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Defining Transit Service Areas with Unsupervised Machine Learning

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Re: Defining Transit Service Areas with Unsupervised Machine Learning

#11

Post by [deleted] »

n4r9 wrote: post #5
pjc50 wrote: post #3

Isn't this in fact k-means clustering rather than "machine learning"?

Looks like he used a hierarchical clustering (probably single-linkage) rather than K-means. But yes, I'm also not too keen on using a clustering algorithm and calling it machine learning.

[deleted]

Re: Defining Transit Service Areas with Unsupervised Machine Learning

#12

Post by thanatropism »

steve_gh wrote: post #2

Interesting application of clustering. My group in the UK (Amey Strategic Consulting: https://www.amey.co.uk/amey-consulting/services/strategic-co... ) have been doing very similar things for a while now to optimise utilities maintenance, for example optimizing the number and position of maintenance depots to enable a utilities company to undertake repairs most efficiently. I'm UK based, and not that familiar with th…

Are you aware of the classic four-stage traffic assignment model? It's the linear regression of transportation modeling. It's scary that the authors didn't even mention that it exists.

https://en.wikipedia.org/wiki/Transportation_forecasting#Fou...

My first job was to estimate trip generation models (the first tier) using simultaneous-equations time-series models in Eviews; a transportation engineer with some experience doing surveys (i.e. coordinating teams of people with clipboards) worked on the mode choice. We had additionally some greyheads who had the Matlab codes for trip distribution and route assignment.

Realistically some machine learning type classification could really help in mode choice, particularly if raw data (like turnstile pushes) is available. Trip generation, like most microeconometrics with time-series, is something of a dark art.

Ideally we would all be doing agent-based simulation by now with super-disaggregate data like Waze has, but I haven't seen complex systems simulation really "arrive" for real problems. It'd be fun to train reinforcement learners on them too :)

Re: Defining Transit Service Areas with Unsupervised Machine Learning

#13

Post by pjc50 »

chrisseaton wrote: post #9
pjc50 wrote: post #3

Isn't this in fact k-means clustering rather than "machine learning"?

Leading universities like Stanford teach k-means clustering in their "machine learning" courses. Why do you think they're wrong and it's not actually machine learning?

Well, where's the learning?

There's a general tendency to reclassify any kind of statistical analysis that produces a useful result as "machine learning", and I don't think it helps. For example there are lots of machine vision techniques; the neural net ones can sensibly be called "learning" but things like edge detection shouldn't be.

(also, just because something is taught in course X doesn't mean that it is X, it might be an embedded prerequisite)

Re: Defining Transit Service Areas with Unsupervised Machine Learning

#14

Post by jonathankoren »

This article was very underwhelming. It’s viz porn dressed up as something insightful. Looking at his clusters, he managed to “find” the transit systems. That’s not particularly interesting. It’s even less interesting because at the start he talks about how you can’t combine transit districts, and then he clusters to find the transit districts and then tries to say “look at all the transit districts I combined”, when in reality the ones he’s combining are tiny and inconsequential.

If he wanted to do something, he should demystify clipper card purchases. Every agency has its own bespoke fare system, and its complete bullshit if you transfer between systems. THAT would have been interesting, especially given how these things interact with agency funding.

Re: Defining Transit Service Areas with Unsupervised Machine Learning

#15

Post by tixocloud »

Apologies for playing devil's advocate but in areas where there are few riders, would it not be a reflection of poor transit service in the first place? I would assume if the service is so bad in those areas that ridership would be poor but you'd still need to check out whether the actual residents in those areas are using alternative methods of transportation as opposed to just commute data?
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