Even more of a reason for putting more money into passenger rail.

Check out youtuber City Nerd and his top 10 city pairs for High Speed Rail. Only the NEC city pairs scored higher than 20, Dallas to Houston and Miami to Orlando scored around 10, just about everything else scored less than 2.
He used a gravity model based on both city populations, divided by the distance between them in miles squared, with an adjustment based on time (closer to 2.5 hours heavily favored. His reasoning is, less than that driving gains, more than that airplanes gain market share. Why build a HSR network that can not fill all the trains it needs to run to compete with planes and cars? He explains it all very well.
Agree or disagree with him or not, he is using population as a measure to rank these c9ity pairs. If a train can not make the trip in less than 3 hours, that train should end.
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I've never watched any of the City Nerd's videos. Is this the video you were talking about? https://www.youtube.com/watch?v=pwgZfZxzuQU

I fully agree with his opinion regarding the necessity of a high-speed rail with bicycle accessibility from DC to Pittsburgh. Traveling by Amtrak took me 8 freakin hours to reach these two cities, whereas driving only took 4 hours!

Population density, economic activity, and current transportation networks are some of the criteria that are included in CityNerd's research. These city pairings are particularly attractive as potential destinations for future high-speed rail lines, even if HSR is still a contentious issue in the United States.

recondite wrote: ↑Wed Dec 13, 2023 2:02 am Population density, economic activity, and current transportation networks are some of the criteria that are included in CityNerd's research. These city pairings are particularly attractive as potential destinations for future high-speed rail lines, even if HSR is still a contentious issue in the United States.

And I might add, New York City to Pittsburg did not make City Nerd's top 10 list.

That response felt a bit like an AI bot. Oddly explicit for the first post to the forums.

eolesen wrote: ↑Thu Aug 03, 2023 4:33 pm Summer temps and payload restrictions are really only an issue at places like Albuquerque, Las Vegas, Tucson and Phoenix.

Perhaps those would be good places to have platform screen doors even at ground level and elevated stations, like in Dubai where summer temperatures are huge issue.

I like what CityNerd did, though I was disappointed at using some a priori sort of model. Why the square of the distance? For travel between places 1 and 2, he uses

Ridership = (constant) * (population 1) * (population 2) / (distance 1 - 2)^2

There is some justification for using the product of populations, because the source would contribute travelers and the destination would contribute places for those travelers to go to. But an inverse square law? For gravity and electromagnetism, that is a consequence of the geometry of space-time, as it is for radiation intensity. So one should not expect it to apply here.

I remember when I found a big pile of trip date for domestic US airlines. The data had a LOT of scatter, but it seems less for the sum of the origin's and destination's populations instead of their product. It also was nearly flat as a function of distance.

I'm unable to find any comparable data for Amtrak, though I've found that sort of data for some urban-rail systems.

The most I can find is how many people depart from each station. For Boston - DC, I found a close to linear fit for each station's nearby population, using its metropolitan statistical areas as its nearby population. That approximately fits population product with a nearly flat distance effect.

lpetrich wrote: ↑Sat Aug 10, 2024 2:44 am I like what CityNerd did, though I was disappointed at using some a priori sort of model. Why the square of the distance? For travel between places 1 and 2, he uses

Ridership = (constant) * (population 1) * (population 2) / (distance 1 - 2)^2

I thought he explains why well. I certainly can not answer that question for him.
He stated he was using a gravity model.
F = GMm/R²
The formula to calculate the gravitational force is F = GMm/R², where F is the force in newtons, G is the gravitational constant (6.674*10 −11 N-m 2 /kg 2 ), M and m are the masses of the two objects in kilograms, and R is the distance between them in meters

The gravitational force between two objects is proportional to the product of their masses and inversely proportional to the square of the distance between their centers of mass.
It appears Nerd swapped population for mass into that gravity formula and invented his own constant from the time chart used in his explanation.

I think it is important is that he is not predicting riderhip as much as he is ranking the various city pairs. When just about all the city pairs along the NEC are ranked with a value over 50, and none of the others nationally have a value over 10, with most below 5, I think it emphasizes why the NEC has almost 50% of all of Amtrak's ridership every year.

What would be interesting is to see how his gravity model works in Asia and Europe. I am not going to test his model, but I am sure others can.

My position is that a gravity model may be a poor fit, and one may want to look for other distance functions. Furthermore, the inverse square law of gravity and similar effects is only for 3 space dimensions. For different numbers:

1. constant
2. ~ 1/distance
3. ~ 1/distance^2
4. ~ 1/distance^3
5. ...