Recently, I’ve had to make regular trips to a location which is only accessible by car. As one who travels nearly everywhere by bicycle or foot, I’m not accustomed to spending so much time behind the wheel. To complicate matters, my lack of regular driving hasn’t kept my Boston driving skills honed, so I must be vigilant to avoid a catastrophic crash.
One of the few positive things about these trips by car is the amount of time I spend sitting in traffic with nothing to do. I do not use electronic devices while driving. Therefore, at every light, I have time to contemplate the wonders of the universe as I await the almighty green light, which will allow me to proceed on my way.
On one particular day, my departure was delayed, forcing me to race through traffic at breakneck speed. It occurred to me that if I studied the traffic patterns closely enough, I could shave some time off of my trip, not only that day, but every day going forward.
To begin my analysis, I studied the movement of the lanes. For the first half mile of the trip, traffic in the right lane moved faster than the left. This was surprising since there were numerous driveways, and several streets, where cars could enter and exit the road.
After passing the first major intersection, the speed of the traffic in the left lane increased. By following the flow of the traffic, I was able to move up four spaces relative to the cars which had been stopped near me at the light. I learned that at a particular intersection where many cars turned left, I would have to quickly maneuver into the right lane to avoid getting trapped behind the huge line of cars queuing up in the left turn lane.
Once I passed through that intersection, pulling back into the left lane would allow me to pick up some time. Two more intersections required staying in the left lane until I reached the last intersection where I would move into the left turn lane to turn onto the road where my trip would end.
For a couple of days, I tested this system, and found it to be fairly consistent. But, then one day, as I was sitting behind a huge truck at an intersection, it occurred to me that I could fine tune my system by more accurately predicting the traffic patterns along my route.
What’s more, I could utilize this information when riding my bike to improve my travel time and reduce the risk of an accident. Just as I was thinking this, a ridiculously tricked out pseudo-sports car pulled up alongside me.
The driver was revving his engine to make his presence known. The car itself, was obnoxiously noisy, making me wonder whatever happened to the laws against unregulated mufflers. In the absence of such laws, one would think that the local noise ordinance would come into play, but to enforce it, someone would have to keep up with the revving car which was itching to fly down the road, as soon as it found an opening in traffic.
At that moment, as I stared in disbelief at this noise and air pollution generating machine, it occurred to me that I could make predictions about the type of cars around me to determine the flow of traffic. For instance, it would not be difficult to predict that a tricked out car, or any sports car for that matter, would be driving faster than the average speed on the road.
It would also stand to reason that such a car would weave in and out of traffic, just like the cars in a video game, to maintain its speed and beat out the cars around it. Not surprisingly, this driver gunned the accelerator at the first hint of a green light. He was off and running.
Unfortunately, in front of me sat an old beat up car with a fading paint job. How difficult would it be to predict that a rundown sedan would drive slowly with an inattentive driver? Just as I imagined, the driver was slow to react to the green light. Both cars behind me blasted the horn out of impatience with this daydreaming wonder.
As his car drifted over the white line into the adjacent lane, his driving revealed itself as careless and unmeasured. Looking down at the dashboard instead of the road ahead of him, his car weaved back and forth, swaying in the breeze of uncertainty. Getting away from him was mandatory to avoid an accident.
I passed him on the left and accelerated to move away from him. Directly in my train of sight, stood an unwieldy minivan. Such a car would probably be occupied by a parent with children who would drive slowly out of concern for the offspring, and who might be distracted by their actions. A lumbering vehicle of this type was not the sort any serious Boston driver would want to be stuck behind, so I made a note to myself to switch lanes as early as possible any time such a car came into view.
Behind me, an obscenely expensive luxury car crept up my bumper. I was not surprised by the driver’s impatience. Despite her dangerous attempts to nudge my car forward, when there was nowhere for me to go, she glanced into the rearview mirror repeatedly to check and touch up her makeup. The combination of aggression and distraction came across as particularly dangerous, so I made a mental note to myself to be wary of expensive cars with entitlement minded drivers behind the wheel.
The longer I studied the cars, the better I became at predicting car behavior based on the type of car, the condition of the car, the size of the car and the color of the car — muted colors seemed to be more low-key, while screaming, bold cars were more likely to be aggressive.
Once home, I climbed onto my bike, where I felt more at home, and set out to test my theory in a car and bicycle road sharing scenario. Sure enough, based on my predictions about the type of car, I could position my bike to be in the right place at the right time.
Not only did this improve my ability to maneuver in traffic, but it also led me to conclude that many strategies used behind the wheel can be employed when riding a bike. So, despite my dislike of time spent waiting behind the wheel, I felt that I had spent my time well and had discovered something my highly attuned bicycling state would never have allowed me to observe.