Throughput is directly proportional to the volume of cars, and SUVs have larger volume. Technically perhaps surface area, but there is a psychological effect to height. I believe people also give taller vehicles more space as a rule.
Throughput in congestion is determined mostly by how quickly drivers react to the opportunity to move and how many points of attrition are in a path. Both of what are impacted by the number of cars and how well they break or accelerate, not by their size.
There's space to claim large car cause attrition, but that's completely dependent of the local properties of the streets.
The footprint of the car matters. When cars get 5% longer, the same number of people in cars takes 5% more roadway, which adds up quickly, because the difference between smoothly-flowing traffic and jammed traffic is a fragile equilibrium dominated by breakpoints. Furthermore, heavier cars accelerate and decelerate slower than lighter cars, which has a compounding effect on decreasing overall throughput.
No, the length you need between cars is variable and depends on the speed of traffic and the time it takes for a car to come to a stop. The longer a car is, the heavier it is (frames do not have negative weight), and the heavier it is, the longer the stopping distance is. Please don't bother commenting further on something you're so belligerently clueless about.
I would largely agree with that assessment, yeah. Dangerous place to bike, too. I've even seen pedestrians get clobbered by bikers because they stepped into the bike lane not realizing a bike was barreling toward them at 20mph+. This is part of why Waymo and Uber warn you when the dropoff is next to a bike lane.
As somebody noticed, we're very lucky to live in a universe where many key things are utterly simple (like basic arithemetics), or allow for acceptable simple approximations (like classical mechanics). There was a sci-fi story set in a world orbiting a binary star, where discovering the laws of celestial mechanics feels like an intractable problem, and even predicting seasons is barely possible.
The need / demand for some verification system might be growing though as I’ve heard fraudulent job application (people applying for jobs using fake identities… for whatever reason) is a growing trend.
Many times I've looked at the output of a regression model, seen this effect, and then thought my model must be very bad. But then remember the points made elsewhere in thread.
One way to visually check that the fit line has the right slope is to (1) pick some x value, and then (2) ensure that the noise on top of the fit is roughly balanced on either side. I.e., that the result does look like y = prediction(x) + epsilon, with epsilon some symmetric noise.
One other point is that if you try to simulate some data as, say
y = 1.5 * x + random noise
then do a least squares fit, you will recover the 1.5 slope, and still it may look visually off to you.
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