At times, I find it easiest to express my momentary sentiment with a pithy pop culture reference. I don't think I've even seen "Don't Tell Mom the Babysitter's Dead" in its entirety, but this particular scene comes to mind right now (where the guys in question were asked by the babysitter to do the dishes):
And by "dishes" in this case I actually mean GRAD SCHOOL. When my thesis was signed yesterday, I became, for all intents and purposes, a Master of Science in Transportation and in Operations Research. I don't feel any different; do you?
As for the content of the thesis, entitled "Automatic Data for Applied Railway Management: Passenger Demand, Service Quality Measurement, and Tactical Planning on the London Overground Network," it's posted here for anyone to see. I recommend it only to (a) transit planners and researchers, and (b) people who need help sleeping.
The gist of the thing, really the gist of my entire graduate education, is that there are a million ways that transit planners and managers should take advantage of the glut of automatic data this is becoming ever more available. As per the abstract:
This thesis develops and tests methods to (i) estimate on-train loads from automatic measurements of train payload weight, (ii) estimate origin-destination matrices by combining multiple types of automatic data, (iii) study passenger incidence (station arrival) behavior relative to the published timetable, (iv) characterize service quality in terms of the difference between automatically measured passenger journey times and journey times implied by the published timetable. It does so using (i) disaggregate journey records from an entry- and exit-controlled automatic fare collection system, (ii) payload weight measurements from ``loadweigh'' sensors in train suspension systems, and (iii) aggregate passenger volumes from electronic station gatelines. The methods developed to analyze passenger incidence behavior and service quality using these data sources include new methodologies that facilitate such analysis under a wide variety of service conditions and passenger behaviors.
The above methods and data are used to characterize passenger demand and service quality on the rapidly growing, largely circumferential London Overground network in London, England. A case study documents how a tactical planning intervention on the Overground network was influenced by the application of these methods, and evaluates the outcomes of this intervention. The proposed analytical methods are judged to be successful in that they estimate the desired quantities with sufficient accuracy and are found to make a positive contribution to the Overground's tactical planning process.
One aspect of the analyses in the research was to "assign" passengers to individual scheduled services depending on when and where they entered the system and where they were going (all recorded by the Oyster smartcard ticketing system). Something I really enjoyed was implementing this using the open source trip-planning software Graphserver. I just:
- converted the Overground's schedules to GTFS (with a little hacked up Perl script)
- fed the GTFS schedules into Graphserver
- queried Graphserver to "plan" a trip for each of the Oyster journey data records (with another little hacked up Perl script)
The result was full information about the least-travel time scheduled path for that journey. In other words, Graphserver did all the algorithmic heavy lifting typically associated with "schedule-based assignment" and all I had to do was a little work around the edges. Bam! This was just one small part of the thesis, but it was great to be able to leverage the open source and open standards-based transit-related tools being developed these days. Naturally, all the statistical, numerical, and graphical analysis was done with R
With that, and after a brief upcoming vacation to a land of cheese, wine, and (edible) Oysters, the page turns. As of July 6, I will be taking what I have learned in the past decade as a hacker (in the most benevolent sense of the word) for the finance, internet, and transit industries and putting it to work for my new employer, the MTA, on some strategic internally and externally facing technology projects. Watch out NYC.