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Most of those systems tax property increase when infrastructure is built and operate in dense cities with extremely anti-car regulation that gives them a guaran

Most of those systems tax property increase when infrastructure is built and operate in dense cities with extremely anti-car regulation that gives them a guaranteed consumer base. Do the same in the US and it will magically become profitable, because there will be no alternative.

renewiltord 3y ago
Sure. There are policies you can change. But if you believe these things shouldn't make a profit you won't put those things into place. It looks like anyone who wants transit to not make a profit just wants it to suck.
- sudosysgen 3y ago
  Levying taxes directly to the transit agency and punishing car usage doesn't incentivize the transit agency to make the transit not suck, it's just a different way for accounting in public funding.
  - renewiltord 3y ago
    I'm sure you have a complex model for this, but transit agencies that aim to make losses all suck and the ones that aim to make profits are good. So if someone is telling me the agency should make a loss, they want it to suck. The correlation is strong and whatever your model is, it isn't capturing some variable.
    - sudosysgen 3y ago
      I don't have a complex model, it's just your test that is capturing the wrong variable. Every network you listed is "profitable" because the subsidies they get are in the "income" column instead of the "subsidy" column. The distinction between profitability or making a loss is purely nominative. In the US for example, the HK transit authority would be "aiming to make a loss" because its largest source of income are the taxes it is allowed to levy. Since in the US it's the government that levies the taxes which are counted as subsidies, the HK transit authority in the US would be classified as loss-making. Additionally the MTR in HK has pseudo-governmental powers in that it gets to expropriate, rezone, and resell land at a higher price, which would also be counted as a subsidy in the US and therefore it would be considered to be loss making. So the problem isn't that my model isn't capturing a variable, it's that your test isn't capturing a variable and is taking in erroneous data points due to diverging definitions of your metric.

renewiltord 3y ago

Sure. There are policies you can change. But if you believe these things shouldn't make a profit you won't put those things into place. It looks like anyone who wants transit to not make a profit just wants it to suck.

sudosysgen 3y ago
Levying taxes directly to the transit agency and punishing car usage doesn't incentivize the transit agency to make the transit not suck, it's just a different way for accounting in public funding.
- renewiltord 3y ago
  I'm sure you have a complex model for this, but transit agencies that aim to make losses all suck and the ones that aim to make profits are good. So if someone is telling me the agency should make a loss, they want it to suck. The correlation is strong and whatever your model is, it isn't capturing some variable.
  - sudosysgen 3y ago
    I don't have a complex model, it's just your test that is capturing the wrong variable. Every network you listed is "profitable" because the subsidies they get are in the "income" column instead of the "subsidy" column. The distinction between profitability or making a loss is purely nominative. In the US for example, the HK transit authority would be "aiming to make a loss" because its largest source of income are the taxes it is allowed to levy. Since in the US it's the government that levies the taxes which are counted as subsidies, the HK transit authority in the US would be classified as loss-making. Additionally the MTR in HK has pseudo-governmental powers in that it gets to expropriate, rezone, and resell land at a higher price, which would also be counted as a subsidy in the US and therefore it would be considered to be loss making. So the problem isn't that my model isn't capturing a variable, it's that your test isn't capturing a variable and is taking in erroneous data points due to diverging definitions of your metric.