeniably expensive with limitations in data collection, due to the size of the sensor vans.
RoadBotics, on the other hand, use smartphones to collect images of the roads and run the collected data through the AI model. Once the model completes its assessment of the road conditions, we post the ratings to the RoadWay platform and never touch it again!
In short, a RoadBotics assessment is objective, efficient and affordable – thanks to the automation and inexpensive equipment.