After many discussions with coworkers, friends, and neighbors (okay, my neighbors don’t really talk to each other, let alone me) about the subject of how people behave when they get behind the wheel in traffic, I decided it was time to apply some scientific mumbo-jumbo.


Equipped with my trusty smartphone, I used Google Keep to capture my voice notes while driving.  That way I didn’t have to look away at my phone.  I was blurting out voice notes like a Tourette’s patient at a Sam’s Club on a Saturday morning.  So far, I’ve kept about 5-1/2 weeks of data and entered into a spreadsheet.

But before I share the dirty details, I need to qualify my constraints and rationale a bit (I earned a 4.0 in statistical analysis I, II and III in college, which is why I name projects things like “A-hole”).

It’s worth noting that my daily commute is along Great Neck road, London Bridge road, Dam Neck road, and Princess Anne road.  Overall, I cover that “path” about 60% of an average week. The other 40% are evenly spread over the other areas (shown below), such as Lynnhaven Pkwy, Rosemont road, Holland road, Independence blvd, Virginia Beach blvd, and Shore Drive.  The roads highlighted in yellow (image below) are abused by my little car every week.  The green path is my typical daily commute.



My goal was to quantify the frequency and tendency of bad drivers with regards to type of vehicle and general areas of the city in which I live, work and traverse every day.  The “general areas” are actually constrained as roadways, which is valid, since I’m measuring asswipetivity factors exhibited by other drivers from my own vehicle point of view.


I already described the physical constraint (roadways), but I also had to constrain my criteria.  Here’s how I qualified each “event” as it related to “bad driving”:

  1. Following another vehicle with less than 4 feet distance between bumpers when both vehicles are traveling faster than the posted speed limit.
  2. Changing lanes whereby the driver does not use a signal AND cuts in front of another vehicle in the destination lane with less than two vehicle lengths available AND the vehicles are traveling faster than the posted speed limit.
  3. Changing lanes whereby the driver does not use a signal AND cuts in front of another vehicle in the destination lane with less than a vehicle length ahead of the car in the destination lane AND using the opportunity to apply brakes and make a sudden turn.
  4. Speeding up in the left lane to cut in front of a vehicle traveling in the right lane which is the last vehicle in a line of vehicles traveling at the same rate AND where there are no trailing vehicles for at least a half-mile behind the vehicle being cut in front of AND where all vehicles are traveling faster than the posted speed limit.

Basically: asshole driving behavior, or ADB, syndrome.  We might find a cure for this some day, but for now, the only chance for a cure would be computer-operated vehicles. But humans prefer killing each other, and themselves, with their own two hands (one doing the texting, of course).

I’ve discussed my criteria over the years with both police officers and insurance agents on quite a few occasion, and they all concur: these are well-known traits of “bad driving“.

The Data

Here’s what I’ve compiled….


Here’s the legend of road abbreviations…


And here’s the charts for each table.  The first chart is based on vehicle type, while the second is focused on the roadway being traveled.




Most of the aggressive, boneheaded vehicle driving behavior tended to be associated with personal SUV drivers, with personal Cars in a close second place.  I was a bit surprised that personal Trucks weren’t at least number two, but this is just my limited sampling.  Also, the most abusive driving I found (again, from my own personal analysis) was along Great Neck road and London Bridge road.

There are probably a lot of possible explanations as to why these two roads align with the vehicle types, and driving habits.  I can’t say for sure, since I have only these bits of data to go on.  However, the results do support my hopes for vehicles that are entirely driven by computers, rather than humans.

Happy Driving! 🙂


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