Most posts here are focusing on the (very long) history of revenue allocation, which has become the defining role of ORCATS. I do however want to emphasise that, at its birth, there were really three things that came together to make ORCATS possible/necessary.
First was a comprehensive set of data on ticket sales. At its birth the NPAAS data mentioned above by Dr Hoo was used. This data was pretty comprehensive, but far from perfect. Manual tickets, group tickets, rovers/rangers (to use today's parlance), and any number of tickets for which the origin and destination of the passenger could not be deduced made the data imperfect, but far better (because it was on computer tapes) than having to sample ticket sales from specific locations.
Second was the available computing power to do the calculations required. The processing needed to take all (significant) passenger flows that would be impacted by the service in question (e.g. Harrogate to Bath would be impacted by the introduction of CrossCountry HSTs) and calculate the overall timetable between those flows and how a change to the Leeds to Bristol service would impact the attractiveness of that timetable, and finally, aggregate all the revenue increases from those flows and 'allocate' it to the Leeds to Bristol service.
Thirdly, to understand how passengers reacted to changes in speed, frequency, number of changes in a services. Previously there were 'rules of thumb' that had been developed as 'distilling previous experience (especially the WCML electrification)', but it was the introduction of HSTs that gave the most significant changes at the time the data and computing power allowed systematic study of the passengers' reaction to the new service and understanding, primarily, the elasticity of demand to speed (journey time).
As I mentioned previously, the question of regular allocation of revenues was always an objective for ORCATS, but the initial focus of this part of the OR group's work was evaluating the various investment decisions in the Inter City sector, and (also a key focus) justifying that investment to the Dept of Transport and Govt.. As time went on (and I left the railway in 1983) the objective of routinely allocating revenues seemed more and more possible and so became a bigger and bigger focus.
Finally, this post gives some good context on the history of the revenue allocation process, including some great historic photos:
And where and what is this? It is in Seymour Street in central London; or rather it was. fbb has driven along Seymour Street c/o Google ...
publictransportexperience.blogspot.com
NEW POST BELOW - WRITTEN SEPARATELY, BUT AUTOMATICALLY MERGED BY THE FORUM SOFTWARE
This post is just to emphasise how rudimentary the understanding of passenger behaviour was at the birth of ORCATS.
The model used was simply to assume that passengers had an ideal departure (or arrival time) for a journey. They then 'evaluated' the timetable on the basis of the journey time, to which they added a fixed 'penalty' for each change required. They then chose the service 'nearest' to their ideal departure time. That was pretty much it. It sounds, and was, rudimentary.
We, of course, understood that there was an expensive list of shortcomings, but to estimate/calibrate the various possible parameters would have required data from far more significant changes in services than actually occurred in those early years of the work on passenger demand. Obviously the introduction of HSTs involved a significant improvement in speed, frequency and on-train experience. It was therefore difficult to separate the impact of those factors. I recall, for example studying closely a doubling of services stopping at Dewsbury, precisely because no change in rolling stock, fares or journey time took place at the same time and so it was a rare opportunity to study the impact or a 'pure' change in frequency in isolation.
I am sure, over the forty of so years since, the understanding of passenger demand has improved immeasurably.
Finally, just to mention that work had also been done on a 'cross sectional' approach to the question of passenger demand. The work described above was essentially time-series analysis; it looked at how demand changed in the weeks/months following a 'step change' in service. The alternative was a more fundamental approach of asking, based on the data on rail travel between every city in the country, how much demand would be expected between two towns/cities with populations P1 and P2, a distance D apart and with an average rail journey time of T1 as compared to a road journey time of T2. The results of that analysis had such enormous 'margins of error' as to be essentially useless for any 'real world'applications.