And here it is.
I’m pretty proud to say that beside searching for the elusive schedule padding, and possibly finding some, I managed fit in a comment about the inevitability of death, a quote from Jerry Seinfeld, and a self-deprecating jab at the idea of human rights.
Also, I put videos in a PDF. Who the hell knew that was possible?
I’m being urged to get my act together regarding my masters thesis. I have a set of datasets I know I want to explore but I need to find a question of sorts that I can quite thoroughly answer with them. I also need to decide what type of person would be good to oversee this project — the ‘committee’ and whatnot. As I so often do, I’ll use you anonymous readers as the spur to set my thoughts to bytes and thereby make rigorous my abstractions.
SO: My dataset is real-time transit data feeds. I don’t care what buses are doing right now unless I’m waiting for them — I care what patterns they’re scratching into our lives. I’ve already demonstrated a Python script that will make random requests from a real-time API and store the results. There exist comparable API’s from other agencies that this script can easily be adapted to. As many agencies as have APIs I could squirrel data from. That’s the dataset or set thereof.
My question has been more difficult to discover. I have so many! Here are a few:
- What is the distribution of delay? How does it vary? Spatially, temporally?
- What kinds of lines/agencies/times have non-random, systematic delay?
- How does the delay spread of ‘good’ transit systems compare to that of ‘bad’ transit systems and what might explain this?
- Good scheduling should minimize systematic delay: what sort of delay remains after that and what might riders learn from it? How should they learn to best accommodate this delay?
- What is the space-time trajectory of a vehicle in various states of delay?
- How different is the delay of lines that don’t mix with traffic?
- What relation does frequency have to delay? At what service frequency can we say quantitatively that schedules should be abandoned and headways maintained instead?
- What is the accuracy of arrival time predictions? What margin of error exists around predictions at various space-time distances?
I suppose the first question is probably my best shot. Though #5 is certainly intriguing. Now on to the lit review I suppose? *deep breath*
And then the committee! Beside my adviser, who is a regular transit user and quantitative geographer, I want another statistician/data-person, and this shouldn’t be too hard to find. I also want someone really good at graphic communication. For that latter, I want someone from DAAP. But I want to be sure that they don’t think or feel or act as though I’ve invited them to proof my presentation while others address it’s content; content is inseparable from presentation. Form does not follow function; rather both form and function must mirror each other. If I fail to make that happen, I will have miscommunicated or misunderstood my project.
Oh dear readers, what would you want to know if you knew, as I may, where all the buses are all the time?
Your illustrious blogger has been accepted into the masters geography program at UC!
Fall semester starts in exactly one month… jeepers. Plans can change so quickly, can’t they?
This is actually very good news for this website as I’ll finally be giving myself explicit permission to spend time diving deep into a lot of the geeky spatial, transportation analysis and writing I’ve been wanting to do for ages. I’ll also get a degree of financial security as a part-time TA that I don’t currently enjoy as a freelancer alone. Good things for the future all of my pet projects!
I think y’all should be able to look forward to some increasingly rigorous, informed, and artful work here over the next couple of years as I formally hone my skills.