

Strengthen enforcement and penalties for left lane camping
The Issue
One of the most ineffective frameworks of penal statutes and enforcement operation is that regarding traffic law. Police are overly invested with preventing and enforcing speeding and other "reckless acts" under a false pretense that preventing a single outcome or a root cause is more effective than preventing the root cause.
The left lane camping epidemic in Florida is a particularly significant case study of enforcement until this false pretense. Here I attempt to prove that if the "keep right pass left" rule were to be more strongly emphasized in drivers’ education, signage, messaging, etc, and if it were to actually be enforced with teeth, there would be less accidents on the road.
Take any road scene
Let:
X ∈{0, 1} be a dummy random variable indicating event that there is a bad driver on scene
- X = 1 -> there is a bad driver
- X = 0 -> there is no bad driver
Y ∈{0, 1} be a dummy random variable indicating the event that there is a speeder on scene
A = event of an accident on scene
Z = all other events/conditions that can cause an accident, which we assume to be constant (like road conditions)
Naturally, P(A|b) ≥ 0, where b is all possible events (or lack thereof), so an accident is always possible.
Then
P(A|X=1∩Y=1∩Z) ≥ P(A|X=1∩Z)
but P(A|X=0∩Y=1∩Z) = P(A|X=0∩Y=0∩Z) meaning that without the presence of a bad driver, the presence of a speeder does not increase the probability of an accident.
Of course, one could say speeding in bad road conditions renders a driver reckless and therefore, bad. Nevertheless, it is the context around how the driver speeds that increases accident likelihood, not speeding alone. A driver going 200 mph in an straight open road is not going to cause an accident if the driver is not a bad driver or does not interact with a bad driver, it takes at least 1 party's bad/reckless decision or negligence combined with the speeding for the speeding to increase accident likelihood.
Yet, speeding is so often targeted as if it were the X parameter instead of the Y. One of the biggest actions that renders a driver a bad driver and activates the X parameter: left lane camping
Now, admittedly, we know that no driver on any driving scene is perfect, so in any driving scene with a strictly positive number of drivers P(X = 1) = 1 (bad drivers guaranteed). We can still prove that enforcing left lane camping is more effective than enforcing other “reckless” violations like speeding.
Let L∈ {0,1} be another dummy random variable that indicates the presence of a left lane camper. For simplicity, we'll limit one left lane camper for any single scene.
Under the conditions allowing for C = 0, we would let Rc represent the aggregate severity of any bad reckless behavior on the scene that can increase accident likelihood irrespective of X, where c is a whole number (technically c ∈ [0,∞) since reckless driving can always be worse.
So if c = 10, for example, Rc ≡ R10, which just means R = 10
We let Yc represent encompass all actions that do not increase accident likelihood in the absence of bad driving (X = 0), but compound the increased risk induced by presence of a bad driver (like speeding).
However, we know that X = 1, so P(A) is always strictly increasing in both R and Y, irrespective of X (though likely in different proportions), so we can just encapsulate both behaviors under 1 variable Y
So, P(A|Yc) strictly increasing in c (the aggregate severity of bad driving behavior)
We can define Y as Yc = ∑Yci where Yci is one individual driver's contribution to the probability of an accident, represented by the severity of their reckless behavior.
i ∈{1,n} where n is the total number of drivers on the scene. We will have n ≥ 3 since this is the minimum number of drivers needed for a left lane camper to wreak havoc (+1 for the camper, +1 for there needing to be at least vehicle in a righter lane attempting to be passed in the left lane by another driver (+1 for the vehicle trying to pass).
Now, when a driver is riding the left lane, we know, empirically, that driver aggravation spikes significantly. We assume (and can verify empirically) that driver aggravation is a direct cause of driver aggression which is a direct cause of dangerous/reckless/bad driving (Y).
So, we can say Y = b + dL where b (≥ 0) represents the aggregate contribution to accident risk represented by the aggregate level/severity of bad driving behavior that is not caused by a left lane camper.
d (≥0) represents the conditional (on L) increase in accident probability <=> severity of bad driving behavior that IS caused by a left lane camper.
While this can't be proven mathematically, I would strongly argue and therefore assume that, b > d and so when L = 1, dL > b
Now, we let e represent any actions/conditions that lead to accidents that are not Y (reckless/bad driving) (vehicle malfunctions, poor road conditions etc).
We can write P(A) = Yc + e
and P(A) = b + dL + e
Since we assume b < d, setting d = 0 has a stronger pulldown on accident risk than setting b = 0. Under this assumption, we already prove that pulling over a left lane camper is more effective than pulling over even every single reckless driver, since (there would be no camper-induced) reckless drivers
Even if we relax the assumption that b > d, we have to remember that b and d are aggregates for all drivers 1 through n in the driving scene, so
b = ∑bi and d = ∑di where i represents an individual driver (di and bi are the risk factors of a single reckless driver)
This is where we suggest the proof that enforcing left lane camping is more effective at reducing accidents than enforcing other reckless behaviors such as speeding:
If a trooper pulls over the left lane camper on the scene, L = 0 (assuming the pull over deters recidivism), then P(A) = b + e
If a trooper decides to pull over a reckless driver on the scene,
either d drops to d' and d'= ∑di - dj and b remains constant (if the reckless act was a result of the camper)
- dj ∈ di
or b drops to b' = ∑bi - bj where bj just indicates the action is of one single driver from 1 to n.
So P(A) = ∑bi - bj + dL + e
or P(A) = b + (∑bi - dj)L + e
Now, for this to be more effective in case 1, ∑bi - bj + dL + e < b + e
=> ∑bi - bj + dL < b
=> ∑bi - bj + dL < ∑bi
=> - bj + (∑di)L < 0, L = 1
=> bj > ∑di
which means that the recklessness of that one driver is of greater severity than the combined recklessness of all drivers engaging in reckless behavior as a result of the left lane camper. Drive one mile in I4 to confirm that this is not true and assert bj > ∑di
For this to be more effective in case 2, b + (∑di - dj)L + e < b + e
=> ∑di - dj < 0
This cannot be true since ∑di = ∑di¬j + dj (all camper-induced recklessness from drivers except driver j plus the recklessness of driver j)
which would mean ∑di¬j + dj < dj
=> ∑di¬j > 0 and which cannot make sense since we assume d > 0 (no negative recklessness among reckless drivers)
Which means the ONLY way pulling over a driver for a reckless act like speeding or tailgating is more effective than pulling over a left lane camper in any driving scene is if the recklessness of that one driver is of greater severity than the combined recklessness of all drivers engaging in reckless behavior as a result of the left lane camper; the empirical evidence is so strong (drive from St. Petersburg to Orlando on I4 before attempting to disprove) that we can prove this scenario to be outside the sample space of all outcomes in the driving scene. We can therefore prove they enforcing left lane camping is more effective than enforcing other reckless acts like speeding,
Q.E.D.
I frequently drive from St. Petersburg to Orlando on I4 and run into an average of 5-6 traffic blockages caused by a left lane camper. I witness the resulting reckless behavior this causes other drivers to engage in. While the proof is theoretical, it is strongly backed by empiricism from myself and countless others.
I am calling on the Florida DOT to push for tougher penalties for left land camping, and for Florida highway patrol to drastically strengthen enforcement on this careless, reckless and dangerous activity.
The Issue
One of the most ineffective frameworks of penal statutes and enforcement operation is that regarding traffic law. Police are overly invested with preventing and enforcing speeding and other "reckless acts" under a false pretense that preventing a single outcome or a root cause is more effective than preventing the root cause.
The left lane camping epidemic in Florida is a particularly significant case study of enforcement until this false pretense. Here I attempt to prove that if the "keep right pass left" rule were to be more strongly emphasized in drivers’ education, signage, messaging, etc, and if it were to actually be enforced with teeth, there would be less accidents on the road.
Take any road scene
Let:
X ∈{0, 1} be a dummy random variable indicating event that there is a bad driver on scene
- X = 1 -> there is a bad driver
- X = 0 -> there is no bad driver
Y ∈{0, 1} be a dummy random variable indicating the event that there is a speeder on scene
A = event of an accident on scene
Z = all other events/conditions that can cause an accident, which we assume to be constant (like road conditions)
Naturally, P(A|b) ≥ 0, where b is all possible events (or lack thereof), so an accident is always possible.
Then
P(A|X=1∩Y=1∩Z) ≥ P(A|X=1∩Z)
but P(A|X=0∩Y=1∩Z) = P(A|X=0∩Y=0∩Z) meaning that without the presence of a bad driver, the presence of a speeder does not increase the probability of an accident.
Of course, one could say speeding in bad road conditions renders a driver reckless and therefore, bad. Nevertheless, it is the context around how the driver speeds that increases accident likelihood, not speeding alone. A driver going 200 mph in an straight open road is not going to cause an accident if the driver is not a bad driver or does not interact with a bad driver, it takes at least 1 party's bad/reckless decision or negligence combined with the speeding for the speeding to increase accident likelihood.
Yet, speeding is so often targeted as if it were the X parameter instead of the Y. One of the biggest actions that renders a driver a bad driver and activates the X parameter: left lane camping
Now, admittedly, we know that no driver on any driving scene is perfect, so in any driving scene with a strictly positive number of drivers P(X = 1) = 1 (bad drivers guaranteed). We can still prove that enforcing left lane camping is more effective than enforcing other “reckless” violations like speeding.
Let L∈ {0,1} be another dummy random variable that indicates the presence of a left lane camper. For simplicity, we'll limit one left lane camper for any single scene.
Under the conditions allowing for C = 0, we would let Rc represent the aggregate severity of any bad reckless behavior on the scene that can increase accident likelihood irrespective of X, where c is a whole number (technically c ∈ [0,∞) since reckless driving can always be worse.
So if c = 10, for example, Rc ≡ R10, which just means R = 10
We let Yc represent encompass all actions that do not increase accident likelihood in the absence of bad driving (X = 0), but compound the increased risk induced by presence of a bad driver (like speeding).
However, we know that X = 1, so P(A) is always strictly increasing in both R and Y, irrespective of X (though likely in different proportions), so we can just encapsulate both behaviors under 1 variable Y
So, P(A|Yc) strictly increasing in c (the aggregate severity of bad driving behavior)
We can define Y as Yc = ∑Yci where Yci is one individual driver's contribution to the probability of an accident, represented by the severity of their reckless behavior.
i ∈{1,n} where n is the total number of drivers on the scene. We will have n ≥ 3 since this is the minimum number of drivers needed for a left lane camper to wreak havoc (+1 for the camper, +1 for there needing to be at least vehicle in a righter lane attempting to be passed in the left lane by another driver (+1 for the vehicle trying to pass).
Now, when a driver is riding the left lane, we know, empirically, that driver aggravation spikes significantly. We assume (and can verify empirically) that driver aggravation is a direct cause of driver aggression which is a direct cause of dangerous/reckless/bad driving (Y).
So, we can say Y = b + dL where b (≥ 0) represents the aggregate contribution to accident risk represented by the aggregate level/severity of bad driving behavior that is not caused by a left lane camper.
d (≥0) represents the conditional (on L) increase in accident probability <=> severity of bad driving behavior that IS caused by a left lane camper.
While this can't be proven mathematically, I would strongly argue and therefore assume that, b > d and so when L = 1, dL > b
Now, we let e represent any actions/conditions that lead to accidents that are not Y (reckless/bad driving) (vehicle malfunctions, poor road conditions etc).
We can write P(A) = Yc + e
and P(A) = b + dL + e
Since we assume b < d, setting d = 0 has a stronger pulldown on accident risk than setting b = 0. Under this assumption, we already prove that pulling over a left lane camper is more effective than pulling over even every single reckless driver, since (there would be no camper-induced) reckless drivers
Even if we relax the assumption that b > d, we have to remember that b and d are aggregates for all drivers 1 through n in the driving scene, so
b = ∑bi and d = ∑di where i represents an individual driver (di and bi are the risk factors of a single reckless driver)
This is where we suggest the proof that enforcing left lane camping is more effective at reducing accidents than enforcing other reckless behaviors such as speeding:
If a trooper pulls over the left lane camper on the scene, L = 0 (assuming the pull over deters recidivism), then P(A) = b + e
If a trooper decides to pull over a reckless driver on the scene,
either d drops to d' and d'= ∑di - dj and b remains constant (if the reckless act was a result of the camper)
- dj ∈ di
or b drops to b' = ∑bi - bj where bj just indicates the action is of one single driver from 1 to n.
So P(A) = ∑bi - bj + dL + e
or P(A) = b + (∑bi - dj)L + e
Now, for this to be more effective in case 1, ∑bi - bj + dL + e < b + e
=> ∑bi - bj + dL < b
=> ∑bi - bj + dL < ∑bi
=> - bj + (∑di)L < 0, L = 1
=> bj > ∑di
which means that the recklessness of that one driver is of greater severity than the combined recklessness of all drivers engaging in reckless behavior as a result of the left lane camper. Drive one mile in I4 to confirm that this is not true and assert bj > ∑di
For this to be more effective in case 2, b + (∑di - dj)L + e < b + e
=> ∑di - dj < 0
This cannot be true since ∑di = ∑di¬j + dj (all camper-induced recklessness from drivers except driver j plus the recklessness of driver j)
which would mean ∑di¬j + dj < dj
=> ∑di¬j > 0 and which cannot make sense since we assume d > 0 (no negative recklessness among reckless drivers)
Which means the ONLY way pulling over a driver for a reckless act like speeding or tailgating is more effective than pulling over a left lane camper in any driving scene is if the recklessness of that one driver is of greater severity than the combined recklessness of all drivers engaging in reckless behavior as a result of the left lane camper; the empirical evidence is so strong (drive from St. Petersburg to Orlando on I4 before attempting to disprove) that we can prove this scenario to be outside the sample space of all outcomes in the driving scene. We can therefore prove they enforcing left lane camping is more effective than enforcing other reckless acts like speeding,
Q.E.D.
I frequently drive from St. Petersburg to Orlando on I4 and run into an average of 5-6 traffic blockages caused by a left lane camper. I witness the resulting reckless behavior this causes other drivers to engage in. While the proof is theoretical, it is strongly backed by empiricism from myself and countless others.
I am calling on the Florida DOT to push for tougher penalties for left land camping, and for Florida highway patrol to drastically strengthen enforcement on this careless, reckless and dangerous activity.
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Petition created on October 21, 2023