I'm currently investigating the feasibility of a small research project that would compare historical train performance statistics for a single or small group of stations against another non-rail dataset. The data I have in mind are the distributions of services that are on-time/delayed x-mins/cancelled for a given station over a long time period, a bit similar to the information displayed in the performance graphics on OnTimeTrains but without the limitation where the data only appears to go back 12 weeks. I would ideally like to look at longer timescales (the longer the better), which could be focused on a single/small group of stations rather than the entire network. To clarify however am not interested in aggregated TOC PPM statistics.
I feel like this is data that would likely be available in the various open data repositories/portals/marketplaces, however I don't exactly know where to start looking, or even if sufficient information is available to make the project worthwhile. I am hopeful however because looking at recenttraintimes.co.uk I appear to be able to query arrival/departure times from farther back in time (March this year). With this in mind, I would be appreciative of suggestions for which datasets may be used and how far back in time they go. It may be I proceed no further if data isn't suitable, but I would rather rule it out if that's the case. Thanks in advance.
I feel like this is data that would likely be available in the various open data repositories/portals/marketplaces, however I don't exactly know where to start looking, or even if sufficient information is available to make the project worthwhile. I am hopeful however because looking at recenttraintimes.co.uk I appear to be able to query arrival/departure times from farther back in time (March this year). With this in mind, I would be appreciative of suggestions for which datasets may be used and how far back in time they go. It may be I proceed no further if data isn't suitable, but I would rather rule it out if that's the case. Thanks in advance.