11–20 of 98 posts
Would not be easier to actually time the actual waiting times as he waited for the bus every day?
Would not be easier to actually time the actual waiting times as he waited for the bus every day?
Not whether other people with different commutes would experience the same, or if its true throughout the day.
Plus, it would incur a sampling bias based on the authors particular commute schedule.
I've never really understood any example involving a poisson process. They always seem to involve bus arrivals or light bulbs burning out, and I can't understand why the memory less property would ever make any sense for these.
Even if the bus system was poorly run, why would it make sense to assume that the expected value of time to arrival doesn't change based on how long you've been waiting?
What is an actual phenomenon that is well modeled by a poisson process?
> a Poisson process is a memoryless process that assumes the probability of an arrival is entirely independent of the time since the previous arrival. In reality, a well-run bus system will have schedules deliberately structured to avoid this kind of behavior: buses don't begin their routes at random times throughout the day, but rather begin their routes on a schedule chosen to best serve the transit-riding public.…
This is very ambiguous. Unless he gives a time frame the numbers do not make sense. Average in a week? Average in a year? This is not how it works in real life.
And I cannot accept his premise. My experience tells me that, in New York, when I used to take a bus to work, sometimes the bus was coming as I was walking to the stop; sometimes I would wait a long time. Sometimes not very long. There was no observable bias.
Any recommendations?
> When waiting for a bus that comes on average every 10 minutes, your average waiting time will be 10 minutes. This is very ambiguous. Unless he gives a time frame the numbers do not make sense. Average in a week? Average in a year? This is not how it works in real life. And I cannot accept his premise. My experience tells me that, in New York, when I used to take a bus to work, sometimes the bus was coming as I was…
> a Poisson process is a memoryless process that assumes the probability of an arrival is entirely independent of the time since the previous arrival. In reality, a well-run bus system will have schedules deliberately structured to avoid this kind of behavior: buses don't begin their routes at random times throughout the day, but rather begin their routes on a schedule chosen to best serve the transit-riding public.…
Nice article. It reminds me of my year living in London, and taking the bus everyday to Imperial College from West End Lane in West Hampstead. There was a stop on both sides of the road - one for the outbound bus, and one for the inbound (the bus went from central London to a terminus and then returned mostly on the same route). Now we did not use schedules - way too inaccurate at rush hour, and the busses there were…
Imagine if (in the future) some item like a phone can detect this information around you, and automatically record it. Forming games ontop of this life data would be weird, neat, fun and sad all at the same time. Imagine seeing a real example of where someone else is just more lucky than you are in stupid but impactful (on your morale) ways.
If it didn't seem so tedious to track, I'd love to implement an app to record this info. Unfortunately no one I know would care, and I'm sure I'd get too lazy to keep it accurate. Neat nonetheless, thanks for the cool thoughts :)
> When waiting for a bus that comes on average every 10 minutes, your average waiting time will be 10 minutes. This is very ambiguous. Unless he gives a time frame the numbers do not make sense. Average in a week? Average in a year? This is not how it works in real life. And I cannot accept his premise. My experience tells me that, in New York, when I used to take a bus to work, sometimes the bus was coming as I was…
In statistics, "average" often means "expected value". No time frame is specified (although you could consider it an infinite time frame). With a small sample size your actual average might not be 10 minutes, but as your sample size grows, it will tend toward 10 minutes.
If you are talking about spherical-cow style poisson buses, yeah (that's what the author means by "reasonable assumptions). But as the author concludes, bus arrival times are not well modeled by a poisson process.